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utils.R
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doSample <- function(data, p.prior, pp.prior, fileName, restart=FALSE,
nCores=31, nmcBurn=250, nmc=1000, cut.converge=1.03, thorough=TRUE,
max.try=20) {
if(restart==FALSE & file.exists(paste0(fileName, '.RData'))) {
samples <- loadSamples(fileName)
} else {
## run burn-in first - this should wash out most of the prior already, reduce burn-time of h.RUN.dmc
samples <- h.samples.dmc(nmc=nmcBurn, p.prior=p.prior, data=data, pp.prior=pp.prior)
samples <- h.run.unstuck.dmc(samples=samples, nmc=nmcBurn, cores=nCores, max.try=100, p.migrate=0.05, h.p.migrate=0.05, end.no.migrate=TRUE)
save(samples, file=paste0(fileName, '.RData'))
## create new samples object, don't add (ie remove burn-in), which will be passed to h.RUN.dmc
samples <- h.samples.dmc(samples=samples, nmc=nmc, add=FALSE)
}
if(samples[[1]]$nmc < nmc) {
nAdd <- nmc - samples[[1]]$nmc
print(paste0('Adding ', nAdd, ' samples...'))
samples <- h.samples.dmc(samples=samples, nmc=nAdd, add=TRUE)
}
samples <- h.RUN.dmc(hsamples = samples, cores=nCores, cut.converge=cut.converge, thorough = thorough,
verbose=TRUE, saveFn=fileName, max.try=max.try)
}
resetPar <- function() {
dev.new()
op <- par(no.readonly = TRUE)
dev.off()
op
}
loadData <- function(name, exclude=TRUE, addBins=FALSE, removeBlock=NULL, subset=FALSE, dataRoot='./data') {
otherCols <- c()
if(name == 'exp1' | name == 'exp2' | name == 'exp3' | name == 'expMA') {
load(file.path(dataRoot, paste0('data_', name, '.RData')))
if(exclude) {
# remove excluded subjects
dat <- droplevels(dat[!dat$excl,])
}
if(subset) {
dat <- dat[!dat$subset,]
}
dat$sub <- as.factor(as.integer(dat$pp))
}
if(name == 'exp2') {
# use accuracy coding, reversing accuracies after reversals
dat$choiceIsHighP_orig <- dat$choiceIsHighP
dat$choiceIsHighP <- dat$choiceIsHighPpreRev
}
if(name == 'exp3') otherCols <- c(otherCols, 'cue')
if(name == 'expMA') {
otherCols <- c(otherCols, 'magnitude', 'ease')
dat$choice <- as.numeric(dat$choice_type)
# prepareForFitting <- prepareForFittingMA
}
# remove RTs < .15 and null responses
dat <- dat[dat$rt>.15 & !is.na(dat$rt),]
if(!is.null(removeBlock)) dat <- dat[dat$block != removeBlock,]
rownames(dat) <- NULL
# Prepare for fitting
cvs <- list()
choiceIdx <- list()
for(sub in unique(dat$sub)) {
d <- prepareForFitting(dat[dat$sub==sub,])
cvs[[sub]] <- d$outcomes
choiceIdx[[sub]] <- d$VVchoiceIdx
if(addBins) {
for(stimSet in unique(dat$stimulus_set)) {
idx = dat$sub==sub&dat$stimulus_set==stimSet
dat[idx, 'bin'] <- paste0('b', as.numeric(cut(1:sum(idx), 6)))
}
}
}
# make DMC-style
data <- dat
data$s <- data$sub
if(name == 'expMA') {
data$R <- factor(as.numeric(data$choice_type), levels=1:3, labels=c('r1', 'r2', 'r3')) #ifelse(data$choiceIsHighP==0, 'r1', 'r2')) # accuracy-coded: r1 = error, r2 = correct
} else {
data$R <- factor(ifelse(data$choiceIsHighP==0, 'r1', 'r2')) # accuracy-coded: r1 = error, r2 = correct
}
data$RT <- dat$rt
data$S <- factor('s1')
if(!addBins) {
data <- data[,c('s', 'S', 'R', 'RT', otherCols)]
} else {
data$bin <- factor(data$bin, levels=paste0('b', 1:6))
data <- data[,c('s', 'S', 'R', 'RT', 'bin', otherCols)]
}
# Add covariates and other useful things as attributes
attr(data, 'startingValues') <- d$values
attr(data, 'trialsToIgnore') <- d$trialsToIgnore
attr(data, 'VVchoiceIdx') <- choiceIdx
attr(data, 'cvs') <- cvs
return(list(data=data, dat=dat))
}
getPriors <- function(p.vector) {
dists <- rep('tnorm', length(p.vector))
p1 <- upper <- rep(NA, length(p.vector))
p2 <- rep(5, length(p.vector))
lower <- rep(0, length(p.vector))
names(p1) <- names(p2) <- names(upper) <- names(lower) <- names(p.vector)
# t0
p1[grepl(pattern='t0', names(p1))] <- 0.3
p2[grepl(pattern='t0', names(p1))] <- 0.5
upper[grepl(pattern='t0', names(p1))] <- 1
lower[grepl(pattern='t0', names(p1))] <- 0.025
# stimulus or risk representations SR/RR
p1[names(p1)=='SR' | names(p1) == 'RR'] = 0
p2[names(p1)=='SR' | names(p1) == 'RR'] = 1
lower[names(p1)=='SR' | names(p1) == 'RR'] = NA
upper[names(p1)=='SR' | names(p1) == 'RR'] = NA
# # learning rate aV or aR
# p1[names(p1)=='aV' | names(p1)=='aR'] <- -1.6 # corresponds to 0.05
# lower[names(p1)=='aV' | names(p1)=='aR'] <- NA # corresponds to 0
p1[grepl(pattern='aV', names(p1)) | grepl(pattern='aR', names(p1))] <- -1.6 # corresponds to 0.05
lower[grepl(pattern='aV', names(p1)) | grepl(pattern='aR', names(p1))] <- NA # corresponds to 0
p1[grepl(pattern='aVc', names(p1)) | grepl(pattern='aRc', names(p1))] <- -1.6 # corresponds to 0.05
lower[grepl(pattern='aVc', names(p1)) | grepl(pattern='aRc', names(p1))] <- NA # corresponds to 0
p1[grepl(pattern='aVu', names(p1)) | grepl(pattern='aRu', names(p1))] <- -1.6 # corresponds to 0.05
lower[grepl(pattern='aVu', names(p1)) | grepl(pattern='aRu', names(p1))] <- NA # corresponds to 0
# urgency v0 - may vary across conditions, so use grepl
p1[grepl(pattern='V0', names(p1))] <- 2
lower[grepl(pattern='V0', names(p1))] <- NA
# weight on v
p1[grepl(pattern='wV', names(p1))] = 9
# weight on B
p1[grepl(pattern='wB', names(p1))] = 0
p2[grepl(pattern='wB', names(p2))] = 0.1
lower[grepl(pattern='wB', names(p1))] = NA
# threshold - this may vary also across conditions, so use grepl
p1[grepl(pattern='B0', names(p1))] = 3
# weight of magnitude effect / sum of EVs
p1[grepl(pattern='wS', names(p1))] = 0
p2[grepl(pattern='wS', names(p2))] = 3
lower[grepl(pattern='wS', names(lower))] = NA
# Potential SAT effects: Mod(ulators) for V0, B0, t0, aV
p1[grepl(pattern='Mod.', names(p1))] = 0
p2[grepl(pattern='Mod.', names(p2))] = 1
lower[grepl(pattern='Mod.', names(lower))] = -1 # values < -1 would indicate make the absolute parameters negative, and they can't be negative
# threshold
p1[names(p1) == 'a' | names(p1) == 'a.SPD' | names(p1) == 'a.ACC'] = 2
p2[names(p1) == 'a' | names(p1) == 'a.SPD' | names(p1) == 'a.ACC'] = 0.5
# linear effect m
p1[names(p1) == 'm' | names(p1) == 'm.SPD' | names(p1) == 'm.ACC'] = 2
p2[names(p1) == 'm' | names(p1) == 'm.SPD' | names(p1) == 'm.ACC'] = 5
# non-linear effect vmax
p1[names(p1) == 'vmax'] = 2
p2[names(p2) == 'vmax'] = 5
p1[names(p1) == 'k'] = 1
p2[names(p2) == 'k'] = 3
p1[names(p1) == 'k2'] = 0
p2[names(p2) == 'k2'] = 1
# sv
p1[names(p1) == 'sv'] = 0.1
p2[names(p2) == 'sv'] = 0.1
# sz
p1[names(p1) == 'sz'] = 0.1
p2[names(p2) == 'sz'] = 0.1
# st0
p1[names(p1) == 'st0'] = 0.1
p2[names(p1) == 'st0'] = 0.1
# lower[names(lower) == 'st0'] = 0
# power-function on feedback
p1[names(p1) == 'pw'] = 1
p2[names(p2) == 'pw'] = 1
# offset
p1[names(p1)=='offset'] = 27.5
p2[names(p1)=='offset'] = 5
p1[names(p1) == 'pN'] = 0
p2[names(p2) == 'pN'] = 1
lower[names(p1)=='pN'] <- -Inf
#drift rate in stationary model
p1[names(p1) == 'v'] = 2
# softmax beta
p1[names(p1) == 'beta' | names(p1) == 'beta.SPD' | names(p1) == 'beta.ACC'] = 1
p2[names(p1) == 'beta' | names(p1) == 'beta.SPD' | names(p1) == 'beta.ACC'] = 5
p1[names(p1) == 'Beta' | names(p1) == 'Beta.SPD' | names(p1) == 'Beta.ACC'] = 1
p2[names(p1) == 'Beta' | names(p1) == 'Beta.SPD' | names(p1) == 'Beta.ACC'] = 5
# lba sd_v
p1[grepl(pattern='sd_v', x=names(p1))] = 1
p2[grepl(pattern='sd_v', x=names(p1))] = 1
lower[grepl(pattern='sd_v', x=names(p1))] = 0
# lba A
p1[grepl(pattern='A\\.', x=names(p1)) | names(p1) == 'A'] = 1
p2[grepl(pattern='A\\.', x=names(p1)) | names(p1) == 'A'] = 1
lower[grepl(pattern='A\\.', x=names(p1)) | names(p1) == 'A'] = 0
# combine everything
p.prior <- prior.p.dmc(
dists = dists,
p1 = p1,
p2 = p2,
lower= lower,
upper= upper
)
# par(mfcol=c(2,3)); for (i in names(p.prior)) plot.prior(i,p.prior)
sigma.prior <- prior.p.dmc(
dists = rep("gamma",length(p.vector)),
p1 =rep(1, length(p.vector)),
p2 =rep(1, length(p.vector)),
lower=rep(NA,length(p.vector)),
upper=rep(NA,length(p.vector))
)
names(sigma.prior) <- names(p.prior)
# par(mfcol=c(2,3)); for (i in names(p.prior)) plot.prior(i,sigma.prior)
pp.prior <- list(p.prior, sigma.prior)
return(pp.prior)
}
addStimSetInfo <- function(x, input, orig_dat, addColumns=NULL, nBins=10) {
df <- input[[x]]
df$stimSet <- orig_dat[orig_dat$sub==x, 'stimulus_set']
df$ease <- orig_dat[orig_dat$sub==x, 'ease']
df$trialNstimset <- NA
df$bin <- NA
# make data compatible with pp format
if(!'reps' %in% colnames(df)) {
df$reps = 1
}
# add trialN by stim set, and bin, for first rep
idx = df$reps == 1
for(stimSet in unique(df[idx, 'stimSet'])) {
idx2 <- df$stimSet == stimSet
df[idx & idx2, 'trialNstimSet'] <- seq(1, sum(idx & idx2))
df[idx & idx2, 'bin'] <- as.numeric(cut(seq(1, sum(idx & idx2)), nBins))
}
# add other columns from original data if wanted/needed
if(!is.null(addColumns)) {
for(colName in addColumns) {
df[,colName] <- NULL
df[idx, colName] <- orig_dat[orig_dat$sub==x, colName]
}
}
# copy&paste for all other reps
if(max(df$reps) > 1) {
df$trialNstimSet <- rep(df[idx, 'trialNstimSet'], max(df$reps))
df$bin <- rep(df[idx, 'bin'], max(df$reps))
if(!is.null(addColumns)) {
for(colName in addColumns) {
df[, colName] <- rep(df[idx, colName], max(df$reps))
}
}
}
df
}
calculateByBin <- function(df) {
df$acc <- as.integer(df$R)==2
attr(df, 'qRTs') <- do.call(data.frame, aggregate(RT~reps*bin, df, quantile, probs=seq(.1, .9, .4))) #cbind(quants[quants$acc==1,c('reps', 'bin')], quants[quants$acc==1,'RT'][,1])
attr(df, 'qRTsCorrect') <- do.call(data.frame, aggregate(RT~reps*bin, df[df$acc==1,], quantile, probs=seq(.1, .9, .4))) #cbind(quants[quants$acc==1,c('reps', 'bin')], quants[quants$acc==1,'RT'][,1])
attr(df, 'qRTsError') <- do.call(data.frame, aggregate(RT~reps*bin, df[df$acc==0,], quantile, probs=seq(.1, .9, .4))) #cbind(quants[quants$acc==1,c('reps', 'bin')], quants[quants$acc==1,'RT'][,1])
attr(df, 'qRTsCorrectByEase') <- do.call(data.frame, aggregate(RT~reps*bin*ease, df[df$acc==1,], quantile, probs=seq(.1, .9, .4))) #cbind(quants[quants$acc==1,c('reps', 'bin')], quants[quants$acc==1,'RT'][,1])
attr(df, 'qRTsErrorByEase') <- do.call(data.frame, aggregate(RT~reps*bin*ease, df[df$acc==0,], quantile, probs=seq(.1, .9, .4))) #cbind(quants[quants$acc==1,c('reps', 'bin')], quants[quants$acc==1,'RT'][,1])
attr(df, 'RTsOverBins') <- aggregate(RT~reps*bin, df, mean)
attr(df, 'AccOverBins') <- aggregate(acc~reps*bin, df, mean)
attr(df, 'SkewOverBins') <- aggregate(RT~reps*bin, df, skewness)
attr(df, 'RTsByChoiceByEase') <- aggregate(RT~reps*bin*ease*R, df, mean)
attr(df, 'RTsByEase') <- aggregate(RT~reps*ease*bin, df, mean)
attr(df, 'AccByEase') <- aggregate(acc~reps*ease*bin, df, mean)
attr(df, 'SkewByEase') <- aggregate(RT~reps*ease*bin, df, skewness)
if(!is.null(attr(df, 'adapt'))) {
adapted <- attr(df, 'adapt')
adapted$ease <- df$ease
adapted$bin <- df$bin
for(colName in colnames(adapted)) {
form <- as.formula(paste0(colName, '~reps*ease*bin'))
attr(df, paste0(colName, 'OverBins')) <- aggregate(form, adapted, mean)
}
}
df
}
getDescriptives <- function(x, dep.var='RT', attr.name='RTsOverBins', id.var1='~reps*bin*s', id.var2='~reps*bin') {
# print('huh')
allOverTime <- do.call(rbind, (lapply(1:length(x), function(y) {tmp <- attr(x[[y]], attr.name); tmp$s <- y; tmp})))
# print(head(allOverTime))
form1 <- as.formula(paste0(dep.var, id.var1))
res <- aggregate(form1, allOverTime, mean)
if(!is.null(id.var2)) {
form2 <- as.formula(paste0(dep.var, id.var2))
res <- aggregate(form2, aggregate(form1, res, mean), mean)
}
return(res)
# if(!is.null(res2)) {
# form2 <- as.formula(paste0(dep.var, id.var2))
# res <- aggregate(form2, aggregate(form1, res, mean), mean)
# }
#
# if('reps' %in% colnames(allOverTime)) {
# # is model
# form1 <- as.formula(paste0(dep.var, id.var1))
# form2 <- as.formula(paste0(dep.var, id.var2))
# meanOverTime <- aggregate(form2, aggregate(form1, allOverTime, mean), mean)
# }
# else {
# form1 <- as.formula(paste0(dep.var, id.var1))
# form2 <- as.formula(paste0(dep.var, id.var2))
# meanOverTime <- aggregate(form2, aggregate(form1, allOverTime, mean), mean)
# }
# return(meanOverTime)
}
plotDataPPBins <- function(data, pp, dep.var='RT', xaxis='bin', colorM='blue', colorD=1,
draw.legend=TRUE, legend.pos='topright', plot.new=TRUE, draw.polygon=TRUE,
xlim=NULL, ylim=NULL, xlab=NULL, ylab=NULL, data.cex=2, data.lwd=2,
hline.by=0.1, axhlines=NULL, plot.model.points=FALSE,
vline.by=2, axvlines=NULL, xaxt='s', yaxt='s') {
if(is.null(xlim)) xlim = range(data[,xaxis])+c(-.5, .5)
if(is.null(ylim)) {
ylimD = range(data[,dep.var])*c(.9, 1.1)
ylimM = range(pp[,dep.var])*c(.9, 1.1)
ylim = c(min(ylimD[1], ylimM[1]), max(ylimD[2], ylimM[2]))
}
if(is.null(xlab)) xlab <- xaxis
if(is.null(ylab)) ylab <- dep.var
# empty canvas
if(plot.new) {
plot(0,0, type='n', xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab, xaxt=xaxt, yaxt=yaxt)
if(!is.null(axhlines)) {
abline(h=axhlines, col='lightgrey', lty=1)
} else {
abline(h=seq(0, 5, hline.by), col='lightgrey', lty=1)
}
if(!is.null(axvlines)) {
abline(v=axvlines, col='lightgrey', lty=1)
} else {
abline(v=seq(0, 20, vline.by), col='lightgrey', lty=1)
}
}
if(draw.polygon) {
lowerQ <- aggregate(as.formula(paste0(dep.var, '~bin')), pp, quantile, .025)
upperQ <- aggregate(as.formula(paste0(dep.var, '~bin')), pp, quantile, .975)
xs <- c(lowerQ[,xaxis], rev(lowerQ[,xaxis]))
ys <- c(lowerQ[,dep.var], rev(upperQ[,dep.var]))
polygon(xs, ys, col=rgb(col2rgb(colorM)[1]/255, col2rgb(colorM)[2]/255, col2rgb(colorM)[3]/255, alpha=.3), lty = NULL, border=NA)
}
# model
if(plot.model.points) points(pp[,xaxis], pp[,dep.var], pch=20, col=colorM, cex=.01)
# data
points(data[,xaxis], data[,dep.var], col=colorD, pch=20, cex=data.cex)
lines(data[,xaxis], data[,dep.var], col=colorD, lwd=data.lwd)
if(!draw.polygon) {
if(draw.legend) legend(legend.pos, c('Data', 'Model'), lty=c(1, NA), lwd=c(2, 2), col=c(colorD,colorM), pch=c(20, 20), bty='n')
} else {
if(draw.legend) legend(legend.pos, c('Data', 'Model'), lty=c(1, NA),
fill=c(NA, rgb(col2rgb(colorM)[1]/255, col2rgb(colorM)[2]/255, col2rgb(colorM)[3]/255, alpha=.3)),
border=c(NA, 'black'),
lwd=c(2, NA), col=c(colorD,colorM), pch=c(20, NA), bty='n')
}
}
plotFits <- function(samples, dat, nCores=30) {
# Fit: overall & per participant
pp = h.post.predict.dmc(samples = samples, adapt=TRUE, save.simulation = TRUE, cores=nCores)
library(moments)
library(snowfall)
nBins <- 10
data <- lapply(samples, function(x) x$data)
if(!'ease' %in% colnames(dat)) dat$ease <- 1
pp2 <- lapply(1:length(pp), addStimSetInfo, input=pp, orig_dat=dat)
data2 <- lapply(1:length(data), addStimSetInfo, input=data, orig_dat=dat)
if(!sfIsRunning()) sfInit(TRUE, nCores); sfLibrary(moments);
pp3 <- sfLapply(pp2, calculateByBin)
data3 <- lapply(data2, calculateByBin)
# get quantiles
q10RTs <- list(getDescriptives(data3, dep.var='RT.10.', attr.name='qRTsCorrect', id.var1='~bin', id.var2=NULL),
getDescriptives(pp3, dep.var='RT.10.', attr.name='qRTsCorrect', id.var1='~reps*bin', id.var2=NULL))
q50RTs <- list(getDescriptives(data3, dep.var='RT.50.', attr.name='qRTsCorrect', id.var1='~bin', id.var2=NULL),
getDescriptives(pp3, dep.var='RT.50.', attr.name='qRTsCorrect', id.var1='~reps*bin', id.var2=NULL))
q90RTs <- list(getDescriptives(data3, dep.var='RT.90.', attr.name='qRTsCorrect', id.var1='~bin', id.var2=NULL),
getDescriptives(pp3, dep.var='RT.90.', attr.name='qRTsCorrect', id.var1='~reps*bin', id.var2=NULL))
q10RTsE <- list(getDescriptives(data3, dep.var='RT.10.', attr.name='qRTsError', id.var1='~bin', id.var2=NULL),
getDescriptives(pp3, dep.var='RT.10.', attr.name='qRTsError', id.var1='~reps*bin', id.var2=NULL))
q50RTsE <- list(getDescriptives(data3, dep.var='RT.50.', attr.name='qRTsError', id.var1='~bin', id.var2=NULL),
getDescriptives(pp3, dep.var='RT.50.', attr.name='qRTsError', id.var1='~reps*bin', id.var2=NULL))
q90RTsE <- list(getDescriptives(data3, dep.var='RT.90.', attr.name='qRTsError', id.var1='~bin', id.var2=NULL),
getDescriptives(pp3, dep.var='RT.90.', attr.name='qRTsError', id.var1='~reps*bin', id.var2=NULL))
meanAcc <- list(getDescriptives(data3, dep.var='acc', attr.name='AccOverBins', id.var1='~bin', id.var2=NULL),
getDescriptives(pp3, dep.var='acc', attr.name='AccOverBins', id.var1='~reps*bin', id.var2=NULL))
par(mfrow=c(1,3), mar=c(5,4,4,1)+.1, oma=c(0,0,1,0))
plotDataPPBins(data=meanAcc[[1]], pp=meanAcc[[2]],
xaxt='s',
dep.var='acc', ylab='Accuracy', xlab = 'Trial bin',
legend.pos='bottomright', ylim=c(0.5, 0.95), hline.by=0.1)
title('Accuracy')
plotDataPPBins(data=q10RTs[[1]], pp=q10RTs[[2]], dep.var='RT.10.', ylim=c(.4, 1.2), xaxt='s', ylab='RT (s)', xlab='Trial bin')
plotDataPPBins(data=q50RTs[[1]], pp=q50RTs[[2]], dep.var='RT.50.', plot.new = FALSE, draw.legend=FALSE)
plotDataPPBins(data=q90RTs[[1]], pp=q90RTs[[2]], dep.var='RT.90.', plot.new = FALSE, draw.legend=FALSE)
title('Correct RTs')
plotDataPPBins(data=q10RTsE[[1]], pp=q10RTsE[[2]], dep.var='RT.10.', ylim=c(.4, 1.2), xaxt='s', ylab='RT (s)', xlab='Trial bin')
plotDataPPBins(data=q50RTsE[[1]], pp=q50RTsE[[2]], dep.var='RT.50.', plot.new = FALSE, draw.legend=FALSE)
plotDataPPBins(data=q90RTsE[[1]], pp=q90RTsE[[2]], dep.var='RT.90.', plot.new = FALSE, draw.legend=FALSE)
title('Error RTs')
title('Across subjects', outer=TRUE)
## by subject
q10RTs <- list(getDescriptives(data3, dep.var='RT.10.', attr.name='qRTsCorrect', id.var1='~bin*s', id.var2=NULL),
getDescriptives(pp3, dep.var='RT.10.', attr.name='qRTsCorrect', id.var1='~reps*bin*s', id.var2=NULL))
q50RTs <- list(getDescriptives(data3, dep.var='RT.50.', attr.name='qRTsCorrect', id.var1='~bin*s', id.var2=NULL),
getDescriptives(pp3, dep.var='RT.50.', attr.name='qRTsCorrect', id.var1='~reps*bin*s', id.var2=NULL))
q90RTs <- list(getDescriptives(data3, dep.var='RT.90.', attr.name='qRTsCorrect', id.var1='~bin*s', id.var2=NULL),
getDescriptives(pp3, dep.var='RT.90.', attr.name='qRTsCorrect', id.var1='~reps*bin*s', id.var2=NULL))
q10RTsE <- list(getDescriptives(data3, dep.var='RT.10.', attr.name='qRTsError', id.var1='~bin*s', id.var2=NULL),
getDescriptives(pp3, dep.var='RT.10.', attr.name='qRTsError', id.var1='~reps*bin*s', id.var2=NULL))
q50RTsE <- list(getDescriptives(data3, dep.var='RT.50.', attr.name='qRTsError', id.var1='~bin*s', id.var2=NULL),
getDescriptives(pp3, dep.var='RT.50.', attr.name='qRTsError', id.var1='~reps*bin*s', id.var2=NULL))
q90RTsE <- list(getDescriptives(data3, dep.var='RT.90.', attr.name='qRTsError', id.var1='~bin*s', id.var2=NULL),
getDescriptives(pp3, dep.var='RT.90.', attr.name='qRTsError', id.var1='~reps*bin*s', id.var2=NULL))
meanAcc <- list(getDescriptives(data3, dep.var='acc', attr.name='AccOverBins', id.var1='~bin*s', id.var2=NULL),
getDescriptives(pp3, dep.var='acc', attr.name='AccOverBins', id.var1='~reps*bin*s', id.var2=NULL))
par(mfrow=c(1,3), mar=c(5,4,4,1)+.1)
for(subject in unique(q10RTs[[1]]$s)) {
idxD <- q10RTs[[1]]$s == subject
idxM <- q10RTs[[2]]$s == subject
plotDataPPBins(data=meanAcc[[1]][idxD,], pp=meanAcc[[2]][idxM,],
xaxt='s',
dep.var='acc', ylab='Accuracy', xlab = 'Trial bin',
legend.pos='bottomright', ylim=c(0.5, 1), hline.by=0.1)
title('Accuracy')
plotDataPPBins(data=q10RTs[[1]][idxD,], pp=q10RTs[[2]][idxM,], dep.var='RT.10.', ylim=c(.3, 1.5), xaxt='s', ylab='RT (s)', xlab='Trial bin')
plotDataPPBins(data=q50RTs[[1]][idxD,], pp=q50RTs[[2]][idxM,], dep.var='RT.50.', plot.new = FALSE, draw.legend=FALSE)
plotDataPPBins(data=q90RTs[[1]][idxD,], pp=q90RTs[[2]][idxM,], dep.var='RT.90.', plot.new = FALSE, draw.legend=FALSE)
title('Correct RTs')
# some subs didnt make errors in some bin(s) - get idx again..
idxD <- q10RTsE[[1]]$s == subject
idxM <- q10RTsE[[2]]$s == subject
plotDataPPBins(data=q10RTsE[[1]][idxD,], pp=q10RTsE[[2]][idxM,], dep.var='RT.10.', ylim=c(.3, 1.5), xaxt='s', ylab='RT (s)', xlab='Trial bin')
plotDataPPBins(data=q50RTsE[[1]][idxD,], pp=q50RTsE[[2]][idxM,], dep.var='RT.50.', plot.new = FALSE, draw.legend=FALSE)
plotDataPPBins(data=q90RTsE[[1]][idxD,], pp=q90RTsE[[2]][idxM,], dep.var='RT.90.', plot.new = FALSE, draw.legend=FALSE)
title('Error RTs')
title(paste0('Subject ', subject), outer=TRUE)
}
}
runDiagnostics <- function(fn, dat, samples=NULL, nCores=30, verbose=TRUE, chainsFileType='jpeg',
plotChains=TRUE, plotPosteriorPriors=TRUE, calculateESS=TRUE, plotFits=TRUE, calculateParametersBPIC=TRUE,
samplesDir='samples', saveDir='diagnostics') {
if(is.null(samples)) samples <- loadSamples(fn, samplesDir=samplesDir)
saveDir <- file.path(saveDir, fn)
dir.create(saveDir, showWarnings = FALSE)
if(chainsFileType == 'jpeg') dir.create(file.path(saveDir, 'chains'), showWarnings = FALSE)
# Chains: Plot hyper, append Gelman's
if(plotChains) {
if(verbose) print('Plotting chains...')
gms <- h.gelman.diag.dmc(samples)
if(chainsFileType == 'pdf') {
pdf(file=file.path(saveDir, 'chains.pdf'))
} else {
jpeg(filename=file.path(saveDir, 'chains', 'chains-%03d.jpeg'), width=7, height=7, units='in', quality=100, res=200)
}
par(mpg=c(1,1,0), mar=c(4,3,3,1)+.1, oma=c(0,0,1,0), mfrow=c(4,4))
plot.dmc(samples, hyper=TRUE, layout=c(4,4))
title(paste0('Hyper, Gelmans diag = ', round(gms['hyper'], 4)), outer=TRUE, line=0)
# Chains: Plot per participant, append Gelman's
for(subject in names(samples)) {
plot.dmc(samples, subject=subject, layout=c(4,4));
title(paste0('Subject ', subject, ' Gelmans diag = ', round(gms[which(names(gms)==as.character(subject))], 4)), outer=TRUE, line=0)
}
dev.off()
}
# Posterior vs Prior
if(plotPosteriorPriors) {
if(!grepl('softmax', fn)) {
pdf(file=file.path(saveDir, 'posteriorPrior.pdf'))
if(verbose) print('Plotting posterior vs prior...')
par(mpg=c(1,1,0), mar=c(4,3,3,1)+.1, oma=c(0,0,1,0), mfrow=c(4,4))
plot.dmc(samples, hyper=TRUE, p.prior = attr(samples, 'hyper')$pp.prior, layout=c(4,4))
for(subject in names(samples)) {
plot.dmc(samples, subject=subject, p.prior = samples[[subject]]$p.prior, layout=c(4,4))
title(paste0('Subject ', subject, ' Gelmans diag = ', round(gms[which(names(gms)==as.character(subject))], 4)), outer=TRUE, line=0)
}
dev.off()
} else {
warning('Skipping posterior vs prior plot, this crashes for softmax... (too few parameters?)')
}
}
# Effective sample size: save to csv
if(calculateESS) {
if(verbose) print('Calculating effective samples sizes...')
effS <- data.frame(do.call(rbind, effectiveSize.dmc(samples)))
effSH <- effectiveSize.dmc(samples, hyper=TRUE)
effS <- rbind(effS, effSH[grepl('.h1', names(effSH))])
effS <- rbind(effS, effSH[grepl('.h2', names(effSH))])
effS <- rbind(effS, apply(effS, 2, min))
row.names(effS) <- c(row.names(effS)[1:(nrow(effS)-3)], 'h1', 'h2', 'minimum')
write.csv(effS, file=file.path(saveDir, 'effectiveSizes.csv'))
}
# Fits
if(plotFits) {
if(verbose) print('Plotting posterior predictives...')
pdf(file=file.path(saveDir, 'posteriorPredictives.pdf'))
plotFits(samples, dat, nCores)
dev.off()
}
# Parameters: save to csv, append BPIC per participant
if(calculateParametersBPIC) {
if(verbose) print('Getting parameters, BPIC...')
bpics <- h.IC.dmc(samples)
summ <- summary.dmc(samples)
medians <- data.frame(do.call(rbind, lapply(summ, function(x) x$quantiles[,3])))
medians$aV <- pnorm(medians$aV)
medians <- rbind(medians, apply(medians, 2, mean))
medians <- rbind(medians, apply(medians, 2, sd))
medians$minimum.deviances <- c(bpics[,1], sum(bpics[,1]), NA)
medians$BPIC <- c(bpics[,2], sum(bpics[,2]), NA)
row.names(medians) <- c(row.names(medians)[1:(nrow(medians)-2)], 'mean/sum', 'sd')
write.csv(medians, file=file.path(saveDir, 'medianParametersBPICs.csv'))
}
}
pp.summary <- function(sim, samples, dat=NULL, n.post=100, probs=c(1:99)/100,random=TRUE,
bw="nrd0",report=10,save.simulation=FALSE,factors=NA, cvs=NULL,adapt=FALSE,
save.simulation.as.attribute=FALSE,ignore.R2=FALSE,censor=c(NA,NA),
gglist=TRUE, probs.gglist=c(0.1, 0.5, 0.9),CI.gglist=c(0.025, 0.975))
{
model <- attributes(samples$data)$model
facs <- names(attr(model,"factors"))
if(is.null(cvs)) {
if(!is.null(attr(samples$data, 'cvs'))) {
cvs <- attr(samples$data, 'cvs')
} else {
cvs <- samples$data[,attr(model,"cvs"),drop=FALSE]
}
}
attr(cvs,"row.facs") <- apply(apply(
samples$data[,facs,drop=FALSE],2,as.character),1,paste,collapse=".")
if ( ignore.R2 & any(names(samples$data)=="R2") )
samples$data <- samples$data[,names(samples$data)[names(samples$data)!="R2"]]
if (!is.null(factors) ) {
if (any(is.na(factors))) factors <- facs
if (!all(factors %in% facs))
stop(paste("Factors argument must contain one or more of:",paste(facs,collapse=",")))
}
resp <- names(attr(model,"responses"))
ns <- table(samples$data[,facs],dnn=facs)
n.par <- dim(samples$theta)[2]
thetas <- matrix(aperm(samples$theta,c(3,1,2)),ncol=n.par)
colnames(thetas) <- dimnames(samples$theta)[[2]]
if (is.na(n.post)) use <- c(1:dim(thetas)[1]) else {
if (random) use <- sample(c(1:dim(thetas)[1]),n.post,replace=F) else
use <- round(seq(1,dim(thetas)[1],length.out=n.post))
}
n.post <- length(use)
posts <- thetas[use,]
n.rep <- sum(ns)
# sim <- data.frame(matrix(nrow=n.post*n.rep,ncol=dim(samples$data)[2]))
# names(sim) <- names(samples$data)
# Tweaks for Stop Signal
if ( !any(names(samples$data)=="SSD") ) {
SSD <- rep(Inf,sum(ns))
leave.out <- -c(1:dim(samples$data)[2])[names(samples$data) %in% c("RT",names(cvs),"R2")]
TRIALS <- NULL
} else {
# Assumes last two are SSD and RT! FIX ME. EG WONT WORK IF THERE ARE CVS
if ( is.null(facs) ) {
SSD <- samples$data$SSD
if (any(names(samples$data)=="TRIALS"))
TRIALS <- samples$data$TRIALS else
TRIALS <- NULL
} else {
SSD <- unlist(tapply(samples$data$SSD,samples$data[,facs],identity))
if ( any(names(samples$data)=="TRIALS") )
TRIALS <- unlist(tapply(samples$data$TRIALS,samples$data[,facs],identity)) else
TRIALS <- NULL
}
leave.out <- -c(1:dim(samples$data)[2])[names(samples$data) %in% c("RT","SSD")]
# leave.out <- -c((dim(samples$data)[2]-1):dim(samples$data)[2])
}
# cat("\n")
# cat(paste("Simulating (\'.\'=",report,"): ",sep=""))
# for (i in names(samples$data)[leave.out])
# sim[[i]] <- factor(rep(NA,n.post*n.rep),levels=levels(samples$data[[i]]))
# if(adapt & save.simulation) {
# adapted <- data.frame()[1:nrow(sim),]
# }
# for (i in 1:n.post) {
# tmp <- simulate.dmc(posts[i,],model,n=ns,SSD=SSD,cvs=cvs,adapt=adapt,TRIALS=TRIALS)
# if (ignore.R2) tmp <- tmp[,names(tmp)[names(tmp)!="R2"]]
# sim[(1+(i-1)*n.rep):(i*n.rep),names(tmp)] <- tmp
# if(adapt & save.simulation) {
# adapt.out <- attr(tmp, 'adapt')
# adapted[(1+(i-1)*n.rep):(i*n.rep),names(adapt.out)] <- adapt.out
# }
# if ( (i %% report) == 0) cat(".")
# }
# cat("\n")
# if ( any(names(sim)=="R2") ) { # MTR model
# levs <- outer(levels(samples$data$R),sort(unique(samples$data$R2)),"paste",sep="")
# if (attributes(model)$type=="normDK") levs[,2] <- rep("DK",dim(levs)[1])
# levs <- sort(unique(as.vector(levs)))
# sim$R <- paste(as.character(sim$R),as.character(sim$R2),sep="")
# if (attributes(model)$type=="normDK") sim$R[sim$R2=="2"] <- "DK"
# sim$R <- factor(sim$R,levels=levs)
# samples$data$R <- paste(as.character(samples$data$R),as.character(samples$data$R2),sep="")
# if (attributes(model)$type=="normDK")
# samples$data$R[samples$data$R2=="2"] <- "DK"
# samples$data$R <- factor(samples$data$R,levels=levs)
# }
# reps <- rep(1:n.post,each=dim(samples$data)[1])
#
# if (!is.na(censor[1])) {
# fast <- sim[,"RT"] < censor[1]
# cat(paste("Removing",sum(fast),"(",round(100*mean(fast),2))," %) fast RTs\n")
# } else fast <- rep(FALSE,dim(sim)[1])
# if (!is.na(censor[2])) {
# slow <- sim[,"RT"] > censor[2]
# cat(paste("Removing",sum(slow),"(",round(100*mean(slow),2))," %) slow RTs\n")
# } else slow <- rep(FALSE,dim(sim)[1])
# ok <- !fast & !slow
# sim <- sim[ok,]
# reps <- reps[ok]
#
reps <- sim[,1]
sim <- sim[,2:ncol(sim)]
# if ( save.simulation ) {
# sim <- cbind(reps,sim)
# if(adapt) {
# adapted <- cbind(reps, adapted)
# attr(sim, 'adapt') <- adapted
# }
# attr(sim,"data") <- samples$data
# sim
# } else {
sim.dqp <- get.dqp(sim,facs=factors,probs,n.post,ns=ns,bw=bw,reps=reps)
dat.dqp <- get.dqp(sim=samples$data,factors,probs,bw=bw)
names(dat.dqp) <- paste("data",names(dat.dqp),sep=".")
out <- c(sim.dqp,dat.dqp)
dpqs <- vector(mode="list",length=length(n.post))
for (i in 1:n.post) {
simi <- sim[reps==i,]
dpqs[[i]] <- get.dqp(simi,factors,probs,1)
}
attr(out,"dpqs") <- dpqs
if (save.simulation.as.attribute)
attr(out,"sim") <- cbind(reps,sim)
if (gglist) attr(out, "gglist") <-
get.fitgglist.dmc(sim=cbind(reps,sim),data=samples$data,factors=factors, noR=FALSE,
quantiles.to.get= probs.gglist, CI = CI.gglist)
out
# }
}
h.pp.summary <- function(sim, samples, dat=NULL, ignore.subjects=FALSE,
n.post=100,probs=c(1:99)/100,bw="nrd0",
save.simulation=FALSE,factors=NA,save.subject.posts=FALSE,adapt=TRUE,
cores=1,ignore.R2=FALSE,
gglist= FALSE, probs.gglist =c(0.1, 0.5, 0.9), CI.gglist =c(0.025, 0.975),
censor=c(NA,NA))
# apply post.predict to each subject
{
# out = sim
if(is.null(dat)) {
dat <- lapply(samples,function(x){x$data})
}
if(!ignore.subjects) {
if(cores == 1) {
out = lapply(1:length(sim), function(x) pp.summary(sim=sim[[x]], samples=samples[[x]], dat=dat[[x]],
n.post=n.post,probs=probs,bw=bw,
factors=factors,save.simulation=save.simulation,gglist=FALSE,
adapt=adapt,
save.simulation.as.attribute=FALSE,ignore.R2=ignore.R2,censor=censor))
} else if(cores > 1) {
if(!sfIsRunning()) {
sfInit(parallel = TRUE, cpus=cores)
wasRunning = TRUE
} else {
wasRunning = FALSE
}
sfExport('pp.summary', 'get.dqp')
out = sfLapply(1:length(sim), function(x) pp.summary(sim=sim[[x]], samples=samples[[x]], dat=dat[[x]],
n.post=n.post,probs=probs,bw=bw,
factors=factors,save.simulation=save.simulation,gglist=FALSE,
adapt=adapt,
save.simulation.as.attribute=FALSE,ignore.R2=ignore.R2,censor=censor))
if(!wasRunning) sfStop()
}
names(out) <- names(sim)
sim <- do.call(rbind, sim)
dat <- do.call(rbind, dat)
} else {
out <- sim <- do.call(rbind, sim)
}
# sim <- do.call(rbind,lapply(out,function(x){attr(x,"sim")}))
if ( (any(names(samples[[1]]$data)=="R2")) && !ignore.R2 ) for (i in 1:length(samples)) {
levs <- outer(levels(samples[[i]]$data$R),sort(unique(samples[[i]]$data$R2)),"paste",sep="")
if (attributes(attributes(samples[[i]]$data)$model)$type=="normDK")
levs[,2] <- rep("DK",dim(levs)[1])
levs <- sort(unique(as.vector(levs)))
samples[[i]]$data$R <-
paste(as.character(samples[[i]]$data$R),as.character(samples[[i]]$data$R2),sep="")
if (attributes(attributes(samples[[i]]$data)$model)$type=="normDK")
samples[[i]]$data$R[samples[[i]]$data$R2=="2"] <- "DK"
samples[[i]]$data$R <- factor(samples[[i]]$data$R,levels=levs)
}
facs <- names(attr(attributes(samples[[1]]$data)$model,"factors"))
if (!is.null(factors)) {
if (any(is.na(factors))) factors <- facs
if (!all(factors %in% facs))
stop(paste("Factors argument must contain one or more of:",paste(facs,collapse=",")))
}
sim.dqp <- get.dqp(sim[,-1],factors,probs,n.post=1,bw=bw)
dat.dqp <- get.dqp(sim=dat,factors,probs,bw=bw)
names(dat.dqp) <- paste("data",names(dat.dqp),sep=".")
av <- c(sim.dqp,dat.dqp)
dpqs <- vector(mode="list",length=length(n.post))
for (i in 1:n.post) {
simi <- sim[sim$reps==i,-1]
dpqs[[i]] <- get.dqp(simi,factors,probs,1)
}
attr(av,"dpqs") <- dpqs
# Strip global sim attribute and dpqs for each participant
out <- lapply(out,function(x){
attr(x,"sim") <- NULL
if (!save.subject.posts) attr(x,"dpqs") <- NULL
x
})
if (gglist) attr(av, "gglist") <- get.fitgglist.dmc(sim,dat,factors=factors, noR=FALSE,
quantiles.to.get = probs.gglist, CI= CI.gglist)
attr(out,"av") <- av
out
}
loadSamples <- function(fn, samplesDir=NULL) {
if(!is.null(samplesDir)) fn <- file.path(samplesDir, fn)
fnExt <- paste0(fn, '.RData')
load(fnExt)
if('samples' %in% ls()) {
return(samples)
} else {
return(hsamples)
}
# fnAutoConverge <- gsub('.RData', '_autoconverge.RData', fnExt)
# hasLoaded <- FALSE
# if(file.exists(fnAutoConverge)) {
# if(file.info(fnAutoConverge)$mtime > file.info(fnExt)$mtime) {
# warning('Autoconverge is newest samples available. Sampling probably hasn\'t finished yet')
# load(fnAutoConverge)
# loadFinal <- TRUE
# }}
# if(!hasLoaded) {
# load(fnExt)
# if('samples' %in% ls()) {
# print('Loaded final samples')
# } else {
# warning('samples not found, only burn found...')
# return(burn1)
# }
# }
# return(samples)
}