GAMMs analyses Trombone - Tongan vs English (NZE) -> paper plots
Jalal Al-Tamimi (Newcastle University)
Donald Derrick (University of Canterbury)
Matthias Heyne (Boston University)
06 May 2019
This notebook provides the plots for the article: Heyne, M., Derrick, D., and Al-Tamimi, J. (under review). “Native language influence on brass instrument performance: An application of generalized additive mixed models (GAMMs) to midsagittal ultrasound images of the tongue”. Frontiers Research Topic: Models and Theories of Speech Production. Ed. Adamantios Gafos & Pascal van Lieshout.
1 Loading packages
load_packages = c("readr","knitr","ggplot2","mgcv","itsadug","parallel","dplyr","rlist","plotly")
# dplyr, rlist, and plotly are required by the custom plotting functions
for(pkg in load_packages){
eval(bquote(library(.(pkg))))
if (paste0("package:", pkg) %in% search()){
cat(paste0("Successfully loaded the ", pkg, " package.\n"))
}else{
install.packages(pkg)
eval(bquote(library(.(pkg))))
if (paste0("package:", pkg) %in% search()){
cat(paste0("Successfully loaded the ", pkg, " package.\n"))
}
}
}Successfully loaded the readr package.
Successfully loaded the knitr package.
Successfully loaded the ggplot2 package.
Successfully loaded the mgcv package.
Successfully loaded the itsadug package.
Successfully loaded the parallel package.
Successfully loaded the dplyr package.
Successfully loaded the rlist package.
Successfully loaded the plotly package.rm(load_packages, pkg)
Sys.setenv('MAPBOX_TOKEN' = 'sk.eyJ1IjoiZGRlcnJpY2siLCJhIjoiY2p2MW1ndnNoMXczYTRkbXd6dzRuMTQ2aCJ9.UTai4wWcFFVaJAuOrAX7VQ')
# decide whether to save plots as PDF and jpg files using the Orca command line utility
save=FALSE
printPDF=FALSE1.1 Loading custom plotting functions
1.1.1 plotly_model_outputs function (Matthias Heyne, 2019)
# plot multiple GAM model outputs
plotly_model_outputs <- function(model, changing_cond, changing_var, constant_cond1, constant_var1, values, constant_cond2=NULL, constant_var2=NULL, print=TRUE){
if(length(constant_var1)>1 | length(constant_var2)>1){
print("Error: Constant variables can only have length 1.")
}else{
if(!is.null(constant_cond2) && !is.null(constant_var2) && length(changing_var)==2){
# works for models Notes.gam...
cond_p1 = capture.output(cat(paste0("list(", changing_cond, "='", changing_var[1], "', ", constant_cond1, "='", constant_var1, "', ", constant_cond2, "='", constant_var2, "')")))
p1=plot_smooth(x=get(model), view=values, cond = eval(parse(text=cond_p1)))
cond_p2 = capture.output(cat(paste0("list(", changing_cond, "='", changing_var[2], "', ", constant_cond1, "='", constant_var1, "', ", constant_cond2, "='", constant_var2, "')")))
p2=plot_smooth(x=get(model), view=values, cond = eval(parse(text=cond_p2)))
# set Rho max to the max of the fit + half the difference between max of the fit and the upper limit
max_ul = max(p1$fv$ul, p2$fv$ul)
max_fit = max(p1$fv$fit, p2$fv$fit)
maximum=max_fit+((max_ul-max_fit)/2)
# plot in polar coordinates
p=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=changing_var[1]) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=changing_var[2]) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title=paste0("GAM smooths @", constant_var1, " & ", constant_var2),
legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
p
}else if(is.null(constant_cond2) && is.null(constant_var2) && length(changing_var)==2){
# no specific case yet
cond_p1 = capture.output(cat(paste0("list(", changing_cond, "='", changing_var[1], "', ", constant_cond1, "='", constant_var1, "')")))
p1=plot_smooth(x=get(model), view=values, cond = eval(parse(text=cond_p1)))
cond_p2 = capture.output(cat(paste0("list(", changing_cond, "='", changing_var[2], "', ", constant_cond1, "='", constant_var1, "')")))
p2=plot_smooth(x=get(model), view=values, cond = eval(parse(text=cond_p2)))
# set Rho max to the max of the fit + half the difference between max of the fit and the upper limit
max_ul = max(p1$fv$ul, p2$fv$ul)
max_fit = max(p1$fv$fit, p2$fv$fit)
maximum=max_fit+((max_ul-max_fit)/2)
# plot in polar coordinates
p=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=changing_var[1]) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=changing_var[2]) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title=paste0("GAM smooths @", constant_var1, " & ", constant_var2),
legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
p
}else if(is.null(constant_cond2) && is.null(constant_var2) && length(changing_var)>2){
# works for models NZE.gam... or Tongan.gam...
for (i in 1:length(changing_var)){
if(changing_cond == constant_cond1){
cond_p1 = capture.output(cat(paste0("list(", constant_cond1, "='", constant_var1, "')")))
p1=plot_smooth(x=get(model), view=values, cond = eval(parse(text=cond_p1)))
# exception for KIT (='\\\\') when not using IPA symbols
if (changing_var[i]!="\\\\"){
cond_p2 = capture.output(cat(paste0("list(", changing_cond, "='", changing_var[i], "')")))
p2=plot_smooth(x=get(model), view=values, cond = eval(parse(text=cond_p2)))
}else{
p2=plot_smooth(x=get(model), view=values, cond = list(token.ord='\\\\'))
}
# set Rho max to the max of the fit + half the difference between max of the fit and the upper limit
max_ul = max(p1$fv$ul, p2$fv$ul)
max_fit = max(p1$fv$fit, p2$fv$fit)
maximum=max_fit+((max_ul-max_fit)/2)
# plot in polar coordinates
p=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=constant_var1) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=changing_var[i]) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title=paste0("GAM smooths ",constant_var1," vs ", changing_var[i]),
legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
Sys.sleep(0)
print(p)
}
}
}
}
}1.1.2 plotly_sig_diff_notes function (Matthias Heyne, 2019) -> with updated legend position
# plot smooths with shading to indicate significant differences (Matthias Heyne, 2019)
plotly_smooths_w_sig_diff <- function(model, condition, var1, var2, values, language, fileName, print=TRUE, save=TRUE){
# specify path for saving plots
# plot_folder = "saved_plots"
# Sys.setenv(MAPBOX_TOKEN = 11122223333444)
# get intervals of significant differences by running plot_diff
# unfortunately setting plot=FALSE doesn't work as intervals of significant difference are not displayed!
# hardcoded condition
# output = capture.output(plot_diff(get(model), view=values,
# comp=list(tokenPooled.ord=c(var1, var2))))
# output = capture.output(plot_diff(get(model), view=values, comp=list(langNoteInt.ord=c(paste0("Tongan.", note, ".", intensity), paste0("NZE.", note, ".", intensity)))))
names_smooths=list()
if (condition=="tokenPooled.ord" && length(language)==1){
output_comp = capture.output(cat(paste0("list(", condition, "=c(var1, var2))")))
names_smooths[1]=var1
names_smooths[2]=var2
plot_title = paste0("GAM smooths ", language, " ", var1, " vs ", var2)
# plot_filename = paste0(language, "_", var1, "_vs_", var2, "_from_", model)
}else if (condition=="langNoteInt.ord" && length(language)==2){
output_comp = capture.output(cat(paste0("list(", condition, "=c('", language[1], ".", var1, ".", var2,
"', '", language[2], ".", var1, ".", var2, "'))")))
names_smooths[1]=paste0(language[1], ".", var1, ".", var2)
names_smooths[2]=paste0(language[2], ".", var1, ".", var2)
plot_title = paste0("GAM smooths ", language[1], ".", var1, ".", var2, " vs ", language[2], ".", var1, ".", var2)
# plot_filename = paste0(language[1], ".", var1, ".", var2, "_vs_", language[2], ".", var1, ".", var2, "_from_", model)
}else if (condition=="langNoteInt.ord" && length(language)==1 && length(var1)==2){
output_comp = capture.output(cat(paste0("list(", condition, "=c('", language, ".", var1[1], ".", var2,
"', '", language, ".", var1[2], ".", var2, "'))")))
names_smooths[1]=paste0(language, ".", var1[1], ".", var2)
names_smooths[2]=paste0(language, ".", var1[2], ".", var2)
plot_title = paste0("GAM smooths ", language, ".", var1[1], ".", var2, " vs ", language, ".", var1[2], ".", var2)
# plot_filename = paste0(language, ".", var1[1], ".", var2, "_vs_", language, ".", var1[2], ".", var2, "_from_", model)
}
# output_comp = capture.output(cat(paste0("list(", condition, "=c(var1, var2))")))
output = capture.output(plot_diff(get(model), view=values, comp=eval(parse(text=output_comp))))
# no significant difference
if ((length(language)==1 && length(var1)==1 && length(output)==7) | (length(language)==2 && length(output)==6) |
(length(language)==1 && length(var1)==2 && length(output)==6)){
if (length(var1)==1){
cat(paste0("Smooths for ", var1, " & ", var2, " are not significantly different.\n"))
dat1 = NA
assign(paste0("int_sig_diff_", var1, "_", var2), dat1, envir = .GlobalEnv)
}else{
cat(paste0("Smooths for ", var1[1], " & ", var1[2], " in ", language, " are not significantly different.\n"))
dat1 = NA
assign(paste0("int_sig_diff_", var1[1], "_", var1[2], "_", var2, "_", language), dat1, envir = .GlobalEnv)
}
rm(dat1, output)
# run plot_smooth to grab data for polar plots
if (condition=="tokenPooled.ord" && length(language)==1){
cond_p1 = capture.output(cat(paste0("list(", condition, "=var1)")))
cond_p2 = capture.output(cat(paste0("list(", condition, "=var2)")))
}else if (condition=="langNoteInt.ord" && length(language)==2){
cond_p1 = capture.output(cat(paste0("list(", condition, "='", language[1], ".", var1, ".", var2,
"', ", condition, "='", language[2], ".", var1, ".", var2, "')")))
cond_p2 = capture.output(cat(paste0("list(", condition, "='", language[2], ".", var1, ".", var2,
"', ", condition, "='", language[1], ".", var1, ".", var2, "')")))
}else if (condition=="langNoteInt.ord" && length(language)==1 && length(var1)==2){
cond_p1 = capture.output(cat(paste0("list(", condition, "='", language, ".", var1[1], ".", var2,
"', ", condition, "='", language, ".", var1[2], ".", var2, "')")))
cond_p2 = capture.output(cat(paste0("list(", condition, "='", language, ".", var1[2], ".", var2,
"', ", condition, "='", language, ".", var1[1], ".", var2, "')")))
}
p1 = plot_smooth(x=get(model), view=values, cond=eval(parse(text=cond_p1)), rm.ranef=TRUE)
p2 = plot_smooth(x=get(model), view=values, cond=eval(parse(text=cond_p2)), rm.ranef=TRUE)
# # old - hardcoded
# p1 = plot_smooth(x=get(model), view=values, cond=list(tokenPooled.ord=var1, tokenPooled.ord=var2))
# p2 = plot_smooth(x=get(model), view=values, cond=list(tokenPooled.ord=var2, tokenPooled.ord=var1))
# set Rho max to the max of the fit + half the difference between max of the fit and the upper limit
maximum=max(p1$fv$fit, p2$fv$fit)+((max(p1$fv$ul, p2$fv$ul)-max(p1$fv$fit, p2$fv$fit))/2)
# plot in polar coordinates
p=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=names_smooths[1]) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=names_smooths[2]) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title=paste0("GAM smooths ", language, " ", var1, " vs ", var2),
legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
Sys.sleep(0)
print(p)
# there are differences...
}else{
# grab intervals of significant differences from output
if (length(language)==1 && length(var1)==1 && length(output)>=8){
# if (length(language)==1 && length(output)>=8){
sig_diff1 = c(as.double(unlist(strsplit(unlist(strsplit(output[8], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[8], " "))[3]))
}else if ((length(language)==2 && length(output)>=7) | (length(language)==1 && length(var1)==2 && length(output)>=7)){
# }else if (length(language)==2 && length(output)>=7){
sig_diff1 = c(as.double(unlist(strsplit(unlist(strsplit(output[7], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[7], " "))[3]))
}
if (length(language)==1 && length(var1)==1 && length(output)>=9){
# if (length(language)==1 && length(output)>=9){
sig_diff2 = c(as.double(unlist(strsplit(unlist(strsplit(output[9], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[9], " "))[3]))
}else if ((length(language)==2 && length(output)>=8) | (length(language)==1 && length(var1)==2 && length(output)>=8)){
# }else if (length(language)==2 && length(output)>=8){
sig_diff2 = c(as.double(unlist(strsplit(unlist(strsplit(output[8], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[8], " "))[3]))
}
if (length(language)==1 && length(var1)==1 && length(output)>=10){
# if (length(language)==1 && length(output)>=10){
sig_diff3 = c(as.double(unlist(strsplit(unlist(strsplit(output[10], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[10], " "))[3]))
}else if ((length(language)==2 && length(output)>=9) | (length(language)==1 && length(var1)==2 && length(output)>=9)){
# }else if (length(language)==2 && length(output)>=9){
sig_diff3 = c(as.double(unlist(strsplit(unlist(strsplit(output[9], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[9], " "))[3]))
}
if (length(language)==1 && length(var1)==1 && length(output)>=11){
# if (length(language)==1 && length(output)>=11){
sig_diff4 = c(as.double(unlist(strsplit(unlist(strsplit(output[11], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[11], " "))[3]))
}else if ((length(language)==2 && length(output)>=10) | (length(language)==1 && length(var1)==2 && length(output)>=10)){
# }else if (length(language)==2 && length(output)>=10){
sig_diff4 = c(as.double(unlist(strsplit(unlist(strsplit(output[10], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[10], " "))[3]))
}
# write intervals of significant difference to variable
if ((length(language)==1 && length(var1)==1 && length(output)>=11) | ((length(language)==2 && length(output)>=10)) |
(length(language)==1 && length(var1)==2 && length(output)>=10)){
# if ((length(language)==1 && length(output)>=11) | (length(language)==2 && length(output)>=10)){
dat1 = c(sig_diff1, sig_diff2, sig_diff3, sig_diff4)
}else if ((length(language)==1 && length(var1)==1 && length(output)>=10) | ((length(language)==2 && length(output)>=9)) |
(length(language)==1 && length(var1)==2 && length(output)>=9)){
# }else if ((length(language)==1 && length(output)>=10) | (length(language)==2 && length(output)>=9)){
dat1 = c(sig_diff1, sig_diff2, sig_diff3)
}else if ((length(language)==1 && length(var1)==1 && length(output)>=9) | ((length(language)==2 && length(output)>=8)) |
(length(language)==1 && length(var1)==2 && length(output)>=8)){
# }else if ((length(language)==1 && length(output)>=9) | (length(language)==2 && length(output)>=8)){
dat1 = c(sig_diff1, sig_diff2)
}else{
dat1 = sig_diff1
}
# old
# assign(paste0("int_sig_diff_", var1, "_", var2), dat1, envir = .GlobalEnv)
if (length(var1)==1){
assign(paste0("int_sig_diff_", var1, "_", var2), dat1, envir = .GlobalEnv)
}else{
assign(paste0("int_sig_diff_", var1[1], "_", var1[2], "_", var2, "_", language), dat1, envir = .GlobalEnv)
}
rm(dat1, output)
# run plot_smooth to grab data for polar plots
if (condition=="tokenPooled.ord" && length(language)==1){
cond_p1 = capture.output(cat(paste0("list(", condition, "=var1)")))
cond_p2 = capture.output(cat(paste0("list(", condition, "=var2)")))
}else if (condition=="langNoteInt.ord" && length(language)==2){
cond_p1 = capture.output(cat(paste0("list(", condition, "='", language[1], ".", var1, ".", var2,
"', ", condition, "='", language[2], ".", var1, ".", var2, "')")))
cond_p2 = capture.output(cat(paste0("list(", condition, "='", language[2], ".", var1, ".", var2,
"', ", condition, "='", language[1], ".", var1, ".", var2, "')")))
}else if (condition=="langNoteInt.ord" && length(language)==1 && length(var1)==2){
cond_p1 = capture.output(cat(paste0("list(", condition, "='", language, ".", var1[1], ".", var2,
"', ", condition, "='", language, ".", var1[2], ".", var2, "')")))
cond_p2 = capture.output(cat(paste0("list(", condition, "='", language, ".", var1[2], ".", var2,
"', ", condition, "='", language, ".", var1[1], ".", var2, "')")))
}
p1 = plot_smooth(x=get(model), view=values, cond=eval(parse(text=cond_p1)), rm.ranef=TRUE)
p2 = plot_smooth(x=get(model), view=values, cond=eval(parse(text=cond_p2)), rm.ranef=TRUE)
# set Rho max to the max of the fit + half the difference between max of the fit and the upper limit
maximum=max(p1$fv$fit, p2$fv$fit)+((max(p1$fv$ul, p2$fv$ul)-max(p1$fv$fit, p2$fv$fit))/2)
# plot in polar coordinates
if (exists("sig_diff4")){
p=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=names_smooths[1]) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=names_smooths[2]) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=seq(sig_diff1[1]*180/pi, sig_diff1[2]*180/pi, length.out=20),
r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
add_trace(theta=seq(sig_diff2[1]*180/pi, sig_diff2[2]*180/pi, length.out=20),
r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
add_trace(theta=seq(sig_diff3[1]*180/pi, sig_diff3[2]*180/pi, length.out=20),
r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
add_trace(theta=seq(sig_diff4[1]*180/pi, sig_diff4[2]*180/pi, length.out=20),
r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title=plot_title, legend=list(orientation="h", xanchor="center", x=0.5, y=0.012))
Sys.sleep(0)
print(p)
}else if (exists("sig_diff3")){
p=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=names_smooths[1]) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=names_smooths[2]) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=seq(sig_diff1[1]*180/pi, sig_diff1[2]*180/pi, length.out=20),
r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
add_trace(theta=seq(sig_diff2[1]*180/pi, sig_diff2[2]*180/pi, length.out=20),
r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
add_trace(theta=seq(sig_diff3[1]*180/pi, sig_diff3[2]*180/pi, length.out=20),
r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title=plot_title, legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
Sys.sleep(0)
print(p)
}else if (exists("sig_diff2")){
p=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=names_smooths[1]) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=names_smooths[2]) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=seq(sig_diff1[1]*180/pi, sig_diff1[2]*180/pi, length.out=20),
r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
add_trace(theta=seq(sig_diff2[1]*180/pi, sig_diff2[2]*180/pi, length.out=20),
r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title=plot_title, legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
Sys.sleep(0)
print(p)
}else{
p=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=names_smooths[1]) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=names_smooths[2]) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=seq(sig_diff1[1]*180/pi, sig_diff1[2]*180/pi, length.out=20),
r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title=plot_title, legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
Sys.sleep(0)
print(p)
}
}
# orca should work with plotly 4.9.0 after loading the orca module before stating R and RStudio
if(save==TRUE)
{
orca(p, paste(fileName,".pdf",sep=""),format="pdf")
orca(p, paste(fileName,".pdf",sep=""),format="jpeg")
}
}2 Load final models
# load final model for H1
Notes.gam.AR.Mod2 = readRDS("updated_models/Notes.gam.AR.Mod2.rds")
# load final model for H1b
VAR.gam.AR.Mod2 = readRDS("updated_models/VAR.gam.AR.Mod2.rds")
# load final models for H2
NZE.gam.AR.Mod2 = readRDS("updated_models/NZE.gam.AR.Mod2.rds")
Tongan.gam.AR.Mod2 = readRDS("updated_models/Tongan.gam.AR.Mod2.rds")3 Create paper plots
3.1 (new) Figure 4 H1 -> predict average values for note productions NZE vs Tongan
# create dfNotes
df <- read.csv("GAMM_Trombone_data.csv", sep=',', stringsAsFactors = F)
# remove empty column
df$X = NULL
df$tokenPooled <- factor(df$tokenPooled)
df$subject <- factor(df$subject)
df$native_lg <- factor(df$native_lg)
# df$playing_proficiency[df$playing_proficiency == "intermediate"] <- "amateur"
df$playing_proficiency <- factor(df$playing_proficiency, levels = c("amateur","intermediate","semi-professional","professional"))
df$block <- factor(df$block)
df$point <- as.numeric(df$point)
df$note_intensity <- factor(df$note_intensity, levels = c("piano","mezzopiano","mezzoforte","forte"))
# remove fortissimo tokens
df = df[!(is.na(df$note_intensity) & df$activity=="music"),]
dfNotes <- subset(df,df$tokenPooled == "Bb2"|
df$tokenPooled == "Bb3"|
df$tokenPooled == "D4"|
df$tokenPooled == "F3"|
df$tokenPooled == "F4")
dfNotes$tokenPooled <- factor(dfNotes$tokenPooled, levels = c("Bb2","F3","Bb3","D4","F4"))
dfNotes <- subset(dfNotes,dfNotes$note_intensity == "forte"|
dfNotes$note_intensity == "mezzoforte"|
dfNotes$note_intensity == "mezzopiano"|
dfNotes$note_intensity == "piano")
dfNotes$note_intensity <- factor(dfNotes$note_intensity, levels = c("piano","mezzopiano","mezzoforte","forte"))
dfNotes$noteIntenInt <- interaction(dfNotes$tokenPooled, dfNotes$note_intensity)
dfNotes$langNoteInt <- interaction(dfNotes$native_lg, dfNotes$tokenPooled, dfNotes$note_intensity)
dfNotes$native_lg.ord <- as.ordered(dfNotes$native_lg)
contrasts(dfNotes$native_lg.ord) <- "contr.treatment"
dfNotes$tokenPooled.ord <- as.ordered(dfNotes$tokenPooled)
contrasts(dfNotes$tokenPooled.ord) <- "contr.treatment"
dfNotes$note_intensity.ord <- as.ordered(dfNotes$note_intensity)
contrasts(dfNotes$note_intensity.ord) <- "contr.treatment"
dfNotes$langNoteInt.ord <- as.ordered(dfNotes$langNoteInt)
contrasts(dfNotes$langNoteInt.ord) <- "contr.treatment"
dfNotes$start <- dfNotes$points==1
# make copy of dfNotes
dat1 = dfNotes
dat1$predicted_values = predict(Notes.gam.AR.Mod2)
# plot in polar coordinates using plotly
dat1_NZE = dat1[dat1$native_lg == "NZE",]
dat1_Tongan = dat1[dat1$native_lg == "Tongan",]
# estimate smooths using R's generic predict.smooth.spline function
smooth_NZE=list(theta=seq(min(dat1_NZE$theta_uncut_z)*180/pi, max(dat1_NZE$theta_uncut_z)*180/pi, length=100), r=predict(smooth.spline(dat1_NZE$theta_uncut_z, dat1_NZE$predicted_values),
seq(min(dat1_NZE$theta_uncut_z), max(dat1_NZE$theta_uncut_z), length=100))$y, line=list(color="blue", dash="dash"))
smooth_Tongan=list(theta=seq(min(dat1_Tongan$theta_uncut_z)*180/pi, max(dat1_Tongan$theta_uncut_z)*180/pi, length=100), r=predict(smooth.spline(dat1_Tongan$theta_uncut_z, dat1_Tongan$predicted_values),
seq(min(dat1_Tongan$theta_uncut_z), max(dat1_Tongan$theta_uncut_z), length=100))$y, line=list(color="red", dash=""))
# set Rho max to the max of the predicted_values + 5
max_predictions = max(max(smooth_NZE$r), max(smooth_Tongan$r))
maximum=max_predictions+5
rm(max_predictions)
p = plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=smooth_NZE$theta, r=smooth_NZE$r, line=list(color=smooth_NZE$line$color[[1]], width=2.5, dash=smooth_NZE$line$dash[[1]]), name="overall average of NZE notes") %>%
add_trace(theta=smooth_Tongan$theta, r=smooth_Tongan$r, line=list(color=smooth_Tongan$line$color[[1]], width=2.5, dash=smooth_Tongan$line$dash[[1]]), name="overall average of Tongan notes") %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)), angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), title="Overall average smooths for NZE vs Tongan note productions", legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
p
if(save==TRUE){
orca(p, "Figure4.pdf",format="pdf")
orca(p, "Figure4.jpg",format="jpeg")
}
rm(dat1, dat1_NZE, dat1_Tongan, maximum, smooth_NZE, smooth_Tongan)3.2 Figure 5 (previously 4) -> H1
3.2.1 Bb2 forte
plotly_smooths_w_sig_diff(model="Notes.gam.AR.Mod2", condition="langNoteInt.ord", var1="Bb2",
var2="forte", values="theta_uncut_z",
language=c("NZE","Tongan"), fileName = "Figure5(a)", save=printPDF)Summary:
* langNoteInt.ord : factor; set to the value(s): NZE.Bb2.forte.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.654831.
* subject : factor; set to the value(s): S3. (Might be canceled as random effect, check below.)
* noteIntenInt : factor; set to the value(s): F3.mezzoforte.
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subject):noteIntenIntBb2.piano,s(theta_uncut_z,subject):noteIntenIntF3.piano,s(theta_uncut_z,subject):noteIntenIntBb3.piano,s(theta_uncut_z,subject):noteIntenIntD4.piano,s(theta_uncut_z,subject):noteIntenIntF4.piano,s(theta_uncut_z,subject):noteIntenIntBb2.mezzopiano,s(theta_uncut_z,subject):noteIntenIntF3.mezzopiano,s(theta_uncut_z,subject):noteIntenIntBb3.mezzopiano,s(theta_uncut_z,subject):noteIntenIntD4.mezzopiano,s(theta_uncut_z,subject):noteIntenIntF4.mezzopiano,s(theta_uncut_z,subject):noteIntenIntBb2.mezzoforte,s(theta_uncut_z,subject):noteIntenIntF3.mezzoforte,s(theta_uncut_z,subject):noteIntenIntBb3.mezzoforte,s(theta_uncut_z,subject):noteIntenIntD4.mezzoforte,s(theta_uncut_z,subject):noteIntenIntF4.mezzoforte,s(theta_uncut_z,subject):noteIntenIntBb2.forte,s(theta_uncut_z,subject):noteIntenIntF3.forte,s(theta_uncut_z,subject):noteIntenIntBb3.forte,s(theta_uncut_z,subject):noteIntenIntD4.forte,s(theta_uncut_z,subject):noteIntenIntF4.forte
Summary:
* langNoteInt.ord : factor; set to the value(s): Tongan.Bb2.forte.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.654831.
* subject : factor; set to the value(s): S3. (Might be canceled as random effect, check below.)
* noteIntenInt : factor; set to the value(s): F3.mezzoforte.
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subject):noteIntenIntBb2.piano,s(theta_uncut_z,subject):noteIntenIntF3.piano,s(theta_uncut_z,subject):noteIntenIntBb3.piano,s(theta_uncut_z,subject):noteIntenIntD4.piano,s(theta_uncut_z,subject):noteIntenIntF4.piano,s(theta_uncut_z,subject):noteIntenIntBb2.mezzopiano,s(theta_uncut_z,subject):noteIntenIntF3.mezzopiano,s(theta_uncut_z,subject):noteIntenIntBb3.mezzopiano,s(theta_uncut_z,subject):noteIntenIntD4.mezzopiano,s(theta_uncut_z,subject):noteIntenIntF4.mezzopiano,s(theta_uncut_z,subject):noteIntenIntBb2.mezzoforte,s(theta_uncut_z,subject):noteIntenIntF3.mezzoforte,s(theta_uncut_z,subject):noteIntenIntBb3.mezzoforte,s(theta_uncut_z,subject):noteIntenIntD4.mezzoforte,s(theta_uncut_z,subject):noteIntenIntF4.mezzoforte,s(theta_uncut_z,subject):noteIntenIntBb2.forte,s(theta_uncut_z,subject):noteIntenIntF3.forte,s(theta_uncut_z,subject):noteIntenIntBb3.forte,s(theta_uncut_z,subject):noteIntenIntD4.forte,s(theta_uncut_z,subject):noteIntenIntF4.forte
3.2.2 F3 mezzoforte
plotly_smooths_w_sig_diff(model="Notes.gam.AR.Mod2", condition="langNoteInt.ord", var1="F3",
var2="mezzoforte", values="theta_uncut_z",
language=c("NZE","Tongan"), ,fileName = "Figure5(b)",save=printPDF)Summary:
* langNoteInt.ord : factor; set to the value(s): NZE.F3.mezzoforte.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.654831.
* subject : factor; set to the value(s): S3. (Might be canceled as random effect, check below.)
* noteIntenInt : factor; set to the value(s): F3.mezzoforte.
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subject):noteIntenIntBb2.piano,s(theta_uncut_z,subject):noteIntenIntF3.piano,s(theta_uncut_z,subject):noteIntenIntBb3.piano,s(theta_uncut_z,subject):noteIntenIntD4.piano,s(theta_uncut_z,subject):noteIntenIntF4.piano,s(theta_uncut_z,subject):noteIntenIntBb2.mezzopiano,s(theta_uncut_z,subject):noteIntenIntF3.mezzopiano,s(theta_uncut_z,subject):noteIntenIntBb3.mezzopiano,s(theta_uncut_z,subject):noteIntenIntD4.mezzopiano,s(theta_uncut_z,subject):noteIntenIntF4.mezzopiano,s(theta_uncut_z,subject):noteIntenIntBb2.mezzoforte,s(theta_uncut_z,subject):noteIntenIntF3.mezzoforte,s(theta_uncut_z,subject):noteIntenIntBb3.mezzoforte,s(theta_uncut_z,subject):noteIntenIntD4.mezzoforte,s(theta_uncut_z,subject):noteIntenIntF4.mezzoforte,s(theta_uncut_z,subject):noteIntenIntBb2.forte,s(theta_uncut_z,subject):noteIntenIntF3.forte,s(theta_uncut_z,subject):noteIntenIntBb3.forte,s(theta_uncut_z,subject):noteIntenIntD4.forte,s(theta_uncut_z,subject):noteIntenIntF4.forte
Summary:
* langNoteInt.ord : factor; set to the value(s): Tongan.F3.mezzoforte.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.654831.
* subject : factor; set to the value(s): S3. (Might be canceled as random effect, check below.)
* noteIntenInt : factor; set to the value(s): F3.mezzoforte.
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subject):noteIntenIntBb2.piano,s(theta_uncut_z,subject):noteIntenIntF3.piano,s(theta_uncut_z,subject):noteIntenIntBb3.piano,s(theta_uncut_z,subject):noteIntenIntD4.piano,s(theta_uncut_z,subject):noteIntenIntF4.piano,s(theta_uncut_z,subject):noteIntenIntBb2.mezzopiano,s(theta_uncut_z,subject):noteIntenIntF3.mezzopiano,s(theta_uncut_z,subject):noteIntenIntBb3.mezzopiano,s(theta_uncut_z,subject):noteIntenIntD4.mezzopiano,s(theta_uncut_z,subject):noteIntenIntF4.mezzopiano,s(theta_uncut_z,subject):noteIntenIntBb2.mezzoforte,s(theta_uncut_z,subject):noteIntenIntF3.mezzoforte,s(theta_uncut_z,subject):noteIntenIntBb3.mezzoforte,s(theta_uncut_z,subject):noteIntenIntD4.mezzoforte,s(theta_uncut_z,subject):noteIntenIntF4.mezzoforte,s(theta_uncut_z,subject):noteIntenIntBb2.forte,s(theta_uncut_z,subject):noteIntenIntF3.forte,s(theta_uncut_z,subject):noteIntenIntBb3.forte,s(theta_uncut_z,subject):noteIntenIntD4.forte,s(theta_uncut_z,subject):noteIntenIntF4.forte
3.2.3 Bb3 mezzoforte
plotly_smooths_w_sig_diff(model="Notes.gam.AR.Mod2", condition="langNoteInt.ord", var1="Bb3",
var2="mezzoforte", values="theta_uncut_z",
language=c("NZE","Tongan"),fileName = "Figure5(c)",save=printPDF)Summary:
* langNoteInt.ord : factor; set to the value(s): NZE.Bb3.mezzoforte.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.654831.
* subject : factor; set to the value(s): S3. (Might be canceled as random effect, check below.)
* noteIntenInt : factor; set to the value(s): F3.mezzoforte.
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subject):noteIntenIntBb2.piano,s(theta_uncut_z,subject):noteIntenIntF3.piano,s(theta_uncut_z,subject):noteIntenIntBb3.piano,s(theta_uncut_z,subject):noteIntenIntD4.piano,s(theta_uncut_z,subject):noteIntenIntF4.piano,s(theta_uncut_z,subject):noteIntenIntBb2.mezzopiano,s(theta_uncut_z,subject):noteIntenIntF3.mezzopiano,s(theta_uncut_z,subject):noteIntenIntBb3.mezzopiano,s(theta_uncut_z,subject):noteIntenIntD4.mezzopiano,s(theta_uncut_z,subject):noteIntenIntF4.mezzopiano,s(theta_uncut_z,subject):noteIntenIntBb2.mezzoforte,s(theta_uncut_z,subject):noteIntenIntF3.mezzoforte,s(theta_uncut_z,subject):noteIntenIntBb3.mezzoforte,s(theta_uncut_z,subject):noteIntenIntD4.mezzoforte,s(theta_uncut_z,subject):noteIntenIntF4.mezzoforte,s(theta_uncut_z,subject):noteIntenIntBb2.forte,s(theta_uncut_z,subject):noteIntenIntF3.forte,s(theta_uncut_z,subject):noteIntenIntBb3.forte,s(theta_uncut_z,subject):noteIntenIntD4.forte,s(theta_uncut_z,subject):noteIntenIntF4.forte
Summary:
* langNoteInt.ord : factor; set to the value(s): Tongan.Bb3.mezzoforte.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.654831.
* subject : factor; set to the value(s): S3. (Might be canceled as random effect, check below.)
* noteIntenInt : factor; set to the value(s): F3.mezzoforte.
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subject):noteIntenIntBb2.piano,s(theta_uncut_z,subject):noteIntenIntF3.piano,s(theta_uncut_z,subject):noteIntenIntBb3.piano,s(theta_uncut_z,subject):noteIntenIntD4.piano,s(theta_uncut_z,subject):noteIntenIntF4.piano,s(theta_uncut_z,subject):noteIntenIntBb2.mezzopiano,s(theta_uncut_z,subject):noteIntenIntF3.mezzopiano,s(theta_uncut_z,subject):noteIntenIntBb3.mezzopiano,s(theta_uncut_z,subject):noteIntenIntD4.mezzopiano,s(theta_uncut_z,subject):noteIntenIntF4.mezzopiano,s(theta_uncut_z,subject):noteIntenIntBb2.mezzoforte,s(theta_uncut_z,subject):noteIntenIntF3.mezzoforte,s(theta_uncut_z,subject):noteIntenIntBb3.mezzoforte,s(theta_uncut_z,subject):noteIntenIntD4.mezzoforte,s(theta_uncut_z,subject):noteIntenIntF4.mezzoforte,s(theta_uncut_z,subject):noteIntenIntBb2.forte,s(theta_uncut_z,subject):noteIntenIntF3.forte,s(theta_uncut_z,subject):noteIntenIntBb3.forte,s(theta_uncut_z,subject):noteIntenIntD4.forte,s(theta_uncut_z,subject):noteIntenIntF4.forte
3.2.4 Intervals of significant difference 16 out of 19 comparisons
# read .csv file
sig_diffs = read.csv(file = "Notes.gam.AR.Mod2_intervals_of_significant_differences.csv",
header=TRUE, row.names=1)
# Got rid of this plot!
# # visualize areas of overall difference
# p1=plot_ly(type='scatterpolar', mode='lines') %>%
# add_trace(theta=seq(sig_diffs["17_out_of_19",1]*180/pi, sig_diffs["17_out_of_19",2]*180/pi,
# length.out=20), r=c(0, rep(320, 18), 0), line=list(color="black", width=0.5),
# fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
# add_trace(theta=seq(sig_diffs["17_out_of_19",3]*180/pi, sig_diffs["17_out_of_19",4]*180/pi,
# length.out=20), r=c(0, rep(320, 18), 0), line=list(color="black", width=0.5),
# fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
# layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,320)),
# angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
# title=paste0("Intervals of significant differences for 17 out of 19 comparisons"),
# legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
# Sys.sleep(0)
# print(p1)
p2=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=seq(sig_diffs["16_out_of_19",1]*180/pi, sig_diffs["16_out_of_19",2]*180/pi,
length.out=20), r=c(0, rep(320, 18), 0), line=list(color="black", width=0.5),
fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
add_trace(theta=seq(sig_diffs["16_out_of_19",3]*180/pi, sig_diffs["16_out_of_19",4]*180/pi,
length.out=20), r=c(0, rep(320, 18), 0), line=list(color="black", width=0.5),
fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,320)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title=paste0("Intervals of significant differences for 16 out of 19 comparisons"),
legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
Sys.sleep(0)
print(p2)
NULLif(printPDF==TRUE)
{
orca(p2, "Figure5(d).pdf",format="pdf")
orca(p2, "Figure5(d).jpg",format="jpeg")
}
rm(sig_diffs, p1, p2)3.3 Figure 6 -> H1b
# df already loaded above
dfSummary <- group_by(df, subject, playing_proficiency, activity, native_lg, tokenPooled, theta_uncut_z_group = cut(theta_uncut_z,breaks=100)) %>% summarise(rhoVar = sd(rho_uncut_z,na.rm=TRUE))
dfSummary$theta_uncut_z = as.character(dfSummary$theta_uncut_z_group)
dfSummary$theta_uncut_z = gsub("\\[|\\]|\\(|\\)", "",dfSummary$theta_uncut_z)
dfSummary$theta_uncut_z = strsplit(dfSummary$theta_uncut_z,",")
dfSummary$theta_uncut_z2 = 1
for(i in c(1:nrow(dfSummary)))
{
dfSummary$theta_uncut_z2[i] = mean(as.numeric(unlist(dfSummary$theta_uncut_z[i])))
}
dfSummary$theta_uncut_z=dfSummary$theta_uncut_z2
dfSummary$playing_proficiency.ord <- as.ordered(dfSummary$playing_proficiency)
contrasts(dfSummary$playing_proficiency.ord) <- "contr.treatment"
dfSummary$activity.ord <- as.ordered(dfSummary$activity)
contrasts(dfSummary$activity.ord) <- "contr.treatment"
dfSummary$langNoteInt <- interaction(dfSummary$native_lg,
dfSummary$tokenPooled)
dfSummary$langNoteInt.ord <- as.ordered(dfSummary$langNoteInt)
contrasts(dfSummary$langNoteInt.ord) <- "contr.treatment"
dfSummary$native_lg.ord <- as.ordered(dfSummary$native_lg)
contrasts(dfSummary$native_lg.ord) <- "contr.treatment"
dfSummary$tokenPooled.ord <- as.ordered(dfSummary$tokenPooled)
contrasts(dfSummary$tokenPooled.ord) <- "contr.treatment"
dfSummary = na.omit(dfSummary)
#dfSummary$start <- dfSummary$theta_uncut_z==min(dfSummary$theta_uncut_z)
dfSummary$start = TRUE
for(i in unique(dfSummary$subject))
{
dfSubject = subset(dfSummary,dfSummary$subject == i)
for(j in unique(dfSubject$tokenPooled))
{
dfSummary$start[dfSummary$subject == i & dfSummary$tokenPooled == j] <-
dfSummary$theta_uncut_z[dfSummary$subject == i & dfSummary$tokenPooled == j] ==
min(dfSummary$theta_uncut_z[dfSummary$subject == i & dfSummary$tokenPooled == j])
}
}
# make copy of dfNotes
dat1 = dfSummary
dat1$predicted_values = predict(VAR.gam.AR.Mod2)
# plot in polar coordinates using plotly
dat1_NZE = dat1[dat1$native_lg == "NZE",]
dat1_Tongan = dat1[dat1$native_lg == "Tongan",]
# estimate smooths using R's generic predict.smooth.spline function
smooth_NZE=list(theta=seq(min(dat1_NZE$theta_uncut_z)*180/pi, max(dat1_NZE$theta_uncut_z)*180/pi, length=100), r=predict(smooth.spline(dat1_NZE$theta_uncut_z, dat1_NZE$predicted_values),
seq(min(dat1_NZE$theta_uncut_z), max(dat1_NZE$theta_uncut_z), length=100))$y, line=list(color="blue", dash="dash"))
smooth_Tongan=list(theta=seq(min(dat1_Tongan$theta_uncut_z)*180/pi, max(dat1_Tongan$theta_uncut_z)*180/pi, length=100), r=predict(smooth.spline(dat1_Tongan$theta_uncut_z, dat1_Tongan$predicted_values),
seq(min(dat1_Tongan$theta_uncut_z), max(dat1_Tongan$theta_uncut_z), length=100))$y, line=list(color="red", dash=""))
# set Rho max to the max of the predicted_values + 5
max_predictions = max(max(smooth_NZE$r), max(smooth_Tongan$r))
maximum=max_predictions+5
rm(max_predictions)
p = plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=smooth_NZE$theta, r=smooth_NZE$r, line=list(color=smooth_NZE$line$color[[1]], width=2.5, dash=smooth_NZE$line$dash[[1]]), name="overall average of NZE notes") %>%
add_trace(theta=smooth_Tongan$theta, r=smooth_Tongan$r, line=list(color=smooth_Tongan$line$color[[1]], width=2.5, dash=smooth_Tongan$line$dash[[1]]), name="overall average of Tongan notes") %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)), angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), title="Overall average smooths for NZE vs Tongan note productions", legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
p
if(printPDF==TRUE)
{
orca(p, "Figure6.pdf",format="pdf")
orca(p, "Figure6.jpg",format="jpeg")
}
rm(dat1, dat1_NZE, dat1_Tongan, maximum, smooth_NZE, smooth_Tongan)3.4 Figure 7 -> H2 vowels and notes NZE
3.4.1 All NZE vowels
# define colors from PhD thesis
RED0 = (rgb(213,13,11, 255, maxColorValue=255))
BROWN0 = (rgb(123,73,55,255, maxColorValue=255))
PURPLE0 = (rgb(143,72,183,255, maxColorValue=255))
GRAY0 = (rgb(108,108,108,255, maxColorValue=255))
GREEN0 = (rgb(0,151,55,255, maxColorValue=255))
ORANGE0 = (rgb(255,123,0,255, maxColorValue=255))
PINK0 = (rgb(224,130,180,255, maxColorValue=255))
TEAL0 = (rgb(0,175,195,255, maxColorValue=255))
GOLD0 = (rgb(172,181,0,255, maxColorValue=255))
light_blue=rgb(0,117,220,255,maxColorValue=255)
black=rgb(25,25,25,255,maxColorValue=255)
pink=rgb(194,0,136,255,maxColorValue=255)
dark_blue=rgb(0,51,128,255,maxColorValue=255)
dark_red=rgb(153,0,0,255,maxColorValue=255)
# correct order
colors = c(ORANGE0, dark_blue, GREEN0, light_blue, PINK0, GOLD0, BROWN0, TEAL0, GRAY0, dark_red,
RED0, PURPLE0, pink)
# run plot_smooth to grab values for fit, upper & lower bounds
p1=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="iː"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): iː.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p2=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="e"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): e.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p3=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ʉː"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ʉː.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p4=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɛ"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ɛ.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p5=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɵː"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ɵː.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p6=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɘ"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ɘ.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p7=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ə"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ə.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p8=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ʊ"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ʊ.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p9=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ə#"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ə#.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p10=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɐ"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ɐ.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p11=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɐː"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ɐː.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p12=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɒ"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ɒ.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p13=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="oː"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): oː.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
smooth_names=c("FLEECE /iː/","DRESS /e/","GOOSE /ʉː/","TRAP /ɛ/","NURSE /ɵː/","KIT /ɘ/",
"non-final schwa /ə/","FOOT /ʊ/","final schwa /ə#/","STRUT /ɐ/","START /ɐː/",
"LOT /ɒ/","THOUGHT /oː/")
# # set Rho max to the max of the fit + half the difference between max of the fit and the upper limit
# max_ul = max(p1$fv$ul, p2$fv$ul, p3$fv$ul, p4$fv$ul, p5$fv$ul, p6$fv$ul, p7$fv$ul, p8$fv$ul, p9$fv$ul, p10$fv$ul, p11$fv$ul, p12$fv$ul, p13$fv$ul)
# max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit, p6$fv$fit, p7$fv$fit, p8$fv$fit, p9$fv$fit, p10$fv$fit, p11$fv$fit, p12$fv$fit, p13$fv$fit)
# maximum=max_fit+((max_ul-max_fit)/2)
# rm(max_ul, max_fit)
#
# # plot in polar coordinates with confidence bounds
# plot1=plot_ly(type='scatterpolar', mode='lines') %>%
# add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], dash="dash", width=2.5), name=smooth_names[1]) %>%
# add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color=colors[1], dash="dash", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color=colors[1], dash="dash", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], width=2.5),
# name=smooth_names[2]) %>%
# add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color=colors[2], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color=colors[2], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], width=2.5),
# name=smooth_names[3]) %>%
# add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$ul, line=list(color=colors[3], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$ll, line=list(color=colors[3], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], dash="dash", width=2.5), name=smooth_names[4]) %>%
# add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$ul, line=list(color=colors[4], dash="dash", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$ll, line=list(color=colors[4], dash="dash", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=2.5),
# name=smooth_names[5]) %>%
# add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$ul, line=list(color=colors[5], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$ll, line=list(color=colors[5], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$fit, line=list(color=colors[6], dash="dash", width=2.5), name=smooth_names[6]) %>%
# add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$ul, line=list(color=colors[6], dash="dash", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$ll, line=list(color=colors[6], dash="dash", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$fit, line=list(color=colors[7], width=2.5),
# name=smooth_names[7]) %>%
# add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$ul, line=list(color=colors[7], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$ll, line=list(color=colors[7], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$fit, line=list(color=colors[8], dash="dash", width=2.5), name=smooth_names[8]) %>%
# add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$ul, line=list(color=colors[8], dash="dash", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$ll, line=list(color=colors[8], dash="dash", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$fit, line=list(color=colors[9], width=2.5),
# name=smooth_names[9]) %>%
# add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$ul, line=list(color=colors[9], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$ll, line=list(color=colors[9], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$fit, line=list(color=colors[10], width=2.5),
# name=smooth_names[10]) %>%
# add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$ul, line=list(color=colors[10], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$ll, line=list(color=colors[10], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p11$fv$theta_uncut_z*180/pi, r=p11$fv$fit, line=list(color=colors[11], width=2.5),
# name=smooth_names[11]) %>%
# add_trace(theta=p11$fv$theta_uncut_z*180/pi, r=p11$fv$ul, line=list(color=colors[11], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p11$fv$theta_uncut_z*180/pi, r=p11$fv$ll, line=list(color=colors[11], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p12$fv$theta_uncut_z*180/pi, r=p12$fv$fit, line=list(color=colors[12], dash="dash", width=2.5), name=smooth_names[12]) %>%
# add_trace(theta=p12$fv$theta_uncut_z*180/pi, r=p12$fv$ul, line=list(color=colors[12], dash="dash", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p12$fv$theta_uncut_z*180/pi, r=p12$fv$ll, line=list(color=colors[12], dash="dash", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p13$fv$theta_uncut_z*180/pi, r=p13$fv$fit, line=list(color=colors[13], width=2.5),
# name=smooth_names[13]) %>%
# add_trace(theta=p13$fv$theta_uncut_z*180/pi, r=p13$fv$ul, line=list(color=colors[13], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p13$fv$theta_uncut_z*180/pi, r=p13$fv$ll, line=list(color=colors[13], dash="dot", width=0.5), showlegend=FALSE) %>%
# layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
# angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
# title="All vowels produced by NZE speakers")
# plot1
# set Rho max to the max of the fit + 5
max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit, p6$fv$fit, p7$fv$fit, p8$fv$fit, p9$fv$fit, p10$fv$fit, p11$fv$fit, p12$fv$fit, p13$fv$fit)
maximum=max_fit+5
rm(max_fit)
# plot in polar coordinates without confidence bounds
plot2=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], dash="dash", width=2.5), name=smooth_names[1]) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], width=2.5),
name=smooth_names[2]) %>%
add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], width=2.5),
name=smooth_names[3]) %>%
add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], dash="dash", width=2.5), name=smooth_names[4]) %>%
add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=2.5),
name=smooth_names[5]) %>%
add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$fit, line=list(color=colors[6], dash="dash", width=2.5), name=smooth_names[6]) %>%
add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$fit, line=list(color=colors[7], width=2.5),
name=smooth_names[7]) %>%
add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$fit, line=list(color=colors[8], dash="dash", width=2.5), name=smooth_names[8]) %>%
add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$fit, line=list(color=colors[9], width=2.5),
name=smooth_names[9]) %>%
add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$fit, line=list(color=colors[10], width=2.5),
name=smooth_names[10]) %>%
add_trace(theta=p11$fv$theta_uncut_z*180/pi, r=p11$fv$fit, line=list(color=colors[11], width=2.5),
name=smooth_names[11]) %>%
add_trace(theta=p12$fv$theta_uncut_z*180/pi, r=p12$fv$fit, line=list(color=colors[12], dash="dash", width=2.5), name=smooth_names[12]) %>%
add_trace(theta=p13$fv$theta_uncut_z*180/pi, r=p13$fv$fit, line=list(color=colors[13], width=2.5),
name=smooth_names[13]) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title="All vowels produced by NZE speakers",
legend=list(orientation="h", xanchor="center", x=0.5,y=0.008))
plot2
if(printPDF==TRUE)
{
orca(plot2, "Figure7(a).pdf",format="pdf")
orca(plot2, "Figure7(a).jpg",format="jpeg")
}
rm(p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12,p13)3.4.2 All NZE notes
# correct order
colors = c(black, black, black, black, black)
p1=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb2"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): Bb2.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p2=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F3"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): F3.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p3=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb3"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): Bb3.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p4=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="D4"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): D4.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p5=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F4"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): F4.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
smooth_names=c("Bb2","F3","Bb3","D4","F4")
# set Rho max to the max of the fit + 5
#max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit)
#maximum=max_fit+5
#rm(max_fit)
# plot in polar coordinates without confidence bounds
p=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], width=2.5), name=smooth_names[1]) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], dash="dash", width=2.5), name=smooth_names[2]) %>%
add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], dash="dot", width=2.5), name=smooth_names[3]) %>%
add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], dash="dashdot", width=2.5), name=smooth_names[4]) %>%
add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=1),
name=smooth_names[5]) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title="All notes produced by NZE speakers",
legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
p
if(printPDF==TRUE)
{
orca(p, "Figure7(b).pdf",format="pdf")
orca(p, "Figure7(b).jpg",format="jpeg")
}
rm(p1,p2,p3,p4,p5)3.5 Figure 8 -> H2 vowels and notes Tongan
3.5.1 All Tongan vowels
# use colors from PhD thesis (loaded above)
colors = c(ORANGE0, dark_blue, RED0, pink, GREEN0)
# run plot_smooth to grab values for fit, upper & lower bounds
p1=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="i"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): i.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p2=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="e"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): e.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p3=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="a"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): a.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p4=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="o"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): o.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p5=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="u"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): u.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
smooth_names=c("/i/","/e/","/a/","/o/","/u/")
# set Rho max to the max of the fit + half the difference between max of the fit and the upper limit
max_ul = max(p1$fv$ul, p2$fv$ul, p3$fv$ul, p4$fv$ul, p5$fv$ul)
max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit)
maximum=max_fit+((max_ul-max_fit)/2)
rm(max_ul, max_fit)
# plot in polar coordinates with confidence bounds
plot1=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], width=2.5),
name=smooth_names[1]) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color=colors[1], dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color=colors[1], dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], width=2.5),
name=smooth_names[2]) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color=colors[2], dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color=colors[2], dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], width=2.5),
name=smooth_names[3]) %>%
add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$ul, line=list(color=colors[3], dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$ll, line=list(color=colors[3], dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], width=2.5),
name=smooth_names[4]) %>%
add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$ul, line=list(color=colors[4], dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$ll, line=list(color=colors[4], dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=2.5),
name=smooth_names[5]) %>%
add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$ul, line=list(color=colors[5], dash="dot", width=0.5), showlegend=FALSE) %>%
add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$ll, line=list(color=colors[5], dash="dot", width=0.5), showlegend=FALSE) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title="All vowels produced by Tongan speakers",
legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
plot1
# # set Rho max to the max of the fit + 5
# max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit)
# maximum=max_fit+5
# rm(max_fit)
#
# # plot in polar coordinates with confidence bounds
# plot2=plot_ly(type='scatterpolar', mode='lines') %>%
# add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], width=2.5),
# name=smooth_names[1]) %>%
# add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], width=2.5),
# name=smooth_names[2]) %>%
# add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], width=2.5),
# name=smooth_names[3]) %>%
# add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], width=2.5),
# name=smooth_names[4]) %>%
# add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=2.5),
# name=smooth_names[5]) %>%
# layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
# angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
# title="All vowels produced by Tongan speakers",
# legend=list(orientation="h", xanchor="center", x=0.5))
# plot2
if(printPDF==TRUE)
{
orca(plot1, "Figure8(a).pdf",format="pdf")
orca(plot1, "Figure8(a).jpg",format="jpeg")
}
rm(p1,p2,p3,p4,p5)3.5.2 All Tongan notes
# use colors from PhD thesis (loaded above)
colors = c(black, black, black, black, black)
p1=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb2"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): Bb2.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p2=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F3"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): F3.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p3=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb3"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): Bb3.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p4=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="D4"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): D4.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p5=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F4"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): F4.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
smooth_names=c("Bb2","F3","Bb3","D4","F4")
# plot in polar coordinates without confidence bounds
p=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], width=2.5), name=smooth_names[1]) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], dash="dash", width=2.5), name=smooth_names[2]) %>%
add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], dash="dot", width=2.5), name=smooth_names[3]) %>%
add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], dash="dashdot", width=2.5), name=smooth_names[4]) %>%
add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=1),
name=smooth_names[5]) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title="All notes produced by Tongan speakers",
legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
p
if(printPDF==TRUE)
{
orca(p, "Figure8(b).pdf",format="pdf")
orca(p, "Figure8(b).jpg",format="jpeg")
}
rm(p1,p2,p3,p4,p5)3.6 Figure 9 -> H2 selected vowels with notes
3.6.1 with NZE notes
colors = c(black, black, black, black, black,BROWN0, GRAY0, PURPLE0, pink, GREEN0)
# run plot_smooth to grab values for fit, upper & lower bounds
# NZE notes
p1=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb2"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): Bb2.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p2=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F3"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): F3.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p3=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb3"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): Bb3.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p4=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="D4"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): D4.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p5=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F4"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): F4.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
# vowels
p6=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ə"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ə.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p7=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ə#"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ə#.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p8=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɒ"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ɒ.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p9=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="o"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): o.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p10=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="u"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): u.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
smooth_names=c("Bb2 NZE","F3 NZE","Bb3 NZE","D4 NZE","F4 NZE","non-final schwa /ə/ NZE","final schwa /ə#/ NZE","LOT /ɒ/ NZE","/o/ Tongan","/u/ Tongan")
# set Rho max to the max of the fit + 5
max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit, p6$fv$fit, p7$fv$fit, p8$fv$fit, p9$fv$fit, p10$fv$fit)
maximum=max_fit+5
rm(max_fit)
# plot in polar coordinates without confidence bounds
plot2=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], width=2.5), name=smooth_names[1]) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], dash="dash", width=2.5), name=smooth_names[2]) %>%
add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], dash="dot", width=2.5), name=smooth_names[3]) %>%
add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], dash="dashdot", width=2.5), name=smooth_names[4]) %>%
add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=1), name=smooth_names[5]) %>%
add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$fit, line=list(color=colors[6], width=2.5), name=smooth_names[6]) %>%
add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$fit, line=list(color=colors[7], width=2.5), name=smooth_names[7]) %>%
add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$fit, line=list(color=colors[8], dash="dash", width=2.5), name=smooth_names[8]) %>%
add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$fit, line=list(color=colors[9], width=2.5), name=smooth_names[9]) %>%
add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$fit, line=list(color=colors[10], width=2.5),
name=smooth_names[10]) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title="Tongan & NZE vowels closest to playing position with NZE notes",legend=list(orientation="h", xanchor="center", x=0.5,y=0.008))
plot2
if(printPDF==TRUE)
{
orca(plot2, "Figure9(a).pdf",format="pdf")
orca(plot2, "Figure9(a).jpeg",format="jpeg")
}
rm(p1,p2,p3,p4,p5,p6,p7,p8,p9,p10)3.6.2 with Tongan notes
colors = c(black, black, black, black, black,BROWN0, GRAY0, PURPLE0, pink, GREEN0)
# run plot_smooth to grab values for fit, upper & lower bounds
# Tongan notes
p1=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb2"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): Bb2.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p2=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F3"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): F3.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p3=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb3"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): Bb3.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p4=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="D4"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): D4.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p5=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F4"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): F4.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
# vowels
p6=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ə"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ə.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p7=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ə#"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ə#.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p8=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɒ"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): ɒ.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.627597 to -0.617214.
* subVowelInt : factor; set to the value(s): S3.ə. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): coronals.ə. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p9=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="o"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): o.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
p10=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="u"), rm.ranef=TRUE)Summary:
* tokenPooled.ord : factor; set to the value(s): u.
* theta_uncut_z : numeric predictor; with 30 values ranging from -2.648777 to -0.708619.
* subVowelInt : factor; set to the value(s): S16.F3. (Might be canceled as random effect, check below.)
* precSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* follSoundVowelInt : factor; set to the value(s): mezzoforte.F3. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(theta_uncut_z,subVowelInt),s(theta_uncut_z,precSoundVowelInt),s(theta_uncut_z,follSoundVowelInt)
smooth_names=c("Bb2 Tongan","F3 Tongan","Bb3 Tongan","D4 Tongan","F4 Tongan","non-final schwa /ə/ NZE","final schwa /ə#/ NZE","LOT /ɒ/ NZE","/o/ Tongan","/u/ Tongan")
# # set Rho max to the max of the fit + 5
# max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit, p6$fv$fit, p7$fv$fit, p8$fv$fit, p9$fv$fit, p10$fv$fit)
# maximum=max_fit+5
# rm(max_fit)
#
# # plot in polar coordinates with confidence bounds
# plot1=plot_ly(type='scatterpolar', mode='lines') %>%
# add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], width=2.5), name=smooth_names[1]) %>%
# add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color=colors[1], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color=colors[1], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], width=2.5), name=smooth_names[2]) %>%
# add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color=colors[2], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color=colors[2], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], dash="dash", width=2.5), name=smooth_names[3]) %>%
# add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$ul, line=list(color=colors[3], dash="dash", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$ll, line=list(color=colors[3], dash="dash", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], width=2.5),
# name=smooth_names[4]) %>%
# add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$ul, line=list(color=colors[4], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$ll, line=list(color=colors[4], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=2.5),
# name=smooth_names[5]) %>%
# add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$ul, line=list(color=colors[5], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$ll, line=list(color=colors[5], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$fit, line=list(color=colors[6], width=1),
# name=smooth_names[6]) %>%
# add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$ul, line=list(color=colors[6], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$ll, line=list(color=colors[6], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$fit, line=list(color=colors[7], dash="dashdot", width=2.5), name=smooth_names[7]) %>%
# add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$ul, line=list(color=colors[7], dash="dashdot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$ll, line=list(color=colors[7], dash="dashdot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$fit, line=list(color=colors[8], dash="dot", width=2.5), name=smooth_names[8]) %>%
# add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$ul, line=list(color=colors[8], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$ll, line=list(color=colors[8], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$fit, line=list(color=colors[9], dash="dash", width=2.5), name=smooth_names[9]) %>%
# add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$ul, line=list(color=colors[9], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$ll, line=list(color=colors[9], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$fit, line=list(color=colors[10], width=2.5),
# name=smooth_names[10]) %>%
# add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$ul, line=list(color=colors[10], dash="dot", width=0.5), showlegend=FALSE) %>%
# add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$ll, line=list(color=colors[10], dash="dot", width=0.5), showlegend=FALSE) %>%
# layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
# angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
# title="Tongan & NZE vowels closest to playing position with Tongan notes")
# plot1
# set Rho max to the max of the fit + 5
max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit, p6$fv$fit, p7$fv$fit, p8$fv$fit, p9$fv$fit, p10$fv$fit)
maximum=max_fit+5
rm(max_fit)
# plot in polar coordinates without confidence bounds
plot2=plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], width=2.5), name=smooth_names[1]) %>%
add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], dash="dash", width=2.5), name=smooth_names[2]) %>%
add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], dash="dot", width=2.5), name=smooth_names[3]) %>%
add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], dash="dashdot", width=2.5), name=smooth_names[4]) %>%
add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=1), name=smooth_names[5]) %>%
add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$fit, line=list(color=colors[6], width=2.5), name=smooth_names[6]) %>%
add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$fit, line=list(color=colors[7], width=2.5), name=smooth_names[7]) %>%
add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$fit, line=list(color=colors[8], dash="dash", width=2.5), name=smooth_names[8]) %>%
add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$fit, line=list(color=colors[9], width=2.5), name=smooth_names[9]) %>%
add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$fit, line=list(color=colors[10], width=2.5),
name=smooth_names[10]) %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title="Tongan & NZE vowels closest to playing position with Tongan notes",legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
plot2
if(printPDF==TRUE)
{
orca(plot2, "Figure9(b).pdf",format="pdf")
orca(plot2, "Figure9(b).jpeg",format="jpeg")
}
rm(p1,p2,p3,p4,p5,p6,p7,p8,p9,p10)3.7 Figure 10 ‘cardinal vowels’ -> updated version using only vowels surrounded by coronals
# create dfNZE & dfTongan -> df already loaded and manipulated above
dfNZE <- subset(df,df$native_lg=="NZE")
dfNZE$tokenPooled <- factor(dfNZE$tokenPooled, levels = c("ɐː","ɐ","ɛ","ɵː","e","iː","ʉː","ʊ","oː","ɒ","ɘ","ə","ə#","Bb2","F3","Bb3","D4","F4"))
dfNZE$playing_proficiency <- as.factor(dfNZE$playing_proficiency)
dfNZE$note_intensity[is.na(dfNZE$note_intensity)] = "NULL"
dfNZE$subVowelInt <- interaction(dfNZE$subject, dfNZE$tokenPooled)
dfNZE$precSoundVowelInt <- interaction(dfNZE$speech_prec_pooled, dfNZE$tokenPooled)
dfNZE$follSoundVowelInt <- interaction(dfNZE$speech_fol_pooled, dfNZE$tokenPooled)
dfNZE$tokenPooled.ord <- as.ordered(dfNZE$tokenPooled)
contrasts(dfNZE$tokenPooled.ord) <- "contr.treatment"
dfNZE$vowels_pooled.ord <- as.ordered(dfNZE$vowels_pooled)
contrasts(dfNZE$vowels_pooled.ord) <- "contr.treatment"
dfNZE$playing_proficiency.ord <- as.ordered(dfNZE$playing_proficiency)
contrasts(dfNZE$playing_proficiency.ord) <- "contr.treatment"
dfNZE$start <- dfNZE$points==1
dfTongan <- subset(df,df$native_lg=="Tongan")
dfTongan$tokenPooled <- factor(dfTongan$tokenPooled, levels = c("aː","a","eː","e","iː","i","uː","u","oː","o","Bb2","F3","Bb3","D4","F4"))
dfTongan$tokenPooled[dfTongan$tokenPooled == "aː"] = "a"
dfTongan$tokenPooled[dfTongan$tokenPooled == "eː"] = "e"
dfTongan$tokenPooled[dfTongan$tokenPooled == "iː"] = "i"
dfTongan$tokenPooled[dfTongan$tokenPooled == "uː"] = "u"
dfTongan$tokenPooled[dfTongan$tokenPooled == "oː"] = "o"
dfTongan$tokenPooled <- factor(dfTongan$tokenPooled)
dfTongan$playing_proficiency <- as.factor(dfTongan$playing_proficiency)
dfTongan$speech_prec_pooled[is.na(dfTongan$speech_prec_pooled)] = "NULL"
dfTongan$speech_fol_pooled[is.na(dfTongan$speech_fol_pooled)] = "NULL"
dfTongan$subVowelInt <- interaction(dfTongan$subject, dfTongan$tokenPooled)
dfTongan$precSoundVowelInt <- interaction(dfTongan$speech_prec_pooled, dfTongan$tokenPooled)
dfTongan$follSoundVowelInt <- interaction(dfTongan$speech_fol_pooled, dfTongan$tokenPooled)
dfTongan$tokenPooled.ord <- as.ordered(dfTongan$tokenPooled)
contrasts(dfTongan$tokenPooled.ord) <- "contr.treatment"
dfTongan$vowels_pooled.ord <- as.ordered(dfTongan$vowels_pooled)
contrasts(dfTongan$vowels_pooled.ord) <- "contr.treatment"
dfTongan$playing_proficiency.ord <- as.ordered(dfTongan$playing_proficiency)
contrasts(dfTongan$playing_proficiency.ord) <- "contr.treatment"
dfTongan$start <- dfTongan$points==1
# # inspect vowel token numbers in different preceding and following contexts
# cat("\ndfNZE$speech_prec_pooled\n")
# table(dfNZE$speech_prec_pooled)
# cat("\ndfNZE$speech_fol_pooled\n")
# table(dfNZE$speech_fol_pooled)
# cat("\ndfTongan$speech_prec_pooled\n")
# table(dfTongan$speech_prec_pooled)
# cat("\ndfNZE$speech_fol_pooled\n")
# table(dfTongan$speech_fol_pooled)
# make copies of dfNZE and dfTongan
dat_NZE = dfNZE
dat_Tongan = dfTongan
# predict values from models
dat_NZE$predicted_values = predict(NZE.gam.AR.Mod2)
dat_Tongan$predicted_values = predict(Tongan.gam.AR.Mod2)
# create new data.frames
NZE_FLEECE = dat_NZE[dat_NZE$tokenPooled == "iː" & dat_NZE$speech_prec_pooled == "coronals" & dat_NZE$speech_fol_pooled == "coronals",]
Tongan_i = dat_Tongan[dat_Tongan$tokenPooled == "i" & dat_Tongan$speech_prec_pooled == "coronals" & dat_Tongan$speech_fol_pooled == "coronals",]
NZE_DRESS = dat_NZE[dat_NZE$tokenPooled == "e" & dat_NZE$speech_prec_pooled == "coronals" & dat_NZE$speech_fol_pooled == "coronals",]
Tongan_e = dat_Tongan[dat_Tongan$tokenPooled == "e" & dat_Tongan$speech_prec_pooled == "coronals" & dat_Tongan$speech_fol_pooled == "coronals",]
NZE_START = dat_NZE[dat_NZE$tokenPooled == "ɐː" & dat_NZE$speech_prec_pooled == "coronals" & dat_NZE$speech_fol_pooled == "coronals",]
Tongan_a = dat_Tongan[dat_Tongan$tokenPooled == "a" & dat_Tongan$speech_prec_pooled == "coronals" & dat_Tongan$speech_fol_pooled == "coronals",]
NZE_THOUGHT = dat_NZE[dat_NZE$tokenPooled == "oː" & dat_NZE$speech_prec_pooled == "coronals" & dat_NZE$speech_fol_pooled == "coronals",]
Tongan_o = dat_Tongan[dat_Tongan$tokenPooled == "o" & dat_Tongan$speech_prec_pooled == "coronals" & dat_Tongan$speech_fol_pooled == "coronals",]
# estimate smooths using R's generic predict.smooth.spline function
smooth_NZE_FLEECE=list(theta=seq(min(NZE_FLEECE$theta_uncut_z)*180/pi,
max(NZE_FLEECE$theta_uncut_z)*180/pi, length=100),
r=predict(smooth.spline(NZE_FLEECE$theta_uncut_z, NZE_FLEECE$predicted_values),
seq(min(NZE_FLEECE$theta_uncut_z), max(NZE_FLEECE$theta_uncut_z),
length=100))$y, line=list(color="ORANGE0", dash="dash"))
smooth_Tongan_i=list(theta=seq(min(Tongan_i$theta_uncut_z)*180/pi,
max(Tongan_i$theta_uncut_z)*180/pi, length=100),
r=predict(smooth.spline(Tongan_i$theta_uncut_z, Tongan_i$predicted_values),
seq(min(Tongan_i$theta_uncut_z), max(Tongan_i$theta_uncut_z),
length=100))$y, line=list(color="ORANGE0", dash=""))
smooth_NZE_DRESS=list(theta=seq(min(NZE_DRESS$theta_uncut_z)*180/pi,
max(NZE_DRESS$theta_uncut_z)*180/pi, length=100),
r=predict(smooth.spline(NZE_DRESS$theta_uncut_z, NZE_DRESS$predicted_values),
seq(min(NZE_DRESS$theta_uncut_z), max(NZE_DRESS$theta_uncut_z),
length=100))$y, line=list(color="dark_blue", dash="dash"))
smooth_Tongan_e=list(theta=seq(min(Tongan_e$theta_uncut_z)*180/pi,
max(Tongan_e$theta_uncut_z)*180/pi, length=100),
r=predict(smooth.spline(Tongan_e$theta_uncut_z, Tongan_e$predicted_values),
seq(min(Tongan_e$theta_uncut_z), max(Tongan_e$theta_uncut_z),
length=100))$y, line=list(color="dark_blue", dash=""))
smooth_NZE_START=list(theta=seq(min(NZE_START$theta_uncut_z)*180/pi,
max(NZE_START$theta_uncut_z)*180/pi, length=100),
r=predict(smooth.spline(NZE_START$theta_uncut_z, NZE_START$predicted_values),
seq(min(NZE_START$theta_uncut_z), max(NZE_START$theta_uncut_z),
length=100))$y, line=list(color="RED0", dash="dash"))
smooth_Tongan_a=list(theta=seq(min(Tongan_a$theta_uncut_z)*180/pi,
max(Tongan_a$theta_uncut_z)*180/pi, length=100),
r=predict(smooth.spline(Tongan_a$theta_uncut_z, Tongan_a$predicted_values),
seq(min(Tongan_a$theta_uncut_z), max(Tongan_a$theta_uncut_z),
length=100))$y, line=list(color="RED0", dash=""))
smooth_NZE_THOUGHT=list(theta=seq(min(NZE_THOUGHT$theta_uncut_z)*180/pi,
max(NZE_THOUGHT$theta_uncut_z)*180/pi, length=100),
r=predict(smooth.spline(NZE_THOUGHT$theta_uncut_z,
NZE_THOUGHT$predicted_values),
seq(min(NZE_THOUGHT$theta_uncut_z), max(NZE_THOUGHT$theta_uncut_z),
length=100))$y, line=list(color="pink", dash="dash"))
smooth_Tongan_o=list(theta=seq(min(Tongan_o$theta_uncut_z)*180/pi,
max(Tongan_o$theta_uncut_z)*180/pi, length=100),
r=predict(smooth.spline(Tongan_o$theta_uncut_z, Tongan_o$predicted_values),
seq(min(Tongan_o$theta_uncut_z), max(Tongan_o$theta_uncut_z),
length=100))$y, line=list(color="pink", dash=""))
# set Rho max to the max of the predicted_values + 5
max_predictions = max(max(smooth_NZE_FLEECE$r), max(smooth_NZE_DRESS$r), max(smooth_NZE_START$r),
max(smooth_NZE_THOUGHT$r), max(smooth_Tongan_i$r), max(smooth_Tongan_e$r),
max(smooth_Tongan_a$r), max(smooth_Tongan_o$r))
maximum=max_predictions+5
rm(max_predictions)
# colors from smooths don't get passed on correctly -> specify again
colors = c(ORANGE0, ORANGE0, dark_blue, dark_blue, RED0, RED0, pink, pink)
p = plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=smooth_NZE_FLEECE$theta, r=smooth_NZE_FLEECE$r, line=list(color=colors[1], width=2.5,dash=smooth_NZE_FLEECE$line$dash[[1]]), name="FLEECE /iː/ NZE") %>%
add_trace(theta=smooth_Tongan_i$theta, r=smooth_Tongan_i$r, line=list(color=colors[2], width=2.5, dash=smooth_Tongan_i$line$dash[[1]]), name="/i/ Tongan") %>%
add_trace(theta=smooth_NZE_DRESS$theta, r=smooth_NZE_DRESS$r, line=list(color=colors[3], width=2.5, dash=smooth_NZE_DRESS$line$dash[[1]]), name="DRESS /e/ NZE") %>%
add_trace(theta=smooth_Tongan_e$theta, r=smooth_Tongan_e$r, line=list(color=colors[4], width=2.5, dash=smooth_Tongan_e$line$dash[[1]]), name="/e/ Tongan") %>%
add_trace(theta=smooth_NZE_START$theta, r=smooth_NZE_START$r, line=list(color=colors[5], width=2.5, dash=smooth_NZE_START$line$dash[[1]]), name="START /iː/ NZE") %>%
add_trace(theta=smooth_Tongan_a$theta, r=smooth_Tongan_a$r, line=list(color=colors[6], width=2.5, dash=smooth_Tongan_a$line$dash[[1]]), name="/a/ Tongan") %>%
add_trace(theta=smooth_NZE_THOUGHT$theta, r=smooth_NZE_THOUGHT$r, line=list(color=colors[7], width=2.5, dash=smooth_NZE_THOUGHT$line$dash[[1]]), name="THOUGHT /oː/ NZE") %>%
add_trace(theta=smooth_Tongan_o$theta, r=smooth_Tongan_o$r, line=list(color=colors[8], width=2.5, dash=smooth_Tongan_o$line$dash[[1]]), name="/o/ Tongan") %>%
layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
title="Tongan & NZE 'cardinal vowels' flanked by coronal sounds",
legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
p
if(printPDF==TRUE)
{
orca(p, "Figure10.pdf",format="pdf")
orca(p, "Figure10.jpg",format="jpeg")
}
rm(dat_NZE, dat_Tongan, NZE_FLEECE, NZE_DRESS, NZE_START, NZE_THOUGHT, smooth_NZE_FLEECE,smooth_NZE_DRESS, smooth_NZE_START, smooth_NZE_THOUGHT, smooth_Tongan_i, smooth_Tongan_e, smooth_Tongan_a, smooth_Tongan_o, maximum)---
title: "GAMMs analyses Trombone - Tongan vs English (NZE) -> paper plots"
author:
- Jalal Al-Tamimi (Newcastle University)
- Donald Derrick (University of Canterbury)
- Matthias Heyne (Boston University)
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
  html_notebook:
    number_sections: yes
    toc: yes
    toc_depth: 6
    toc_float:
      collapsed: yes
  word_document:
    toc: yes
    toc_depth: '6'
  pdf_document:
    toc: yes
    toc_depth: '6'
  html_document:
    toc: yes
    toc_depth: '6'
---

This notebook provides the plots for the article: Heyne, M., Derrick, D., and Al-Tamimi, J. (under review). "Native language influence on brass instrument performance: An application of generalized additive mixed models (GAMMs) to midsagittal ultrasound images of the tongue". Frontiers Research Topic: Models and Theories of Speech Production. Ed. Adamantios Gafos & Pascal van Lieshout.


# Loading packages

```{r warning=FALSE, message=FALSE, error=FALSE}
load_packages = c("readr","knitr","ggplot2","mgcv","itsadug","parallel","dplyr","rlist","plotly")
# dplyr, rlist, and plotly are required by the custom plotting functions
for(pkg in load_packages){
  eval(bquote(library(.(pkg))))
  if (paste0("package:", pkg) %in% search()){
    cat(paste0("Successfully loaded the ", pkg, " package.\n"))
  }else{
    install.packages(pkg)
    eval(bquote(library(.(pkg))))
    if (paste0("package:", pkg) %in% search()){
      cat(paste0("Successfully loaded the ", pkg, " package.\n"))
    }
  }
}
rm(load_packages, pkg)
Sys.setenv('MAPBOX_TOKEN' = 'sk.eyJ1IjoiZGRlcnJpY2siLCJhIjoiY2p2MW1ndnNoMXczYTRkbXd6dzRuMTQ2aCJ9.UTai4wWcFFVaJAuOrAX7VQ')

# decide whether to save plots as PDF and jpg files using the Orca command line utility
save=FALSE
printPDF=FALSE
```

## Loading custom plotting functions

### plotly_model_outputs function (Matthias Heyne, 2019)

```{r warning=FALSE, message=FALSE, error=FALSE}
# plot multiple GAM model outputs
plotly_model_outputs <- function(model, changing_cond, changing_var, constant_cond1, constant_var1, values, constant_cond2=NULL, constant_var2=NULL, print=TRUE){
  if(length(constant_var1)>1 | length(constant_var2)>1){
    print("Error: Constant variables can only have length 1.")
  }else{
    if(!is.null(constant_cond2) && !is.null(constant_var2) && length(changing_var)==2){
      # works for models Notes.gam...
      cond_p1 = capture.output(cat(paste0("list(", changing_cond, "='", changing_var[1], "', ", constant_cond1, "='", constant_var1, "', ", constant_cond2, "='", constant_var2, "')")))
      p1=plot_smooth(x=get(model), view=values, cond = eval(parse(text=cond_p1)))
      cond_p2 = capture.output(cat(paste0("list(", changing_cond, "='", changing_var[2], "', ", constant_cond1, "='", constant_var1, "', ", constant_cond2, "='", constant_var2, "')")))
      p2=plot_smooth(x=get(model), view=values, cond = eval(parse(text=cond_p2)))
      
      # set Rho max to the max of the fit + half the difference between max of the fit and the upper limit
      max_ul = max(p1$fv$ul, p2$fv$ul)
      max_fit = max(p1$fv$fit, p2$fv$fit)
      maximum=max_fit+((max_ul-max_fit)/2)
      
      # plot in polar coordinates
      p=plot_ly(type='scatterpolar', mode='lines') %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=changing_var[1]) %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=changing_var[2]) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
        layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
                          angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), 
               title=paste0("GAM smooths @", constant_var1, " & ", constant_var2), 
               legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
      p
    }else if(is.null(constant_cond2) && is.null(constant_var2) && length(changing_var)==2){
      # no specific case yet
      cond_p1 = capture.output(cat(paste0("list(", changing_cond, "='", changing_var[1], "', ", constant_cond1, "='", constant_var1, "')")))
      p1=plot_smooth(x=get(model), view=values, cond = eval(parse(text=cond_p1)))
      cond_p2 = capture.output(cat(paste0("list(", changing_cond, "='", changing_var[2], "', ", constant_cond1, "='", constant_var1, "')")))
      p2=plot_smooth(x=get(model), view=values, cond = eval(parse(text=cond_p2)))
      
      # set Rho max to the max of the fit + half the difference between max of the fit and the upper limit
      max_ul = max(p1$fv$ul, p2$fv$ul)
      max_fit = max(p1$fv$fit, p2$fv$fit)
      maximum=max_fit+((max_ul-max_fit)/2)
      
      # plot in polar coordinates
      p=plot_ly(type='scatterpolar', mode='lines') %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=changing_var[1]) %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=changing_var[2]) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
        layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
                          angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), 
               title=paste0("GAM smooths @", constant_var1, " & ", constant_var2), 
               legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
      p
    }else if(is.null(constant_cond2) && is.null(constant_var2) && length(changing_var)>2){
      # works for models NZE.gam... or Tongan.gam...
      for (i in 1:length(changing_var)){
        if(changing_cond == constant_cond1){
          cond_p1 = capture.output(cat(paste0("list(", constant_cond1, "='", constant_var1, "')")))
          p1=plot_smooth(x=get(model), view=values, cond = eval(parse(text=cond_p1)))
          # exception for KIT (='\\\\') when not using IPA symbols
          if (changing_var[i]!="\\\\"){
            cond_p2 = capture.output(cat(paste0("list(", changing_cond, "='", changing_var[i], "')")))
            p2=plot_smooth(x=get(model), view=values, cond = eval(parse(text=cond_p2)))
          }else{
            p2=plot_smooth(x=get(model), view=values, cond = list(token.ord='\\\\'))
          }
          
          # set Rho max to the max of the fit + half the difference between max of the fit and the upper limit
          max_ul = max(p1$fv$ul, p2$fv$ul)
          max_fit = max(p1$fv$fit, p2$fv$fit)
          maximum=max_fit+((max_ul-max_fit)/2)

          # plot in polar coordinates
          p=plot_ly(type='scatterpolar', mode='lines') %>% 
            add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=constant_var1) %>% 
            add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
            add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
            add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=changing_var[i]) %>% 
            add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>% 
            add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
            layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
                              angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), 
                   title=paste0("GAM smooths ",constant_var1," vs ", changing_var[i]), 
                   legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
          Sys.sleep(0)
          print(p)
        }
      }
    }
  }
}
```

### plotly_sig_diff_notes function (Matthias Heyne, 2019) -> with updated legend position

```{r warning=FALSE, message=FALSE, error=FALSE}
# plot smooths with shading to indicate significant differences (Matthias Heyne, 2019)
plotly_smooths_w_sig_diff <- function(model, condition, var1, var2, values, language, fileName, print=TRUE, save=TRUE){
  # specify path for saving plots
  # plot_folder = "saved_plots"
  # Sys.setenv(MAPBOX_TOKEN = 11122223333444)
  # get intervals of significant differences by running plot_diff
  # unfortunately setting plot=FALSE doesn't work as intervals of significant difference are not displayed!
  # hardcoded condition
  # output = capture.output(plot_diff(get(model), view=values, 
  #                                   comp=list(tokenPooled.ord=c(var1, var2))))
  # output = capture.output(plot_diff(get(model), view=values, comp=list(langNoteInt.ord=c(paste0("Tongan.", note, ".", intensity), paste0("NZE.", note, ".", intensity)))))
  names_smooths=list()
  if (condition=="tokenPooled.ord" && length(language)==1){
    output_comp = capture.output(cat(paste0("list(", condition, "=c(var1, var2))")))
    names_smooths[1]=var1
    names_smooths[2]=var2
    plot_title = paste0("GAM smooths ", language, " ", var1, " vs ", var2)
    # plot_filename = paste0(language, "_", var1, "_vs_", var2, "_from_", model)
  }else if (condition=="langNoteInt.ord" && length(language)==2){
    output_comp = capture.output(cat(paste0("list(", condition, "=c('", language[1], ".", var1, ".", var2, 
                                            "', '", language[2], ".", var1, ".", var2, "'))")))
    names_smooths[1]=paste0(language[1], ".", var1, ".", var2)
    names_smooths[2]=paste0(language[2], ".", var1, ".", var2)
    plot_title = paste0("GAM smooths ", language[1], ".", var1, ".", var2, " vs ", language[2], ".", var1, ".", var2)
    # plot_filename = paste0(language[1], ".", var1, ".", var2, "_vs_", language[2], ".", var1, ".", var2, "_from_", model)
  }else if (condition=="langNoteInt.ord" && length(language)==1 && length(var1)==2){
    output_comp = capture.output(cat(paste0("list(", condition, "=c('", language, ".", var1[1], ".", var2, 
                                            "', '", language, ".", var1[2], ".", var2, "'))")))
    names_smooths[1]=paste0(language, ".", var1[1], ".", var2)
    names_smooths[2]=paste0(language, ".", var1[2], ".", var2)
    plot_title = paste0("GAM smooths ", language, ".", var1[1], ".", var2, " vs ", language, ".", var1[2], ".", var2)
    # plot_filename = paste0(language, ".", var1[1], ".", var2, "_vs_", language, ".", var1[2], ".", var2, "_from_", model)
  }
  # output_comp = capture.output(cat(paste0("list(", condition, "=c(var1, var2))")))
  output = capture.output(plot_diff(get(model), view=values, comp=eval(parse(text=output_comp))))
  # no significant difference
  if ((length(language)==1 && length(var1)==1 && length(output)==7) | (length(language)==2 && length(output)==6) | 
    (length(language)==1 && length(var1)==2 && length(output)==6)){
    if (length(var1)==1){
      cat(paste0("Smooths for ", var1, " & ", var2, " are not significantly different.\n"))
      dat1 = NA
      assign(paste0("int_sig_diff_", var1, "_", var2), dat1, envir = .GlobalEnv)
    }else{
      cat(paste0("Smooths for ", var1[1], " & ", var1[2], " in ", language, " are not significantly different.\n"))
      dat1 = NA
      assign(paste0("int_sig_diff_", var1[1], "_", var1[2], "_", var2, "_", language), dat1, envir = .GlobalEnv)
    }
    rm(dat1, output)
    # run plot_smooth to grab data for polar plots
    if (condition=="tokenPooled.ord" && length(language)==1){
      cond_p1 = capture.output(cat(paste0("list(", condition, "=var1)")))
      cond_p2 = capture.output(cat(paste0("list(", condition, "=var2)")))
    }else if (condition=="langNoteInt.ord" && length(language)==2){
      cond_p1 = capture.output(cat(paste0("list(", condition, "='", language[1], ".", var1, ".", var2, 
                                          "', ", condition, "='", language[2], ".", var1, ".", var2, "')")))
      cond_p2 = capture.output(cat(paste0("list(", condition, "='", language[2], ".", var1, ".", var2, 
                                          "', ", condition, "='", language[1], ".", var1, ".", var2, "')")))
    }else if (condition=="langNoteInt.ord" && length(language)==1 && length(var1)==2){
      cond_p1 = capture.output(cat(paste0("list(", condition, "='", language, ".", var1[1], ".", var2, 
                                          "', ", condition, "='", language, ".", var1[2], ".", var2, "')")))
      cond_p2 = capture.output(cat(paste0("list(", condition, "='", language, ".", var1[2], ".", var2, 
                                          "', ", condition, "='", language, ".", var1[1], ".", var2, "')")))
    }
    p1 = plot_smooth(x=get(model), view=values, cond=eval(parse(text=cond_p1)), rm.ranef=TRUE)
    p2 = plot_smooth(x=get(model), view=values, cond=eval(parse(text=cond_p2)), rm.ranef=TRUE)
    # # old - hardcoded
    # p1 = plot_smooth(x=get(model), view=values, cond=list(tokenPooled.ord=var1, tokenPooled.ord=var2))
    # p2 = plot_smooth(x=get(model), view=values, cond=list(tokenPooled.ord=var2, tokenPooled.ord=var1))
    # set Rho max to the max of the fit + half the difference between max of the fit and the upper limit
    maximum=max(p1$fv$fit, p2$fv$fit)+((max(p1$fv$ul, p2$fv$ul)-max(p1$fv$fit, p2$fv$fit))/2)
    # plot in polar coordinates
    p=plot_ly(type='scatterpolar', mode='lines') %>% 
      add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=names_smooths[1]) %>% 
      add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
      add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash",  width=0.5), showlegend=FALSE) %>% 
      add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=names_smooths[2]) %>% 
      add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>% 
      add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
      layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
                        angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), 
             title=paste0("GAM smooths ", language, " ", var1, " vs ", var2), 
             legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
    Sys.sleep(0)
    print(p)
  # there are differences...
  }else{
    # grab intervals of significant differences from output
    if (length(language)==1 && length(var1)==1 && length(output)>=8){
    # if (length(language)==1 && length(output)>=8){
      sig_diff1 = c(as.double(unlist(strsplit(unlist(strsplit(output[8], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[8], " "))[3]))
    }else if ((length(language)==2 && length(output)>=7) | (length(language)==1 && length(var1)==2 && length(output)>=7)){
    # }else if (length(language)==2 && length(output)>=7){
      sig_diff1 = c(as.double(unlist(strsplit(unlist(strsplit(output[7], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[7], " "))[3]))
    }
    if (length(language)==1 && length(var1)==1 && length(output)>=9){
    # if (length(language)==1 && length(output)>=9){
      sig_diff2 = c(as.double(unlist(strsplit(unlist(strsplit(output[9], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[9], " "))[3]))
    }else if ((length(language)==2 && length(output)>=8) | (length(language)==1 && length(var1)==2 && length(output)>=8)){
    # }else if (length(language)==2 && length(output)>=8){
      sig_diff2 = c(as.double(unlist(strsplit(unlist(strsplit(output[8], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[8], " "))[3]))
    }
    if (length(language)==1 && length(var1)==1 && length(output)>=10){
    # if (length(language)==1 && length(output)>=10){
      sig_diff3 = c(as.double(unlist(strsplit(unlist(strsplit(output[10], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[10], " "))[3]))
    }else if ((length(language)==2 && length(output)>=9) | (length(language)==1 && length(var1)==2 && length(output)>=9)){
    # }else if (length(language)==2 && length(output)>=9){
      sig_diff3 = c(as.double(unlist(strsplit(unlist(strsplit(output[9], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[9], " "))[3]))
    }
    if (length(language)==1 && length(var1)==1 && length(output)>=11){
    # if (length(language)==1 && length(output)>=11){
      sig_diff4 = c(as.double(unlist(strsplit(unlist(strsplit(output[11], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[11], " "))[3]))
    }else if ((length(language)==2 && length(output)>=10) | (length(language)==1 && length(var1)==2 && length(output)>=10)){
    # }else if (length(language)==2 && length(output)>=10){
      sig_diff4 = c(as.double(unlist(strsplit(unlist(strsplit(output[10], " "))[1], "\t"))[2]), as.double(unlist(strsplit(output[10], " "))[3]))
    }

    
    # write intervals of significant difference to variable
    if ((length(language)==1 && length(var1)==1 && length(output)>=11) | ((length(language)==2 && length(output)>=10)) | 
        (length(language)==1 && length(var1)==2 && length(output)>=10)){
    # if ((length(language)==1 && length(output)>=11) | (length(language)==2 && length(output)>=10)){
      dat1 = c(sig_diff1, sig_diff2, sig_diff3, sig_diff4)
    }else if ((length(language)==1 && length(var1)==1 && length(output)>=10) | ((length(language)==2 && length(output)>=9)) | 
              (length(language)==1 && length(var1)==2 && length(output)>=9)){
    # }else if ((length(language)==1 && length(output)>=10) | (length(language)==2 && length(output)>=9)){
      dat1 = c(sig_diff1, sig_diff2, sig_diff3)
    }else if ((length(language)==1 && length(var1)==1 && length(output)>=9) | ((length(language)==2 && length(output)>=8)) | 
              (length(language)==1 && length(var1)==2 && length(output)>=8)){
    # }else if ((length(language)==1 && length(output)>=9) | (length(language)==2 && length(output)>=8)){
      dat1 = c(sig_diff1, sig_diff2)
    }else{
      dat1 = sig_diff1
    }
    # old
    # assign(paste0("int_sig_diff_", var1, "_", var2), dat1, envir = .GlobalEnv)
    if (length(var1)==1){
      assign(paste0("int_sig_diff_", var1, "_", var2), dat1, envir = .GlobalEnv)
    }else{
      assign(paste0("int_sig_diff_", var1[1], "_", var1[2], "_", var2, "_", language), dat1, envir = .GlobalEnv)
    }
    rm(dat1, output)
    # run plot_smooth to grab data for polar plots
    if (condition=="tokenPooled.ord" && length(language)==1){
      cond_p1 = capture.output(cat(paste0("list(", condition, "=var1)")))
      cond_p2 = capture.output(cat(paste0("list(", condition, "=var2)")))
    }else if (condition=="langNoteInt.ord" && length(language)==2){
      cond_p1 = capture.output(cat(paste0("list(", condition, "='", language[1], ".", var1, ".", var2, 
                                "', ", condition, "='", language[2], ".", var1, ".", var2, "')")))
      cond_p2 = capture.output(cat(paste0("list(", condition, "='", language[2], ".", var1, ".", var2, 
                                          "', ", condition, "='", language[1], ".", var1, ".", var2, "')")))
    }else if (condition=="langNoteInt.ord" && length(language)==1 && length(var1)==2){
      cond_p1 = capture.output(cat(paste0("list(", condition, "='", language, ".", var1[1], ".", var2, 
                                          "', ", condition, "='", language, ".", var1[2], ".", var2, "')")))
      cond_p2 = capture.output(cat(paste0("list(", condition, "='", language, ".", var1[2], ".", var2, 
                                          "', ", condition, "='", language, ".", var1[1], ".", var2, "')")))
    }
    p1 = plot_smooth(x=get(model), view=values, cond=eval(parse(text=cond_p1)), rm.ranef=TRUE)
    p2 = plot_smooth(x=get(model), view=values, cond=eval(parse(text=cond_p2)), rm.ranef=TRUE)
    # set Rho max to the max of the fit + half the difference between max of the fit and the upper limit
    maximum=max(p1$fv$fit, p2$fv$fit)+((max(p1$fv$ul, p2$fv$ul)-max(p1$fv$fit, p2$fv$fit))/2)
    # plot in polar coordinates
    if (exists("sig_diff4")){
      p=plot_ly(type='scatterpolar', mode='lines') %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=names_smooths[1]) %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=names_smooths[2]) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
        add_trace(theta=seq(sig_diff1[1]*180/pi, sig_diff1[2]*180/pi, length.out=20), 
                  r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
        add_trace(theta=seq(sig_diff2[1]*180/pi, sig_diff2[2]*180/pi, length.out=20), 
                  r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
        add_trace(theta=seq(sig_diff3[1]*180/pi, sig_diff3[2]*180/pi, length.out=20), 
                  r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
        add_trace(theta=seq(sig_diff4[1]*180/pi, sig_diff4[2]*180/pi, length.out=20), 
                  r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
        layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
                          angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), 
               title=plot_title, legend=list(orientation="h", xanchor="center", x=0.5, y=0.012))
      Sys.sleep(0)
      print(p)
    }else if (exists("sig_diff3")){
      p=plot_ly(type='scatterpolar', mode='lines') %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=names_smooths[1]) %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=names_smooths[2]) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
        add_trace(theta=seq(sig_diff1[1]*180/pi, sig_diff1[2]*180/pi, length.out=20), 
                  r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
        add_trace(theta=seq(sig_diff2[1]*180/pi, sig_diff2[2]*180/pi, length.out=20), 
                  r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
        add_trace(theta=seq(sig_diff3[1]*180/pi, sig_diff3[2]*180/pi, length.out=20), 
                  r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
        layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
                          angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), 
               title=plot_title, legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
      Sys.sleep(0)
      print(p)
    }else if (exists("sig_diff2")){
      p=plot_ly(type='scatterpolar', mode='lines') %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=names_smooths[1]) %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=names_smooths[2]) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
        add_trace(theta=seq(sig_diff1[1]*180/pi, sig_diff1[2]*180/pi, length.out=20), 
                  r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
        add_trace(theta=seq(sig_diff2[1]*180/pi, sig_diff2[2]*180/pi, length.out=20), 
                  r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
        layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
                          angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), 
               title=plot_title, legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
      Sys.sleep(0)
      print(p)
    }else{
      p=plot_ly(type='scatterpolar', mode='lines') %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color="blue", dash="dash", width=2.5), name=names_smooths[1]) %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color="blue", dash="dash", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color="red", width=2.5), name=names_smooths[2]) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>% 
        add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color="red", dash="dot", width=0.5), showlegend=FALSE) %>%
        add_trace(theta=seq(sig_diff1[1]*180/pi, sig_diff1[2]*180/pi, length.out=20), 
                  r=c(0, rep(maximum, 18), 0), line=list(color="black", width=0.5), fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
        layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
                          angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), 
               title=plot_title, legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
      Sys.sleep(0)
      print(p)
    }
  }
  # orca should work with plotly 4.9.0 after loading the orca module before stating R and RStudio
  if(save==TRUE)
  {
    orca(p, paste(fileName,".pdf",sep=""),format="pdf")
    orca(p, paste(fileName,".pdf",sep=""),format="jpeg")
  }
}
```


# Load final models

```{r warning=FALSE, message=FALSE, error=FALSE}
# load final model for H1
Notes.gam.AR.Mod2 = readRDS("updated_models/Notes.gam.AR.Mod2.rds")

# load final model for H1b
VAR.gam.AR.Mod2 = readRDS("updated_models/VAR.gam.AR.Mod2.rds")

# load final models for H2
NZE.gam.AR.Mod2 = readRDS("updated_models/NZE.gam.AR.Mod2.rds")
Tongan.gam.AR.Mod2 = readRDS("updated_models/Tongan.gam.AR.Mod2.rds")
```

# Create paper plots

## (new) Figure 4 H1 -> predict average values for note productions NZE vs Tongan

```{r warning=FALSE, message=FALSE, error=FALSE}
# create dfNotes
df <- read.csv("GAMM_Trombone_data.csv", sep=',', stringsAsFactors = F)
# remove empty column
df$X = NULL
df$tokenPooled <- factor(df$tokenPooled)
df$subject <- factor(df$subject)
df$native_lg <- factor(df$native_lg)
# df$playing_proficiency[df$playing_proficiency == "intermediate"] <- "amateur"
df$playing_proficiency <- factor(df$playing_proficiency, levels = c("amateur","intermediate","semi-professional","professional"))
df$block <- factor(df$block)
df$point <- as.numeric(df$point)
df$note_intensity <- factor(df$note_intensity, levels = c("piano","mezzopiano","mezzoforte","forte"))
# remove fortissimo tokens
df = df[!(is.na(df$note_intensity) & df$activity=="music"),]
dfNotes <- subset(df,df$tokenPooled == "Bb2"|
                    df$tokenPooled == "Bb3"|
                    df$tokenPooled == "D4"|
                    df$tokenPooled == "F3"|
                    df$tokenPooled == "F4")
dfNotes$tokenPooled <- factor(dfNotes$tokenPooled, levels = c("Bb2","F3","Bb3","D4","F4"))
dfNotes <- subset(dfNotes,dfNotes$note_intensity == "forte"|
                    dfNotes$note_intensity == "mezzoforte"|
                    dfNotes$note_intensity == "mezzopiano"|
                    dfNotes$note_intensity == "piano")
dfNotes$note_intensity <- factor(dfNotes$note_intensity, levels = c("piano","mezzopiano","mezzoforte","forte"))
dfNotes$noteIntenInt <- interaction(dfNotes$tokenPooled, dfNotes$note_intensity)
dfNotes$langNoteInt <- interaction(dfNotes$native_lg, dfNotes$tokenPooled, dfNotes$note_intensity)
dfNotes$native_lg.ord <- as.ordered(dfNotes$native_lg)
contrasts(dfNotes$native_lg.ord) <- "contr.treatment"
dfNotes$tokenPooled.ord <- as.ordered(dfNotes$tokenPooled)
contrasts(dfNotes$tokenPooled.ord) <- "contr.treatment"
dfNotes$note_intensity.ord <- as.ordered(dfNotes$note_intensity)
contrasts(dfNotes$note_intensity.ord) <- "contr.treatment"
dfNotes$langNoteInt.ord <- as.ordered(dfNotes$langNoteInt)
contrasts(dfNotes$langNoteInt.ord) <- "contr.treatment"
dfNotes$start <- dfNotes$points==1

# make copy of dfNotes
dat1 = dfNotes
dat1$predicted_values = predict(Notes.gam.AR.Mod2)

# plot in polar coordinates using plotly
dat1_NZE = dat1[dat1$native_lg == "NZE",]
dat1_Tongan = dat1[dat1$native_lg == "Tongan",]

# estimate smooths using R's generic predict.smooth.spline function
smooth_NZE=list(theta=seq(min(dat1_NZE$theta_uncut_z)*180/pi, max(dat1_NZE$theta_uncut_z)*180/pi, length=100), r=predict(smooth.spline(dat1_NZE$theta_uncut_z, dat1_NZE$predicted_values),
 seq(min(dat1_NZE$theta_uncut_z), max(dat1_NZE$theta_uncut_z), length=100))$y, line=list(color="blue", dash="dash"))

smooth_Tongan=list(theta=seq(min(dat1_Tongan$theta_uncut_z)*180/pi, max(dat1_Tongan$theta_uncut_z)*180/pi, length=100), r=predict(smooth.spline(dat1_Tongan$theta_uncut_z, dat1_Tongan$predicted_values),
 seq(min(dat1_Tongan$theta_uncut_z), max(dat1_Tongan$theta_uncut_z), length=100))$y, line=list(color="red", dash=""))

# set Rho max to the max of the predicted_values + 5
max_predictions = max(max(smooth_NZE$r), max(smooth_Tongan$r))
maximum=max_predictions+5
rm(max_predictions)

p = plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=smooth_NZE$theta, r=smooth_NZE$r, line=list(color=smooth_NZE$line$color[[1]], width=2.5, dash=smooth_NZE$line$dash[[1]]), name="overall average of NZE notes") %>%
  add_trace(theta=smooth_Tongan$theta, r=smooth_Tongan$r, line=list(color=smooth_Tongan$line$color[[1]], width=2.5, dash=smooth_Tongan$line$dash[[1]]), name="overall average of Tongan notes") %>%
  layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)), angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), title="Overall average smooths for NZE vs Tongan note productions", legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
p
if(save==TRUE){
  orca(p, "Figure4.pdf",format="pdf")
  orca(p, "Figure4.jpg",format="jpeg")
}
rm(dat1, dat1_NZE, dat1_Tongan, maximum, smooth_NZE, smooth_Tongan)
```


## Figure 5 (previously 4) -> H1

### Bb2 forte

```{r warning=FALSE, message=FALSE, error=FALSE, fig.width=8, fig.height=5}
plotly_smooths_w_sig_diff(model="Notes.gam.AR.Mod2", condition="langNoteInt.ord", var1="Bb2", 
                          var2="forte", values="theta_uncut_z", 
                          language=c("NZE","Tongan"), fileName = "Figure5(a)", save=printPDF)
```

### F3 mezzoforte

```{r warning=FALSE, message=FALSE, error=FALSE, fig.width=8, fig.height=5}
plotly_smooths_w_sig_diff(model="Notes.gam.AR.Mod2", condition="langNoteInt.ord", var1="F3", 
                          var2="mezzoforte", values="theta_uncut_z",
                          language=c("NZE","Tongan"), ,fileName = "Figure5(b)",save=printPDF)
```

### Bb3 mezzoforte

```{r warning=FALSE, message=FALSE, error=FALSE, fig.width=8, fig.height=5}
plotly_smooths_w_sig_diff(model="Notes.gam.AR.Mod2", condition="langNoteInt.ord", var1="Bb3", 
                          var2="mezzoforte", values="theta_uncut_z", 
                          language=c("NZE","Tongan"),fileName = "Figure5(c)",save=printPDF)

```

### Intervals of significant difference 16 out of 19 comparisons

```{r warning=FALSE, message=FALSE, error=FALSE, fig.width=8, fig.height=5}
# read .csv file
sig_diffs = read.csv(file = "Notes.gam.AR.Mod2_intervals_of_significant_differences.csv", 
                     header=TRUE, row.names=1)

# Got rid of this plot!
# # visualize areas of overall difference
# p1=plot_ly(type='scatterpolar', mode='lines') %>%
#   add_trace(theta=seq(sig_diffs["17_out_of_19",1]*180/pi, sig_diffs["17_out_of_19",2]*180/pi, 
#                       length.out=20), r=c(0, rep(320, 18), 0), line=list(color="black", width=0.5), 
#             fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
#   add_trace(theta=seq(sig_diffs["17_out_of_19",3]*180/pi, sig_diffs["17_out_of_19",4]*180/pi, 
#                       length.out=20), r=c(0, rep(320, 18), 0), line=list(color="black", width=0.5), 
#             fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>% 
#   layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,320)),
#                         angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), 
#          title=paste0("Intervals of significant differences for 17 out of 19 comparisons"), 
#          legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
# Sys.sleep(0)
# print(p1)

p2=plot_ly(type='scatterpolar', mode='lines') %>%
  add_trace(theta=seq(sig_diffs["16_out_of_19",1]*180/pi, sig_diffs["16_out_of_19",2]*180/pi, 
                      length.out=20), r=c(0, rep(320, 18), 0), line=list(color="black", width=0.5), 
            fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>%
  add_trace(theta=seq(sig_diffs["16_out_of_19",3]*180/pi, sig_diffs["16_out_of_19",4]*180/pi, 
                      length.out=20), r=c(0, rep(320, 18), 0), line=list(color="black", width=0.5), 
            fill="toself", fillcolor=rgb(0,0,0,max=255,alpha=25), showlegend=FALSE) %>% 
  layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,320)),
                        angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), 
         title=paste0("Intervals of significant differences for 16 out of 19 comparisons"), 
         legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
Sys.sleep(0)
print(p2)
if(printPDF==TRUE)
{
  orca(p2, "Figure5(d).pdf",format="pdf")
  orca(p2, "Figure5(d).jpg",format="jpeg")
}
rm(sig_diffs, p1, p2)
```


## Figure 6 -> H1b

```{r}
# df already loaded above

dfSummary <- group_by(df, subject, playing_proficiency, activity, native_lg, tokenPooled, theta_uncut_z_group = cut(theta_uncut_z,breaks=100)) %>% summarise(rhoVar = sd(rho_uncut_z,na.rm=TRUE))
dfSummary$theta_uncut_z = as.character(dfSummary$theta_uncut_z_group)
dfSummary$theta_uncut_z = gsub("\\[|\\]|\\(|\\)", "",dfSummary$theta_uncut_z)
dfSummary$theta_uncut_z = strsplit(dfSummary$theta_uncut_z,",")
dfSummary$theta_uncut_z2 = 1
for(i in c(1:nrow(dfSummary)))
{
  dfSummary$theta_uncut_z2[i] = mean(as.numeric(unlist(dfSummary$theta_uncut_z[i])))
}
dfSummary$theta_uncut_z=dfSummary$theta_uncut_z2
dfSummary$playing_proficiency.ord <- as.ordered(dfSummary$playing_proficiency)
contrasts(dfSummary$playing_proficiency.ord) <- "contr.treatment"
dfSummary$activity.ord <- as.ordered(dfSummary$activity)
contrasts(dfSummary$activity.ord) <- "contr.treatment"
dfSummary$langNoteInt <- interaction(dfSummary$native_lg, 
dfSummary$tokenPooled)
dfSummary$langNoteInt.ord <- as.ordered(dfSummary$langNoteInt)
contrasts(dfSummary$langNoteInt.ord) <- "contr.treatment"
dfSummary$native_lg.ord <- as.ordered(dfSummary$native_lg)
contrasts(dfSummary$native_lg.ord) <- "contr.treatment"
dfSummary$tokenPooled.ord <- as.ordered(dfSummary$tokenPooled)
contrasts(dfSummary$tokenPooled.ord) <- "contr.treatment"
dfSummary = na.omit(dfSummary)

#dfSummary$start <- dfSummary$theta_uncut_z==min(dfSummary$theta_uncut_z)

dfSummary$start = TRUE
for(i in unique(dfSummary$subject))
{
  dfSubject = subset(dfSummary,dfSummary$subject == i)
  for(j in unique(dfSubject$tokenPooled))
  {
    dfSummary$start[dfSummary$subject == i & dfSummary$tokenPooled == j] <- 
      dfSummary$theta_uncut_z[dfSummary$subject == i & dfSummary$tokenPooled == j] ==
      min(dfSummary$theta_uncut_z[dfSummary$subject == i & dfSummary$tokenPooled == j])
  }
}

# make copy of dfNotes
dat1 = dfSummary
dat1$predicted_values = predict(VAR.gam.AR.Mod2)

# plot in polar coordinates using plotly
dat1_NZE = dat1[dat1$native_lg == "NZE",]
dat1_Tongan = dat1[dat1$native_lg == "Tongan",]

# estimate smooths using R's generic predict.smooth.spline function
smooth_NZE=list(theta=seq(min(dat1_NZE$theta_uncut_z)*180/pi, max(dat1_NZE$theta_uncut_z)*180/pi, length=100), r=predict(smooth.spline(dat1_NZE$theta_uncut_z, dat1_NZE$predicted_values),
 seq(min(dat1_NZE$theta_uncut_z), max(dat1_NZE$theta_uncut_z), length=100))$y, line=list(color="blue", dash="dash"))

smooth_Tongan=list(theta=seq(min(dat1_Tongan$theta_uncut_z)*180/pi, max(dat1_Tongan$theta_uncut_z)*180/pi, length=100), r=predict(smooth.spline(dat1_Tongan$theta_uncut_z, dat1_Tongan$predicted_values),
 seq(min(dat1_Tongan$theta_uncut_z), max(dat1_Tongan$theta_uncut_z), length=100))$y, line=list(color="red", dash=""))

# set Rho max to the max of the predicted_values + 5
max_predictions = max(max(smooth_NZE$r), max(smooth_Tongan$r))
maximum=max_predictions+5
rm(max_predictions)

p = plot_ly(type='scatterpolar', mode='lines') %>%
add_trace(theta=smooth_NZE$theta, r=smooth_NZE$r, line=list(color=smooth_NZE$line$color[[1]], width=2.5, dash=smooth_NZE$line$dash[[1]]), name="overall average of NZE notes") %>%
  add_trace(theta=smooth_Tongan$theta, r=smooth_Tongan$r, line=list(color=smooth_Tongan$line$color[[1]], width=2.5, dash=smooth_Tongan$line$dash[[1]]), name="overall average of Tongan notes") %>%
  layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)), angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), title="Overall average smooths for NZE vs Tongan note productions", legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
p
if(printPDF==TRUE)
{
  orca(p, "Figure6.pdf",format="pdf")
  orca(p, "Figure6.jpg",format="jpeg")
}
rm(dat1, dat1_NZE, dat1_Tongan, maximum, smooth_NZE, smooth_Tongan)
```


## Figure 7 -> H2 vowels and notes NZE

### All NZE vowels

```{r warning=FALSE, message=FALSE, error=FALSE, fig.width=8, fig.height=5}
# define colors from PhD thesis
RED0 = (rgb(213,13,11, 255, maxColorValue=255))
BROWN0 = (rgb(123,73,55,255, maxColorValue=255))
PURPLE0 = (rgb(143,72,183,255, maxColorValue=255))
GRAY0 = (rgb(108,108,108,255, maxColorValue=255))
GREEN0 = (rgb(0,151,55,255, maxColorValue=255))
ORANGE0 = (rgb(255,123,0,255, maxColorValue=255))
PINK0 = (rgb(224,130,180,255, maxColorValue=255))
TEAL0 = (rgb(0,175,195,255, maxColorValue=255))
GOLD0 = (rgb(172,181,0,255, maxColorValue=255))
light_blue=rgb(0,117,220,255,maxColorValue=255)
black=rgb(25,25,25,255,maxColorValue=255)
pink=rgb(194,0,136,255,maxColorValue=255)
dark_blue=rgb(0,51,128,255,maxColorValue=255)
dark_red=rgb(153,0,0,255,maxColorValue=255)

# correct order
colors = c(ORANGE0, dark_blue, GREEN0, light_blue, PINK0, GOLD0, BROWN0, TEAL0, GRAY0, dark_red,
           RED0, PURPLE0, pink)

# run plot_smooth to grab values for fit, upper & lower bounds
p1=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="iː"), rm.ranef=TRUE)
p2=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="e"), rm.ranef=TRUE)
p3=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ʉː"), rm.ranef=TRUE)
p4=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɛ"), rm.ranef=TRUE)
p5=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɵː"), rm.ranef=TRUE)
p6=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɘ"), rm.ranef=TRUE)
p7=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ə"), rm.ranef=TRUE)
p8=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ʊ"), rm.ranef=TRUE)
p9=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ə#"), rm.ranef=TRUE)
p10=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɐ"), rm.ranef=TRUE)
p11=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɐː"), rm.ranef=TRUE)
p12=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɒ"), rm.ranef=TRUE)
p13=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="oː"), rm.ranef=TRUE)

smooth_names=c("FLEECE /iː/","DRESS /e/","GOOSE /ʉː/","TRAP /ɛ/","NURSE /ɵː/","KIT /ɘ/",
               "non-final schwa /ə/","FOOT /ʊ/","final schwa /ə#/","STRUT /ɐ/","START /ɐː/",
               "LOT /ɒ/","THOUGHT /oː/")

# # set Rho max to the max of the fit + half the difference between max of the fit and the upper limit
# max_ul = max(p1$fv$ul, p2$fv$ul, p3$fv$ul, p4$fv$ul, p5$fv$ul, p6$fv$ul, p7$fv$ul, p8$fv$ul, p9$fv$ul, p10$fv$ul, p11$fv$ul, p12$fv$ul, p13$fv$ul)
# max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit, p6$fv$fit, p7$fv$fit, p8$fv$fit, p9$fv$fit, p10$fv$fit, p11$fv$fit, p12$fv$fit, p13$fv$fit)
# maximum=max_fit+((max_ul-max_fit)/2)
# rm(max_ul, max_fit)
# 
# # plot in polar coordinates with confidence bounds
# plot1=plot_ly(type='scatterpolar', mode='lines') %>%
#   add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], dash="dash", width=2.5), name=smooth_names[1]) %>%
#   add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color=colors[1], dash="dash", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color=colors[1], dash="dash", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], width=2.5),
#             name=smooth_names[2]) %>%
#   add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color=colors[2], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color=colors[2], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], width=2.5),
#             name=smooth_names[3]) %>%
#   add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$ul, line=list(color=colors[3], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$ll, line=list(color=colors[3], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], dash="dash", width=2.5), name=smooth_names[4]) %>%
#   add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$ul, line=list(color=colors[4], dash="dash", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$ll, line=list(color=colors[4], dash="dash", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=2.5),
#             name=smooth_names[5]) %>%
#   add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$ul, line=list(color=colors[5], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$ll, line=list(color=colors[5], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$fit, line=list(color=colors[6], dash="dash", width=2.5), name=smooth_names[6]) %>%
#   add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$ul, line=list(color=colors[6], dash="dash", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$ll, line=list(color=colors[6], dash="dash", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$fit, line=list(color=colors[7], width=2.5),
#             name=smooth_names[7]) %>%
#   add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$ul, line=list(color=colors[7], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$ll, line=list(color=colors[7], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$fit, line=list(color=colors[8], dash="dash", width=2.5), name=smooth_names[8]) %>%
#   add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$ul, line=list(color=colors[8], dash="dash", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$ll, line=list(color=colors[8], dash="dash", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$fit, line=list(color=colors[9], width=2.5),
#             name=smooth_names[9]) %>%
#   add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$ul, line=list(color=colors[9], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$ll, line=list(color=colors[9], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$fit, line=list(color=colors[10], width=2.5),
#             name=smooth_names[10]) %>%
#   add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$ul, line=list(color=colors[10], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$ll, line=list(color=colors[10], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p11$fv$theta_uncut_z*180/pi, r=p11$fv$fit, line=list(color=colors[11], width=2.5),
#             name=smooth_names[11]) %>%
#   add_trace(theta=p11$fv$theta_uncut_z*180/pi, r=p11$fv$ul, line=list(color=colors[11], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p11$fv$theta_uncut_z*180/pi, r=p11$fv$ll, line=list(color=colors[11], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p12$fv$theta_uncut_z*180/pi, r=p12$fv$fit, line=list(color=colors[12], dash="dash", width=2.5), name=smooth_names[12]) %>%
#   add_trace(theta=p12$fv$theta_uncut_z*180/pi, r=p12$fv$ul, line=list(color=colors[12], dash="dash", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p12$fv$theta_uncut_z*180/pi, r=p12$fv$ll, line=list(color=colors[12], dash="dash", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p13$fv$theta_uncut_z*180/pi, r=p13$fv$fit, line=list(color=colors[13], width=2.5),
#             name=smooth_names[13]) %>%
#   add_trace(theta=p13$fv$theta_uncut_z*180/pi, r=p13$fv$ul, line=list(color=colors[13], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p13$fv$theta_uncut_z*180/pi, r=p13$fv$ll, line=list(color=colors[13], dash="dot", width=0.5), showlegend=FALSE) %>%
#   layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
#                     angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
#          title="All vowels produced by NZE speakers")
# plot1

# set Rho max to the max of the fit + 5
max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit, p6$fv$fit, p7$fv$fit, p8$fv$fit, p9$fv$fit, p10$fv$fit, p11$fv$fit, p12$fv$fit, p13$fv$fit)
maximum=max_fit+5
rm(max_fit)

# plot in polar coordinates without confidence bounds
plot2=plot_ly(type='scatterpolar', mode='lines') %>%
  add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], dash="dash", width=2.5), name=smooth_names[1]) %>%
  add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], width=2.5),
            name=smooth_names[2]) %>%
  add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], width=2.5),
            name=smooth_names[3]) %>%
  add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], dash="dash", width=2.5), name=smooth_names[4]) %>%
  add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=2.5),
            name=smooth_names[5]) %>%
  add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$fit, line=list(color=colors[6], dash="dash", width=2.5), name=smooth_names[6]) %>%
  add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$fit, line=list(color=colors[7], width=2.5),
            name=smooth_names[7]) %>%
  add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$fit, line=list(color=colors[8], dash="dash", width=2.5), name=smooth_names[8]) %>%
  add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$fit, line=list(color=colors[9], width=2.5),
            name=smooth_names[9]) %>%
  add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$fit, line=list(color=colors[10], width=2.5),
            name=smooth_names[10]) %>%
  add_trace(theta=p11$fv$theta_uncut_z*180/pi, r=p11$fv$fit, line=list(color=colors[11], width=2.5),
            name=smooth_names[11]) %>%
  add_trace(theta=p12$fv$theta_uncut_z*180/pi, r=p12$fv$fit, line=list(color=colors[12], dash="dash", width=2.5), name=smooth_names[12]) %>%
  add_trace(theta=p13$fv$theta_uncut_z*180/pi, r=p13$fv$fit, line=list(color=colors[13], width=2.5),
            name=smooth_names[13]) %>%
  layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
                    angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
         title="All vowels produced by NZE speakers",
         legend=list(orientation="h", xanchor="center", x=0.5,y=0.008))
plot2
if(printPDF==TRUE)
{
  orca(plot2, "Figure7(a).pdf",format="pdf")
  orca(plot2, "Figure7(a).jpg",format="jpeg")
}

rm(p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12,p13)
```

### All NZE notes

```{r warning=FALSE, message=FALSE, error=FALSE, fig.width=8, fig.height=5}
# correct order
colors = c(black, black, black, black, black)

p1=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb2"), rm.ranef=TRUE)
p2=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F3"), rm.ranef=TRUE)
p3=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb3"), rm.ranef=TRUE)
p4=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="D4"), rm.ranef=TRUE)
p5=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F4"), rm.ranef=TRUE)

smooth_names=c("Bb2","F3","Bb3","D4","F4")

# set Rho max to the max of the fit + 5
#max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit)
#maximum=max_fit+5
#rm(max_fit)

# plot in polar coordinates without confidence bounds
p=plot_ly(type='scatterpolar', mode='lines') %>%
  add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], width=2.5), name=smooth_names[1]) %>%
  add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], dash="dash", width=2.5), name=smooth_names[2]) %>%
  add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], dash="dot", width=2.5), name=smooth_names[3]) %>%
  add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], dash="dashdot", width=2.5), name=smooth_names[4]) %>%
  add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=1),
            name=smooth_names[5]) %>%
  layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
                    angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
         title="All notes produced by NZE speakers",
         legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
p

if(printPDF==TRUE)
{
  orca(p, "Figure7(b).pdf",format="pdf")
  orca(p, "Figure7(b).jpg",format="jpeg")
}

rm(p1,p2,p3,p4,p5)
```


## Figure 8 -> H2 vowels and notes Tongan

### All Tongan vowels

```{r warning=FALSE, message=FALSE, error=FALSE, fig.width=8, fig.height=5}
# use colors from PhD thesis (loaded above)
colors = c(ORANGE0, dark_blue, RED0, pink, GREEN0)

# run plot_smooth to grab values for fit, upper & lower bounds
p1=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="i"), rm.ranef=TRUE)
p2=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="e"), rm.ranef=TRUE)
p3=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="a"), rm.ranef=TRUE)
p4=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="o"), rm.ranef=TRUE)
p5=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="u"), rm.ranef=TRUE)
smooth_names=c("/i/","/e/","/a/","/o/","/u/")

# set Rho max to the max of the fit + half the difference between max of the fit and the upper limit
max_ul = max(p1$fv$ul, p2$fv$ul, p3$fv$ul, p4$fv$ul, p5$fv$ul)
max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit)
maximum=max_fit+((max_ul-max_fit)/2)
rm(max_ul, max_fit)

# plot in polar coordinates with confidence bounds
plot1=plot_ly(type='scatterpolar', mode='lines') %>% 
  add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], width=2.5),
            name=smooth_names[1]) %>% 
  add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color=colors[1], dash="dot", width=0.5), showlegend=FALSE) %>% 
  add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color=colors[1], dash="dot", width=0.5), showlegend=FALSE) %>% 
  add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], width=2.5), 
            name=smooth_names[2]) %>% 
  add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color=colors[2], dash="dot", width=0.5), showlegend=FALSE) %>% 
  add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color=colors[2], dash="dot", width=0.5), showlegend=FALSE) %>%
  add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], width=2.5), 
            name=smooth_names[3]) %>% 
  add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$ul, line=list(color=colors[3], dash="dot", width=0.5), showlegend=FALSE) %>% 
  add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$ll, line=list(color=colors[3], dash="dot", width=0.5), showlegend=FALSE) %>%
  add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], width=2.5), 
            name=smooth_names[4]) %>% 
  add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$ul, line=list(color=colors[4], dash="dot", width=0.5), showlegend=FALSE) %>% 
  add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$ll, line=list(color=colors[4], dash="dot", width=0.5), showlegend=FALSE) %>%
  add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=2.5), 
            name=smooth_names[5]) %>% 
  add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$ul, line=list(color=colors[5], dash="dot", width=0.5), showlegend=FALSE) %>% 
  add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$ll, line=list(color=colors[5], dash="dot", width=0.5), showlegend=FALSE) %>%
  layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
                    angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), 
         title="All vowels produced by Tongan speakers",
         legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
plot1

# # set Rho max to the max of the fit + 5
# max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit)
# maximum=max_fit+5
# rm(max_fit)
# 
# # plot in polar coordinates with confidence bounds
# plot2=plot_ly(type='scatterpolar', mode='lines') %>% 
#   add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], width=2.5),
#             name=smooth_names[1]) %>% 
#   add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], width=2.5), 
#             name=smooth_names[2]) %>% 
#   add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], width=2.5), 
#             name=smooth_names[3]) %>% 
#   add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], width=2.5), 
#             name=smooth_names[4]) %>% 
#   add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=2.5), 
#             name=smooth_names[5]) %>% 
#   layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
#                     angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), 
#          title="All vowels produced by Tongan speakers",
#          legend=list(orientation="h", xanchor="center", x=0.5))
# plot2
if(printPDF==TRUE)
{

  orca(plot1, "Figure8(a).pdf",format="pdf")
  orca(plot1, "Figure8(a).jpg",format="jpeg")
}
  
rm(p1,p2,p3,p4,p5)
```

### All Tongan notes

```{r warning=FALSE, message=FALSE, error=FALSE, fig.width=8, fig.height=5}
# use colors from PhD thesis (loaded above)
colors = c(black, black, black, black, black)

p1=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb2"), rm.ranef=TRUE)
p2=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F3"), rm.ranef=TRUE)
p3=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb3"), rm.ranef=TRUE)
p4=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="D4"), rm.ranef=TRUE)
p5=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F4"), rm.ranef=TRUE)

smooth_names=c("Bb2","F3","Bb3","D4","F4")

# plot in polar coordinates without confidence bounds
p=plot_ly(type='scatterpolar', mode='lines') %>% 
  add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], width=2.5), name=smooth_names[1]) %>% 
  add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], dash="dash", width=2.5), name=smooth_names[2]) %>% 
  add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], dash="dot", width=2.5), name=smooth_names[3]) %>% 
  add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], dash="dashdot", width=2.5), name=smooth_names[4]) %>% 
  add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=1), 
            name=smooth_names[5]) %>% 
  layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
                    angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), 
         title="All notes produced by Tongan speakers",
         legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
p

if(printPDF==TRUE)
{
  orca(p, "Figure8(b).pdf",format="pdf")
  orca(p, "Figure8(b).jpg",format="jpeg")
}
  
rm(p1,p2,p3,p4,p5)
```


## Figure 9 -> H2 selected vowels with notes

### with NZE notes

```{r warning=FALSE, message=FALSE, error=FALSE, fig.width=8, fig.height=5}
colors = c(black, black, black, black, black,BROWN0, GRAY0, PURPLE0, pink, GREEN0)

# run plot_smooth to grab values for fit, upper & lower bounds
# NZE notes
p1=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb2"), rm.ranef=TRUE)
p2=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F3"), rm.ranef=TRUE)
p3=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb3"), rm.ranef=TRUE)
p4=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="D4"), rm.ranef=TRUE)
p5=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F4"), rm.ranef=TRUE)

# vowels
p6=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ə"), rm.ranef=TRUE)
p7=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ə#"), rm.ranef=TRUE)
p8=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɒ"), rm.ranef=TRUE)
p9=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="o"), rm.ranef=TRUE)
p10=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="u"), rm.ranef=TRUE)

smooth_names=c("Bb2 NZE","F3 NZE","Bb3 NZE","D4 NZE","F4 NZE","non-final schwa /ə/ NZE","final schwa /ə#/ NZE","LOT /ɒ/ NZE","/o/ Tongan","/u/ Tongan")

# set Rho max to the max of the fit + 5
max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit, p6$fv$fit, p7$fv$fit, p8$fv$fit, p9$fv$fit, p10$fv$fit)
maximum=max_fit+5
rm(max_fit)

# plot in polar coordinates without confidence bounds
plot2=plot_ly(type='scatterpolar', mode='lines') %>%
  add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], width=2.5), name=smooth_names[1]) %>%
  add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], dash="dash", width=2.5), name=smooth_names[2]) %>%
  add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], dash="dot", width=2.5), name=smooth_names[3]) %>%
  add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], dash="dashdot", width=2.5), name=smooth_names[4]) %>%
  add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=1), name=smooth_names[5]) %>%
  add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$fit, line=list(color=colors[6], width=2.5), name=smooth_names[6]) %>%
  add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$fit, line=list(color=colors[7], width=2.5), name=smooth_names[7]) %>%
  add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$fit, line=list(color=colors[8], dash="dash", width=2.5), name=smooth_names[8]) %>%
  add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$fit, line=list(color=colors[9], width=2.5), name=smooth_names[9]) %>%
  add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$fit, line=list(color=colors[10], width=2.5),
name=smooth_names[10]) %>%
  layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
                    angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
         title="Tongan & NZE vowels closest to playing position with NZE notes",legend=list(orientation="h", xanchor="center", x=0.5,y=0.008))
plot2

if(printPDF==TRUE)
{
  orca(plot2, "Figure9(a).pdf",format="pdf")
  orca(plot2, "Figure9(a).jpeg",format="jpeg")
}

rm(p1,p2,p3,p4,p5,p6,p7,p8,p9,p10)
```

### with Tongan notes

```{r warning=FALSE, message=FALSE, error=FALSE, fig.width=8, fig.height=5}
colors = c(black, black, black, black, black,BROWN0, GRAY0, PURPLE0, pink, GREEN0)

# run plot_smooth to grab values for fit, upper & lower bounds
# Tongan notes
p1=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb2"), rm.ranef=TRUE)
p2=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F3"), rm.ranef=TRUE)
p3=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="Bb3"), rm.ranef=TRUE)
p4=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="D4"), rm.ranef=TRUE)
p5=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="F4"), rm.ranef=TRUE)

# vowels
p6=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ə"), rm.ranef=TRUE)
p7=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ə#"), rm.ranef=TRUE)
p8=plot_smooth(x=NZE.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="ɒ"), rm.ranef=TRUE)
p9=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="o"), rm.ranef=TRUE)
p10=plot_smooth(x=Tongan.gam.AR.Mod2, view="theta_uncut_z", cond=list(tokenPooled.ord="u"), rm.ranef=TRUE)

smooth_names=c("Bb2 Tongan","F3 Tongan","Bb3 Tongan","D4 Tongan","F4 Tongan","non-final schwa /ə/ NZE","final schwa /ə#/ NZE","LOT /ɒ/ NZE","/o/ Tongan","/u/ Tongan")

# # set Rho max to the max of the fit + 5
# max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit, p6$fv$fit, p7$fv$fit, p8$fv$fit, p9$fv$fit, p10$fv$fit)
# maximum=max_fit+5
# rm(max_fit)
# 
# # plot in polar coordinates with confidence bounds
# plot1=plot_ly(type='scatterpolar', mode='lines') %>%
#   add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], width=2.5), name=smooth_names[1]) %>%
#   add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ul, line=list(color=colors[1], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$ll, line=list(color=colors[1], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], width=2.5), name=smooth_names[2]) %>%
#   add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ul, line=list(color=colors[2], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$ll, line=list(color=colors[2], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], dash="dash", width=2.5), name=smooth_names[3]) %>%
#   add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$ul, line=list(color=colors[3], dash="dash", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$ll, line=list(color=colors[3], dash="dash", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], width=2.5),
#             name=smooth_names[4]) %>%
#   add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$ul, line=list(color=colors[4], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$ll, line=list(color=colors[4], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=2.5),
#             name=smooth_names[5]) %>%
#   add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$ul, line=list(color=colors[5], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$ll, line=list(color=colors[5], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$fit, line=list(color=colors[6], width=1),
#             name=smooth_names[6]) %>%
#   add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$ul, line=list(color=colors[6], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$ll, line=list(color=colors[6], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$fit, line=list(color=colors[7], dash="dashdot", width=2.5), name=smooth_names[7]) %>%
#   add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$ul, line=list(color=colors[7], dash="dashdot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$ll, line=list(color=colors[7], dash="dashdot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$fit, line=list(color=colors[8], dash="dot", width=2.5), name=smooth_names[8]) %>%
#   add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$ul, line=list(color=colors[8], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$ll, line=list(color=colors[8], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$fit, line=list(color=colors[9], dash="dash", width=2.5), name=smooth_names[9]) %>%
#   add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$ul, line=list(color=colors[9], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$ll, line=list(color=colors[9], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$fit, line=list(color=colors[10], width=2.5),
#             name=smooth_names[10]) %>%
#   add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$ul, line=list(color=colors[10], dash="dot", width=0.5), showlegend=FALSE) %>%
#   add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$ll, line=list(color=colors[10], dash="dot", width=0.5), showlegend=FALSE) %>%
#   layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
#                     angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
#          title="Tongan & NZE vowels closest to playing position with Tongan notes")
# plot1

# set Rho max to the max of the fit + 5
max_fit = max(p1$fv$fit, p2$fv$fit, p3$fv$fit, p4$fv$fit, p5$fv$fit, p6$fv$fit, p7$fv$fit, p8$fv$fit, p9$fv$fit, p10$fv$fit)
maximum=max_fit+5
rm(max_fit)

# plot in polar coordinates without confidence bounds
plot2=plot_ly(type='scatterpolar', mode='lines') %>%
  add_trace(theta=p1$fv$theta_uncut_z*180/pi, r=p1$fv$fit, line=list(color=colors[1], width=2.5), name=smooth_names[1]) %>%
  add_trace(theta=p2$fv$theta_uncut_z*180/pi, r=p2$fv$fit, line=list(color=colors[2], dash="dash", width=2.5), name=smooth_names[2]) %>%
  add_trace(theta=p3$fv$theta_uncut_z*180/pi, r=p3$fv$fit, line=list(color=colors[3], dash="dot", width=2.5), name=smooth_names[3]) %>%
  add_trace(theta=p4$fv$theta_uncut_z*180/pi, r=p4$fv$fit, line=list(color=colors[4], dash="dashdot", width=2.5), name=smooth_names[4]) %>%
  add_trace(theta=p5$fv$theta_uncut_z*180/pi, r=p5$fv$fit, line=list(color=colors[5], width=1), name=smooth_names[5]) %>%
  add_trace(theta=p6$fv$theta_uncut_z*180/pi, r=p6$fv$fit, line=list(color=colors[6], width=2.5), name=smooth_names[6]) %>%
  add_trace(theta=p7$fv$theta_uncut_z*180/pi, r=p7$fv$fit, line=list(color=colors[7], width=2.5), name=smooth_names[7]) %>%
  add_trace(theta=p8$fv$theta_uncut_z*180/pi, r=p8$fv$fit, line=list(color=colors[8], dash="dash", width=2.5), name=smooth_names[8]) %>%
  add_trace(theta=p9$fv$theta_uncut_z*180/pi, r=p9$fv$fit, line=list(color=colors[9], width=2.5), name=smooth_names[9]) %>%
  add_trace(theta=p10$fv$theta_uncut_z*180/pi, r=p10$fv$fit, line=list(color=colors[10], width=2.5),
name=smooth_names[10]) %>%
  layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),
                    angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)),
         title="Tongan & NZE vowels closest to playing position with Tongan notes",legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
plot2

if(printPDF==TRUE)
{
  orca(plot2, "Figure9(b).pdf",format="pdf")
  orca(plot2, "Figure9(b).jpeg",format="jpeg")
}
  
rm(p1,p2,p3,p4,p5,p6,p7,p8,p9,p10)
```


## Figure 10 'cardinal vowels' -> updated version using only vowels surrounded by coronals

```{r warning=FALSE, message=FALSE, error=FALSE, fig.width=8, fig.height=5}
# create dfNZE & dfTongan -> df already loaded and manipulated above
dfNZE <- subset(df,df$native_lg=="NZE")
dfNZE$tokenPooled <- factor(dfNZE$tokenPooled, levels = c("ɐː","ɐ","ɛ","ɵː","e","iː","ʉː","ʊ","oː","ɒ","ɘ","ə","ə#","Bb2","F3","Bb3","D4","F4"))
dfNZE$playing_proficiency <- as.factor(dfNZE$playing_proficiency)
dfNZE$note_intensity[is.na(dfNZE$note_intensity)] = "NULL"
dfNZE$subVowelInt <- interaction(dfNZE$subject, dfNZE$tokenPooled)
dfNZE$precSoundVowelInt <- interaction(dfNZE$speech_prec_pooled, dfNZE$tokenPooled)
dfNZE$follSoundVowelInt <- interaction(dfNZE$speech_fol_pooled, dfNZE$tokenPooled)
dfNZE$tokenPooled.ord <- as.ordered(dfNZE$tokenPooled)
contrasts(dfNZE$tokenPooled.ord) <- "contr.treatment"
dfNZE$vowels_pooled.ord <- as.ordered(dfNZE$vowels_pooled)
contrasts(dfNZE$vowels_pooled.ord) <- "contr.treatment"
dfNZE$playing_proficiency.ord <- as.ordered(dfNZE$playing_proficiency)
contrasts(dfNZE$playing_proficiency.ord) <- "contr.treatment"
dfNZE$start <- dfNZE$points==1

dfTongan <- subset(df,df$native_lg=="Tongan")
dfTongan$tokenPooled <- factor(dfTongan$tokenPooled, levels = c("aː","a","eː","e","iː","i","uː","u","oː","o","Bb2","F3","Bb3","D4","F4"))
dfTongan$tokenPooled[dfTongan$tokenPooled == "aː"] = "a"
dfTongan$tokenPooled[dfTongan$tokenPooled == "eː"] = "e"
dfTongan$tokenPooled[dfTongan$tokenPooled == "iː"] = "i"
dfTongan$tokenPooled[dfTongan$tokenPooled == "uː"] = "u"
dfTongan$tokenPooled[dfTongan$tokenPooled == "oː"] = "o"
dfTongan$tokenPooled <- factor(dfTongan$tokenPooled)
dfTongan$playing_proficiency <- as.factor(dfTongan$playing_proficiency)
dfTongan$speech_prec_pooled[is.na(dfTongan$speech_prec_pooled)] = "NULL"
dfTongan$speech_fol_pooled[is.na(dfTongan$speech_fol_pooled)] = "NULL"
dfTongan$subVowelInt <- interaction(dfTongan$subject, dfTongan$tokenPooled)
dfTongan$precSoundVowelInt <- interaction(dfTongan$speech_prec_pooled, dfTongan$tokenPooled)
dfTongan$follSoundVowelInt <- interaction(dfTongan$speech_fol_pooled, dfTongan$tokenPooled)
dfTongan$tokenPooled.ord <- as.ordered(dfTongan$tokenPooled)
contrasts(dfTongan$tokenPooled.ord) <- "contr.treatment"
dfTongan$vowels_pooled.ord <- as.ordered(dfTongan$vowels_pooled)
contrasts(dfTongan$vowels_pooled.ord) <- "contr.treatment"
dfTongan$playing_proficiency.ord <- as.ordered(dfTongan$playing_proficiency)
contrasts(dfTongan$playing_proficiency.ord) <- "contr.treatment"
dfTongan$start <- dfTongan$points==1

# # inspect vowel token numbers in different preceding and following contexts
# cat("\ndfNZE$speech_prec_pooled\n")
# table(dfNZE$speech_prec_pooled)
# cat("\ndfNZE$speech_fol_pooled\n")
# table(dfNZE$speech_fol_pooled)
# cat("\ndfTongan$speech_prec_pooled\n")
# table(dfTongan$speech_prec_pooled)
# cat("\ndfNZE$speech_fol_pooled\n")
# table(dfTongan$speech_fol_pooled)

# make copies of dfNZE and dfTongan
dat_NZE = dfNZE
dat_Tongan = dfTongan

# predict values from models
dat_NZE$predicted_values = predict(NZE.gam.AR.Mod2)
dat_Tongan$predicted_values = predict(Tongan.gam.AR.Mod2)

# create new data.frames
NZE_FLEECE = dat_NZE[dat_NZE$tokenPooled == "iː" & dat_NZE$speech_prec_pooled == "coronals" & dat_NZE$speech_fol_pooled == "coronals",]
Tongan_i = dat_Tongan[dat_Tongan$tokenPooled == "i" & dat_Tongan$speech_prec_pooled == "coronals" & dat_Tongan$speech_fol_pooled == "coronals",]

NZE_DRESS = dat_NZE[dat_NZE$tokenPooled == "e" & dat_NZE$speech_prec_pooled == "coronals" & dat_NZE$speech_fol_pooled == "coronals",]
Tongan_e = dat_Tongan[dat_Tongan$tokenPooled == "e" & dat_Tongan$speech_prec_pooled == "coronals" & dat_Tongan$speech_fol_pooled == "coronals",]

NZE_START = dat_NZE[dat_NZE$tokenPooled == "ɐː" & dat_NZE$speech_prec_pooled == "coronals" & dat_NZE$speech_fol_pooled == "coronals",]
Tongan_a = dat_Tongan[dat_Tongan$tokenPooled == "a" & dat_Tongan$speech_prec_pooled == "coronals" & dat_Tongan$speech_fol_pooled == "coronals",]

NZE_THOUGHT = dat_NZE[dat_NZE$tokenPooled == "oː" & dat_NZE$speech_prec_pooled == "coronals" & dat_NZE$speech_fol_pooled == "coronals",]
Tongan_o = dat_Tongan[dat_Tongan$tokenPooled == "o" & dat_Tongan$speech_prec_pooled == "coronals" & dat_Tongan$speech_fol_pooled == "coronals",]

# estimate smooths using R's generic predict.smooth.spline function
smooth_NZE_FLEECE=list(theta=seq(min(NZE_FLEECE$theta_uncut_z)*180/pi, 
                                 max(NZE_FLEECE$theta_uncut_z)*180/pi, length=100), 
                       r=predict(smooth.spline(NZE_FLEECE$theta_uncut_z, NZE_FLEECE$predicted_values), 
                                 seq(min(NZE_FLEECE$theta_uncut_z), max(NZE_FLEECE$theta_uncut_z), 
                                     length=100))$y, line=list(color="ORANGE0", dash="dash"))
smooth_Tongan_i=list(theta=seq(min(Tongan_i$theta_uncut_z)*180/pi, 
                               max(Tongan_i$theta_uncut_z)*180/pi, length=100), 
                     r=predict(smooth.spline(Tongan_i$theta_uncut_z, Tongan_i$predicted_values), 
                               seq(min(Tongan_i$theta_uncut_z), max(Tongan_i$theta_uncut_z), 
                                   length=100))$y, line=list(color="ORANGE0", dash=""))

smooth_NZE_DRESS=list(theta=seq(min(NZE_DRESS$theta_uncut_z)*180/pi, 
                                 max(NZE_DRESS$theta_uncut_z)*180/pi, length=100), 
                       r=predict(smooth.spline(NZE_DRESS$theta_uncut_z, NZE_DRESS$predicted_values), 
                                 seq(min(NZE_DRESS$theta_uncut_z), max(NZE_DRESS$theta_uncut_z), 
                                     length=100))$y, line=list(color="dark_blue", dash="dash"))
smooth_Tongan_e=list(theta=seq(min(Tongan_e$theta_uncut_z)*180/pi, 
                               max(Tongan_e$theta_uncut_z)*180/pi, length=100), 
                     r=predict(smooth.spline(Tongan_e$theta_uncut_z, Tongan_e$predicted_values), 
                               seq(min(Tongan_e$theta_uncut_z), max(Tongan_e$theta_uncut_z), 
                                   length=100))$y, line=list(color="dark_blue", dash=""))

smooth_NZE_START=list(theta=seq(min(NZE_START$theta_uncut_z)*180/pi, 
                                 max(NZE_START$theta_uncut_z)*180/pi, length=100), 
                       r=predict(smooth.spline(NZE_START$theta_uncut_z, NZE_START$predicted_values), 
                                 seq(min(NZE_START$theta_uncut_z), max(NZE_START$theta_uncut_z), 
                                     length=100))$y, line=list(color="RED0", dash="dash"))
smooth_Tongan_a=list(theta=seq(min(Tongan_a$theta_uncut_z)*180/pi, 
                               max(Tongan_a$theta_uncut_z)*180/pi, length=100), 
                     r=predict(smooth.spline(Tongan_a$theta_uncut_z, Tongan_a$predicted_values), 
                               seq(min(Tongan_a$theta_uncut_z), max(Tongan_a$theta_uncut_z), 
                                   length=100))$y, line=list(color="RED0", dash=""))

smooth_NZE_THOUGHT=list(theta=seq(min(NZE_THOUGHT$theta_uncut_z)*180/pi, 
                                 max(NZE_THOUGHT$theta_uncut_z)*180/pi, length=100), 
                       r=predict(smooth.spline(NZE_THOUGHT$theta_uncut_z, 
                                               NZE_THOUGHT$predicted_values), 
                                 seq(min(NZE_THOUGHT$theta_uncut_z), max(NZE_THOUGHT$theta_uncut_z), 
                                     length=100))$y, line=list(color="pink", dash="dash"))
smooth_Tongan_o=list(theta=seq(min(Tongan_o$theta_uncut_z)*180/pi, 
                               max(Tongan_o$theta_uncut_z)*180/pi, length=100), 
                     r=predict(smooth.spline(Tongan_o$theta_uncut_z, Tongan_o$predicted_values), 
                               seq(min(Tongan_o$theta_uncut_z), max(Tongan_o$theta_uncut_z), 
                                   length=100))$y, line=list(color="pink", dash=""))

# set Rho max to the max of the predicted_values + 5
max_predictions = max(max(smooth_NZE_FLEECE$r), max(smooth_NZE_DRESS$r), max(smooth_NZE_START$r), 
                      max(smooth_NZE_THOUGHT$r), max(smooth_Tongan_i$r), max(smooth_Tongan_e$r), 
                      max(smooth_Tongan_a$r), max(smooth_Tongan_o$r))
maximum=max_predictions+5
rm(max_predictions)

# colors from smooths don't get passed on correctly -> specify again
colors = c(ORANGE0, ORANGE0, dark_blue, dark_blue, RED0, RED0, pink, pink)

p = plot_ly(type='scatterpolar', mode='lines') %>% 
  add_trace(theta=smooth_NZE_FLEECE$theta, r=smooth_NZE_FLEECE$r, line=list(color=colors[1], width=2.5,dash=smooth_NZE_FLEECE$line$dash[[1]]), name="FLEECE /iː/ NZE") %>% 
  add_trace(theta=smooth_Tongan_i$theta, r=smooth_Tongan_i$r, line=list(color=colors[2], width=2.5, dash=smooth_Tongan_i$line$dash[[1]]), name="/i/ Tongan") %>%
  add_trace(theta=smooth_NZE_DRESS$theta, r=smooth_NZE_DRESS$r, line=list(color=colors[3], width=2.5, dash=smooth_NZE_DRESS$line$dash[[1]]), name="DRESS /e/ NZE") %>% 
  add_trace(theta=smooth_Tongan_e$theta, r=smooth_Tongan_e$r, line=list(color=colors[4], width=2.5, dash=smooth_Tongan_e$line$dash[[1]]), name="/e/ Tongan") %>%
  add_trace(theta=smooth_NZE_START$theta, r=smooth_NZE_START$r, line=list(color=colors[5], width=2.5, dash=smooth_NZE_START$line$dash[[1]]), name="START /iː/ NZE") %>% 
  add_trace(theta=smooth_Tongan_a$theta, r=smooth_Tongan_a$r, line=list(color=colors[6], width=2.5, dash=smooth_Tongan_a$line$dash[[1]]), name="/a/ Tongan") %>%
  add_trace(theta=smooth_NZE_THOUGHT$theta, r=smooth_NZE_THOUGHT$r, line=list(color=colors[7], width=2.5, dash=smooth_NZE_THOUGHT$line$dash[[1]]), name="THOUGHT /oː/ NZE") %>% 
  add_trace(theta=smooth_Tongan_o$theta, r=smooth_Tongan_o$r, line=list(color=colors[8], width=2.5, dash=smooth_Tongan_o$line$dash[[1]]), name="/o/ Tongan") %>%
  layout(polar=list(sector=c(20,160), radialaxis=list(angle=90, range=c(0,maximum)),  
                    angularaxis=list(thetaunit='radians', direction="clockwise", rotation=0)), 
         title="Tongan & NZE 'cardinal vowels' flanked by coronal sounds", 
         legend=list(orientation="h", xanchor="center", x=0.5,y=0.012))
p

if(printPDF==TRUE)
{
  orca(p, "Figure10.pdf",format="pdf")
  orca(p, "Figure10.jpg",format="jpeg")
}

rm(dat_NZE, dat_Tongan, NZE_FLEECE, NZE_DRESS, NZE_START, NZE_THOUGHT, smooth_NZE_FLEECE,smooth_NZE_DRESS, smooth_NZE_START, smooth_NZE_THOUGHT, smooth_Tongan_i, smooth_Tongan_e, smooth_Tongan_a, smooth_Tongan_o, maximum)
```
