statisticalAnalyses.Rmd

---
title: "Dundun Modeling"
output: html_notebook
author: Lauren K. Fink, with input from Pauline Larrouy-Maestri
contact: lauren.fink@ae.mpg.de
---

This notebook contains code to model data from the dundun project and generate figures. 
It produces all perception-related figures from the paper (i.e., heatmap, confidence histograms, familiarity boxplot, logistic regression model), as well as the correlations among acoustic features (supplemental information). Please note that Tina Roeske analyzed all acoustic features (Figs. 1, 2, 3) in MATLAB. 

## Load required packages 
```{r message=FALSE}
library(lme4)
library(lmerTest)
library(car)
library(sjPlot)
library(r2glmm)
library(sjmisc)
library(sjlabelled)
library(effsize)
library(ggplot2)
library(nortest)
library(dplyr)
library(tidyverse)
library(readxl)
library(nortest)
library(PerformanceAnalytics)
library(MuMIn)
library(caret)
library(magick)
library(PEIP)
library(gplots)
library(heatmap.plus)
library(gridExtra)
```

# Load required data
NOTE: USER TODO: set files paths appropriately on your machine
Load participant and stimulus data
```{r}
# STIMULUS DATA
# mean values for each stimulus
smeans = read.csv("~/Documents/Projects/dundun/Data/dundun_acoustFeat_means_absDiffs.csv") # all acoustic features from Tina
colnames(smeans)[1] = 'Stimulus'  # fix this so it matches other tables
pc = read.csv("~/Documents/Projects/dundun/Data/pulseClarity.csv") # pulse clarity values 
# combine pulse clarity info with smeans
smeans$pulseClarity = pc$pulseClarity

# PARTICIPANT DATA
# Cecilia now put all behavioral data in one table
# Let's read that in and then recreate the tables we had formerly been using
pdata = read_excel("~/Documents/Projects/dundun/Data/dundun_allTrials.xlsx") # read in all data
pall = pdata[,1:31] # speech/music rating for every trial
pconf = pdata[,c(1,32:61)] # confidence for every trial
pinfo = pdata[,c(1,62:70)] # general background info about participants
pinfo$INST = pinfo$PAST.INST + pinfo$CURRENT.INST # sum current and past instrument playing to get one measure of instrument playing 
pmeans = pdata[,c(1,71:81)] # mean hit rates, etc. for each participant

# OUTPUT FILE PATHS
# Set output path for figures
outpath = "~/Documents/Projects/dundun/Figures/"
op = 0 #flag whether to output plots to file (1) or plot them in line

```

# Analyze differences between acoustic features
```{r}
# t-tests for acoustic features
i = 2
for (col in smeans[,-1]){
  print(colnames(smeans[i]))
  # 1:15 are music, 16:30 are speech 
  tres = t.test(col[1:15], col[16:30], paired=FALSE)
  print(tres)
  print("Mean diff")
  print((tres$estimate[1]-tres$estimate[2]))
  print("Eff size")
  d = cohen.d(col[1:15], col[16:30], paired=FALSE, within=FALSE)
  print(d)
  print((tres$estimate[1]-tres$estimate[2])/sd(col))
  i = i+1
  print("-")
  print("-")
  print("-")
}

```


# Look at languages represented in the participant pool and clean
```{r}
# first do some basic cleaning
pinfo$NAT.LANG = tolower(pinfo$NAT.LANG)
langs = pinfo$NAT.LANG
print(unique(langs))

# we can see there are many instances of Yoruba.. let's make those all identical
langs[langs=="yoruba language"]<-"yoruba"
langs[langs=="egun yoruba"]<-"yoruba"
langs[langs=="german and portugese"]<-"portuguese"
langs[langs=="english and mandarin"]<-"mandarin"
# or maybe let these two be unique? ^^

print(cat("\nCleaned Languages"))
print(unique(langs))

#replace our original column
pinfo$NAT.LANG = langs

# add a column for language family, in case want to report that way
# initialize column
pinfo$NAT.LANG.FAM = langs # initialize column
pmeans$Lang_cols = langs

# define language families
nc = c("bette", "yoruba", "agbor", "igbo", "ishan", "efik", "ibibio")
ie = c("german", "albanian", "serbian", "portoguese", "assamese", "english")
st = "mandarin"
aa = "hebrew"

# create binary col for yoruba 
pinfo$yoruba_binary = pinfo$FAMILIARITY 
for (i in 1:length(pinfo$NAT.LANG)){
  if (pinfo$NAT.LANG[i] == "yoruba"){
    pinfo$yoruba_binary[i] = 1
  }
  else{
    pinfo$yoruba_binary[i] = 0
  }
}


# define colors for future plotting
# familiarity will be gold (1), gray (0)
# yoruba will be black (1), white (0)
pmeans$FAMILIARITY = pinfo$FAMILIARITY
pmeans$Fam_cols = pinfo$FAMILIARITY
pmeans$Fam_cols[pmeans$Fam_cols == 1] ='gray'
pmeans$Fam_cols[pmeans$Fam_cols == 2] ='#e69d00'

pmeans$yor_cols = pinfo$yoruba_binary
pmeans$yor_cols[pmeans$yor_cols == 1] ='black'
pmeans$yor_cols[pmeans$yor_cols == 0] ='white'
```

Check number of people familiar vs unfamiliar who speak Yoruba
```{r}
print("Number of Yoruba speakers")
sum(pinfo$yoruba_binary)
print("Number of unfamiliar Yoruba speakers:")
sum(pinfo$yoruba_binary[pinfo$FAMILIARITY==1])
print("Number of familiar Yoruba speakers:")
sum(pinfo$yoruba_binary[pinfo$FAMILIARITY==2])
print("Number of familiar participants:")
print(sum(pinfo$FAMILIARITY-1))
print("Percent familiar who speak Yoruba")
print(28/51 *100)

```

# calculate other signal processing / binary classification metrics we might want to use
```{r}

# bias
pmeans$bias = -(pmeans$Hit_rate_H + pmeans$FA_rate_F)/2

# accuracy
pmeans$accuracy = 100 * ((pmeans$Hit_rate_H + pmeans$`Correct_Rejection (Speech)`)/(pmeans$Hit_rate_H + pmeans$`Correct_Rejection (Speech)` + pmeans$False_Alarm + pmeans$Miss))

# FNR
pmeans$FNR_akaMiss = 100 * (pmeans$Miss / (pmeans$Miss + pmeans$Hits_Music))

# FPR
pmeans$FPR_akaFA = 100 * (pmeans$False_Alarm / (pmeans$False_Alarm + pmeans$`Correct_Rejection (Speech)`))

# d prime (d′ = Z(hit rate) − Z(false alarm rate))
# d' music (already in table)
# d' speech (in this case it would be CR rate - miss rate)
pmeans$d_speech = pmeans$CR_rate - pmeans$Miss

# NOTE: d′ assumes that the standard deviations for signal and noise are equal, in our case they likely are not. Let's check
print(sd(pmeans$Hits_Music))
print(sd(pmeans$`Correct_Rejection (Speech)`))
# t test confirms that they are not.. 
print(t.test(pmeans$Hits_Music, pmeans$`Correct_Rejection (Speech)`))


# mcc 
# https://en.wikipedia.org/wiki/Matthews_correlation_coefficient
# https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-6413-7#citeas
# first the numerator
pmeans$numerator = ( (pmeans$Hits_Music * pmeans$`Correct_Rejection (Speech)`) - (pmeans$Miss * pmeans$False_Alarm) )
# now denominator
pmeans$denominator = sqrt((pmeans$Hits_Music + pmeans$False_Alarm) *   (pmeans$Hits_Music+pmeans$Miss) * (pmeans$`Correct_Rejection (Speech)` + pmeans$False_Alarm) * (pmeans$`Correct_Rejection (Speech)`+pmeans$Miss))
pmeans
# NOTE will have zero in denominator for e.g. people who FA everything. Set this to 1 (as recommended)
pmeans$denominator[pmeans$denominator==0] = 1
# now calculate mcc
pmeans$mcc = pmeans$numerator / pmeans$denominator



```



# Create long form table of participant responses for future modeling
```{r}
# convert into long form 
prating_long = pall %>%
  pivot_longer(!Participant, names_to = "Stimulus", values_to = "Rating")

pconf_long = pconf %>%
  pivot_longer(!Participant, names_to = "Stimulus", values_to = "Confidence")

# combine ratings and confidence into one table
long_all = prating_long
long_all$Confidence = pconf_long$Confidence

# add column with number for class of stim
musInds = grep("M", long_all$Stimulus)
speechInds = grep("S", long_all$Stimulus)
long_all$Class = NA
long_all$Class[musInds] = 2
long_all$Class[speechInds] = 1

# add column for error
long_all$Error = NA
long_all$Error = long_all$Class - long_all$Rating

# normalize confidence by participant?
long_all$Conf_norm = NA

long_all = long_all %>% 
  group_by(Participant) %>%
  mutate(Conf_norm = (Confidence - mean(Confidence))/std(Confidence))

# do left inner joins on other participant and stimulus level features we might want
pinfo$Participant = rownames(pinfo)
long_all = merge(x = long_all, y = pinfo, by = "Participant", all.x = TRUE)

# merge stimulus info into long table
long_all = merge(x = long_all, y = smeans, by = "Stimulus", all.x = TRUE)
```
There are 15 unique languages represented in the dataset ("undecided" does not count)


## Analyze participant responses
```{r}
########################################################################################
# Print out matrix of different error types
########################################################################################

# look at total errors in subs vs stims
# need to just get to 0 (no error) or 1 (error) 
error_pall = subset(pall, select = -c(Participant) ) # remove participant column
error_pall[,1:15] = error_pall[,1:15] - 2 # subtract 2 from music (this results in 0 if no error)
error_pall[,16:30] = error_pall[,16:30] - 1 # subtract 1 from speech (this results in 0 if no error)
error_pall = abs(error_pall)

# get some summary statistics
sub_errors = rowSums(error_pall)
music_errors = colMeans(error_pall[1:15])
speech_errors = colMeans(error_pall[16:30])
pmeans$sub_errors = sub_errors
pall$sub_errors = sub_errors
stim_errors = colSums(error_pall[1:30])
pmeans = pmeans[order(pmeans$sub_errors),]

# print out number of absolute errors
print("Range of confusions for each participant")
print(range(sub_errors))
print("Range of confusions for each stimulus")
print(range(stim_errors))

print("Number of subs with no errors:")
print(sum(sub_errors == 0))

# print out total errors by type
# can get this from table of means already provided by Pauline
print("Speech / Speech")
print(sum(pmeans$`Correct_Rejection (Speech)`))

print("Speech / Music")
print(sum(pmeans$False_Alarm))

print("Music / Speech")
print(sum(pmeans$Miss))

print("Music / Music")
print(sum(pmeans$Hits_Music))

print("Average acccuracy across participants")
print(mean(pmeans$accuracy))
print(sd(pmeans$accuracy))

print("Average FNR across participants")
print(mean(pmeans$FNR_akaMiss))
print(sd(pmeans$FNR_akaMiss))

print("Average FPR across participants")
print(mean(pmeans$FPR_akaFA))
print(sd(pmeans$FPR_akaFA))

# ahh but these are susceptible to extremes, so maybe better to go with the normalized rates Pauline already computed?
# Seems they are identical.. 
print("Average miss rate (norm) across participants")
print(mean(pmeans$Miss_rate))
print(sd(pmeans$Miss_rate))

print("Average FA rate (norm) across participants")
print(mean(pmeans$FA_rate_F))
print(sd(pmeans$FA_rate_F))

print("Average mcc across participants")
print(mean(pmeans$mcc))
print(sd(pmeans$mcc))

# run t-test on d' for familiar vs. unfamilar participants
tres = t.test(pmeans$mcc[pmeans$FAMILIARITY==2], pmeans$mcc[pmeans$FAMILIARITY==1], paired=FALSE)
d = cohen.d(pmeans$mcc[pmeans$FAMILIARITY==2], pmeans$mcc[pmeans$FAMILIARITY==1], paired=FALSE, within=FALSE)
print(tres)
print(d)
print("Difference between means")
print(tres$estimate[1] - tres$estimate[2])

```

For reviewer, check subgroup who was familiar but did not speak yoruba
```{r}
# run t-test on d' for familiar non-yoruba vs. unfamilar participants
tres = t.test(pmeans$mcc[pmeans$FAMILIARITY==2 & pmeans$yor_cols=="white"], paired=FALSE)
d = cohen.d(pmeans$mcc[pmeans$FAMILIARITY==2], pmeans$mcc[pmeans$FAMILIARITY==1], paired=FALSE, within=FALSE)
print(tres)
print(d)
print("Difference between means")
print(tres$estimate[1] - tres$estimate[2])
```



```{r}
########################################################################################
# analyze confidence
# conf_means_stims = colMeans(pconf[,-1])
# print("Mean confidence:")

# quick function to make plot we want
plot_conf_fam_hist = function(unfam, fam){
  # should feed unfam$confidence and fam$confidence
  uf = hist(unfam, freq=FALSE, breaks = seq(from=.5, to=4.5, by=1), col=rgb(.192, .192, .192, .5), main="", xlab="Confidence", ylim=c(0,1))
  
  f = hist(fam, freq=FALSE, breaks = seq(from=.5, to=4.5, by=1), col=rgb(.9,.62, 0, .5), add=TRUE, main="", xlab="Confidence", ylim=c(0,1)) 
}

########################################################################################
# look at avg conf for each error type
########################################################################################

# music --> speech (Miss)
if (op) {tiff(paste(outpath, "dundun_conf_MS.tiff", sep=""), units="in", width=3, height=3, res=300)}
hist(long_all$Confidence[long_all$Error == 1], breaks = seq(from=.5, to=4.5, by=1))
#axis(side=1, at=seq(0,120,40), labels=c(0,40,80, 120))
print("music / speech")
print(mean(long_all$Confidence[long_all$Error == 1]))

# add familiarity to plot
d1 = long_all[(long_all$Error == 1) & (long_all$FAMILIARITY==1),]
d2 = long_all[(long_all$Error == 1) & (long_all$FAMILIARITY==2),]
if (op){tiff(paste(outpath, "dundun_confFAM_MS.tiff", sep=""), units="in", width=4, height=3, res=300)}
plot_conf_fam_hist(d1$Confidence, d2$Confidence)
print("Unfamiliar")
print(mean(d1$Confidence))
print("Familiar")
print(mean(d2$Confidence))

########################################################################################
# speech --> music (FA)
if (op){tiff("dundun_conf_SM.tiff", units="in", width=3, height=3, res=300)}
hist(long_all$Confidence[long_all$Error == -1], breaks = seq(from=.5, to=4.5, by=1))
print("speech / music")
print(mean(long_all$Confidence[long_all$Error == -1]))

d1 = long_all[(long_all$Error == -1) & (long_all$FAMILIARITY==1),]
d12 = long_all[(long_all$Error == -1) & (long_all$FAMILIARITY==2),]
if (op){tiff(paste(outpath, "dundun_confFAM_SM.tiff", sep=""), units="in", width=4, height=3, res=300)}
plot_conf_fam_hist(d1$Confidence, d2$Confidence)
print("Unfamiliar")
print(mean(d1$Confidence))
print("Familiar")
print(mean(d2$Confidence))


########################################################################################
# hits music (Hit)
if (op){tiff("dundun_conf_MM.tiff", units="in", width=3, height=3, res=300)}
hist(long_all$Confidence[(long_all$Error == 0) & (long_all$Class == 2)], breaks = seq(from=.5, to=4.5, by=1))
print("music / music")
print(mean(long_all$Confidence[(long_all$Error == 0) & (long_all$Class == 2)]))


d1 = long_all[(long_all$Error == 0) & (long_all$Class == 2) & long_all$FAMILIARITY == 1,]
d2 = long_all[(long_all$Error == 0) & (long_all$Class == 2) & long_all$FAMILIARITY == 2,]
if (op){tiff(paste(outpath, "dundun_confFAM_MM.tiff", sep=""), units="in", width=4, height=3, res=300)}
plot_conf_fam_hist(d1$Confidence, d2$Confidence)
print("Unfamiliar")
print(mean(d1$Confidence))
print("Familiar")
print(mean(d2$Confidence))

########################################################################################
# hits speech (correct rejections)
if (op){tiff("dundun_conf_SS.tiff", units="in", width=3, height=3, res=300)}
hist(long_all$Confidence[(long_all$Error == 0) & (long_all$Class == 1)], breaks = seq(from=.5, to=4.5, by=1))
print("speech / speech")
print(mean(long_all$Confidence[(long_all$Error == 0) & (long_all$Class == 1)]))

if (op){tiff(paste(outpath, "dundun_confFAM_SS.tiff", sep=""), units="in", width=4, height=3, res=300)}
d1 = long_all[(long_all$Error == 0) & (long_all$Class == 1) & long_all$FAMILIARITY == 1,]
d2 = long_all[(long_all$Error == 0) & (long_all$Class == 1) & long_all$FAMILIARITY == 2,]
plot_conf_fam_hist(d1$Confidence, d2$Confidence)
print("Unfamiliar")
print(mean(d1$Confidence))
print("Familiar")
print(mean(d2$Confidence))
```

```{r}
########################################################################################
# Create heatmap
########################################################################################

# sort matrices by numbers of errors 
pall = pall[order(pall$sub_errors),]
for_hm = pall[,-c(1,ncol(pall))]
mean_stim_errors = colMeans(error_pall)
for_hm = for_hm[,c(order(music_errors), order(speech_errors)+15)]

if (op){tiff("dundunPerception_allSubs_rev1_yor.tiff", units="in", width=15, height=27, res=300)}
heatmap.2(data.matrix(for_hm), 
        scale="none", 
        xlab="", ylab="", 
        col=c("red", "blue"), 
        dendrogram="none", #"col",
        Rowv=FALSE,
        trace="none",
        #RowSideColors = pmeans$Fam_cols, 
        RowSideColors = pmeans$yor_cols,
        key=FALSE,
        density.info = "none",
        cexCol = 2,
        labRow = FALSE,
        Colv=FALSE,
)
```

Plot heatmap with yoruba and familiarity
```{r}
#if (op){png("dundunPerception_allSubs_rev1_yorFam.png", units="in", width=15, height=27, res=300)}
heatmap.plus(data.matrix(for_hm), 
        scale="none", 
        xlab="", ylab="", 
        col=c("red", "blue"), 
        Rowv=NA,
        RowSideColors = cbind(pmeans$yor_cols, pmeans$Fam_cols),  
        cexCol = 2,
        labRow = FALSE,
        Colv=NA,
        keep.dendro=FALSE
)
```

```{r}
########################################################################################
# Create box plot (with underlying points) for  mcc
########################################################################################

# recode familiarity to display how we want on plot
pmeans$Familiarity = pmeans$FAMILIARITY
pmeans$Familiarity[pmeans$Familiarity==1]<-"Unfamiliar"
pmeans$Familiarity[pmeans$Familiarity==2]<-"Familiar"
pmeans$Familiarity = as.factor(pmeans$Familiarity)

if (op){tiff("dundun_mcc2_rev1.tiff", units="in", width=5, height=7, res=300)}
pmeans %>%
  ggplot( aes(x=Familiarity, y=mcc, group=Familiarity, fill=Familiarity, color = Familiarity)) +
   # geom_boxplot(outlier.shape = NA) + # don't plot outlier because will be ovelaid by jitter point (and look like two points instead of the one that it is)
  geom_violin(draw_quantiles = c(.25,.5,.75)) +
    scale_color_manual(values=c("black", "black")) +
    scale_fill_manual(values=c("#e69d00", "grey")) +
    geom_jitter(size=2, alpha=0.3, width=.2) +
    theme_classic() +
    theme(
      legend.position="none",
      text = element_text(size=20, color="black"),
      panel.grid.minor.y = element_blank(),
    ) +
    ylim(-1.1, 1.1) + 
    xlab("") +
    ylab("MCC")
```



# Build predictive model
We ultimately want to predict participants' perception from the acoustic and participant features. Therefore we first need to get a sense of the relationships among predictors. 

#### Check for correlations among acoustic features
```{r}
# remove ratio diff and other columns we don't need 
acoust_feats = smeans[ , -which(names(smeans) %in% c("Filename","Stimulus", "Category", "ratioDiff"))] # drop label columns and ones not using

# rearrange ams to be first (how we want in paper)
acoust_feats <- acoust_feats %>%
     select(AMS_peak, everything())

# rename cols for how we want to display in paper
acoust_feats = acoust_feats %>% rename(intensity = amp, intensity_chg = ampDiff, pitch_chg = pitchDiff, entropy = ent, entropy_chg = entDiff, IOI = o2o, pulse_clarity = pulseClarity)
if (op){png("dundun_acousticCorrs.png", units="in", width=11, height=8, res=300)}
chart.Correlation(acoust_feats, histogram=TRUE, pch=25, text.panel=labs)
```
Amp diff has a very high correlation with o2o and amp. Might need to get rid. Will check VIFs.  
```{r} 
# quick plot of distribution of pulse clarity
tp = smeans
tp$Category[1:15] = "Music"
tp$Category[16:30] = "Speech"

if (op){tiff("dundun_pulseClarity.tiff", units="in", width=5, height=7, res=300)}

j = ggplot(data=tp,aes(x=Category, y=pulseClarity, group=Category,fill=Category, colour=Category))+
  geom_violin(draw_quantiles = .5) +
  #geom_boxplot(outlier.shape = NA) +
  #geom_point() +
  geom_jitter(width=.1) +
    theme_classic() +
    theme(
      legend.position="none",
      text = element_text(size=20, color="black"),
      panel.grid.minor.y = element_blank(),
    ) +
    xlab("") +
    ylim(0,.5) +
    ylab("Pulse Clarity")
j + scale_fill_manual(values=c("#0000FF", "#FF0000")) + scale_color_manual(values=c("#000000", "#000000"))


```


```{r}
# re-code some variables for model
long_all$Rating = long_all$Rating -1 # subtract 1 to get binary 0 (speech) 1 (music)
long_all$FAMILIARITY = long_all$FAMILIARITY-1 # again subtract 1 so binary
long_all$INST = long_all$INST -2 # subtract 2 so logically have 0,1,2
long_all$Confidence = long_all$Confidence-1 # again to have scale start at 0 no conf
# Center these person level vars? No because it would confuse interpretation

# remove the 4 participants who always answered music? TODO CHECK
# does not make a difference
# Participants 7, 9, 10, 51
# test = long_all
# test = subset(long_all, Participant != c(7, 9, 10,51))
# long_all = test

# Scale acoustic features
# NOTE: Tina has already scaled acoustic features between 2nd and 98th percentiles in Matlab. However, we still get scale warnings if we include them the way they are.. so.. 
long_all$amp = scale(long_all$amp, center=TRUE, scale=TRUE)
long_all$ampDiff = scale(long_all$ampDiff, center=TRUE, scale=TRUE)
long_all$ent = scale(long_all$ent, center=TRUE, scale=TRUE)
long_all$entDiff = scale(long_all$entDiff, center=TRUE, scale=TRUE)
long_all$pitch = scale(long_all$pitch, center=TRUE, scale=TRUE)
long_all$pitchDiff = scale(long_all$pitchDiff, center=TRUE, scale=TRUE)
long_all$ratio = scale(long_all$ratio, center=TRUE, scale=TRUE)
long_all$ratioDiff = scale(long_all$ratioDiff, center=TRUE, scale=TRUE)
long_all$o2o = scale(long_all$o2o, center=TRUE, scale=TRUE)
long_all$AMS_peak = scale(long_all$AMS_peak, center=TRUE, scale=TRUE)
long_all$pulseClarity = scale(long_all$pulseClarity, center=TRUE, scale=TRUE)

# full model with familiarity interactions
# lr <- glmer(Rating ~ FAMILIARITY + Confidence + Confidence*FAMILIARITY + AMS_peak + AMS_peak*FAMILIARITY +  amp + amp*FAMILIARITY + pitch + pitch*FAMILIARITY + ent + ent*FAMILIARITY + entDiff + ampDiff + ampDiff*FAMILIARITY + pitchDiff + pitchDiff*FAMILIARITY + entDiff*FAMILIARITY + o2o + o2o*FAMILIARITY + ratio + ratio*FAMILIARITY +(1|Stimulus) + (1|Participant), 
#             data=long_all,
#             family=binomial(link="logit"),
#             na.action=na.exclude,
#             control=glmerControl(optimizer="bobyqa",
#                                  optCtrl=list(maxfun=2e5)))
# summary(lr)
# print(car::vif(lr))

# final with terms with high VIF deleted
lr <- glmer(Rating ~ FAMILIARITY + Confidence + Confidence*FAMILIARITY + AMS_peak + AMS_peak*FAMILIARITY +  amp + amp*FAMILIARITY + pitch + pitch*FAMILIARITY + ent + ent*FAMILIARITY + + pitchDiff + pitchDiff*FAMILIARITY + entDiff + entDiff*FAMILIARITY + entDiff*FAMILIARITY + ratio + ratio*FAMILIARITY + pulseClarity + pulseClarity*FAMILIARITY +(1|Stimulus) + (1|Participant), 
            data=long_all,
            family=binomial(link="logit"),
            na.action=na.exclude,
            control=glmerControl(optimizer="bobyqa",
                                 optCtrl=list(maxfun=2e5)))

summary(lr)

print(car::vif(lr))

```
None of our VIFs are over 5 so we should be ok to keep all predictors.

Let's calculate some classification metrics of interest for the model 
```{r}

long_all$p <- as.numeric(predict(lr, type="response")>0.5)
print("Prediction Accuracy:")
print(mean(long_all$p==long_all$Rating))
cm = table(long_all$p,long_all$Rating)
print(cm)

tp = 1656
tn = 1081
fp = 247
fn = 226
# mcc = ( (cm[4]*cm[1]) - (cm[3]*cm[2]) ) / sqrt( (cm[4]+cm[3])*(cm[4]+cm[2])*(cm[1]+cm[3])*(cm[1]+cm[2]))
# NOTE: hard-coding this because not sure how to deal with the L formatting from the cm table. Needed quick answer. TODO fix eventually +!
mcc = ((tp*tn) - (fp*fn)) / sqrt( (tp+fp)*(tp+fn)*(tn+fp)*(tn+fn) )

print("MCC:")
print(mcc)
```


Now let's make some plots 
```{r}
# plot residuals
plot(lr)

# plot fixed effect estimates + CIs
plot_model(lr, show.values = TRUE, type = "est", value.offset = .4)

# plot marginal effects of specifc predictors
plot_model(lr, type = "pred", terms = "amp")

# plot marginal effects of specifc predictors
plot_model(lr, type = "int")

# Diagnostics
plot_model(lr, show.values = TRUE, type = "diag")

# plot random effects
plot_model(lr, type = "re") #show.values = TRUE)
```

```{r}
# save plot we want to file
# fixed effect estimates + CIs

p = plot_model(lr, show.values = TRUE, type = "est", value.offset = .4, axis.lim= c(.2,5))
if (op){tiff("dundun_perceptionLogisticModel_rev1.tiff", units="in", width=11, height=8, res=300)}
p 
# p + theme_blank()
# p + theme_sjplot2()
# p + theme_minimal()

```


```{r}
# check if overparameterized 
print(rePCA(lr)) #not overparameterized


# Do F test for model
print(Anova(lr))

# Output table with fixed and random effect results
sjPlot::tab_model(lr, 
                  show.re.var= TRUE) 

#print(r2beta(lr, method = "nsj")) # variance explained by each fixed effect
```