Nothing
R/dynrPlot.R
In dynr: Dynamic Models with Regime-Switching
Defines functions
autoplot.dynrTaste
autoplot.dynrCook
dynr.ggplot
plotFormula
dynr.plotFreq
plotdf
plot.dynrCook
Mode
findRegime
multiplot
Documented in
autoplot.dynrCook
autoplot.dynrTaste
dynr.ggplot
dynr.plotFreq
plot.dynrCook
plotFormula
#------------------------------------------------------------------------------
# Plotting methods
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid::grid.newpage()
grid::pushViewport(grid::viewport(layout = grid::grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = grid::viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
findRegime = function(prop){
RegimeIndices = 1:length(prop)
MostLikelyRegime = RegimeIndices[prop==max(prop)]
return(MostLikelyRegime[1])
#If the probabilities are equal, return the first regime
}
Mode <- function(y) {
uy <- unique(y)
uy[which.max(tabulate(match(y, uy)))]
}
#Old plot function for dynrCook object
#setMethod("plot", "dynrCook",
# function(x, y=NULL, data.dynr, graphingPar=par(no.readonly = TRUE), ylab = #"Smoothed state values", xlab = "Time", numSubjDemo=2, legend.cex=1.2){
# opar = par(no.readonly = TRUE)
# par(graphingPar)
# thesmooth = data.frame(t(res@eta_smooth_final))
#
# dim_latent_var=dim(res@eta_smooth_final)[1]
# num_regime=dim(res@pr_t_given_T)[1]
#
# colnames(thesmooth) = paste0("state",1:dim_latent_var)
#
# ID = data.dynr[["id"]]
# rowIndex = 1:length(ID)
# uniID <- sort(unique(ID))
# thes = sort(sample(1:length(uniID), numSubjDemo))
#
# par(mfrow=c(ifelse(numSubjDemo%%2 > 0,
# numSubjDemo,numSubjDemo/2),
# ifelse(numSubjDemo%%2 > 0,
# 1,2)))
# if (num_regime > 1){
# thePr = t(x@pr_t_given_T)
# mostLikelyRegime = apply(thePr,1,findRegime)
# theR = Mode(mostLikelyRegime)
# }
# for (s in 1:numSubjDemo){
# T <- length(ID[ID %in% uniID[thes[s]]])
# therow = rowIndex[ID %in% uniID[thes[s]]]
# plot(
# c(1,T),
# c(min(thesmooth)-1, max(thesmooth)+quantile(unlist(thesmooth),.1)),
# ylab=ifelse(exists("ylab"),ylab,"State values"),
# xlab=ifelse(exists("xlab"),xlab,"Time"),
# main=ifelse(exists("main"),main,""),
# type='n')
# if (num_regime > 1){
# times=1:T
# thepri = mostLikelyRegime[therow]
# rect(times[thepri==theR]-.5,quantile(unlist(thesmooth),.01),times[th#epri==theR]+.5,quantile(unlist(thesmooth),.99),col="yellow",density=30)
# #legend('topleft',paste0("Regime",theR),
# #bty="n",cex=1.4,col=c(NA),fill=c("yellow"),
# #density=c(100))
# }
# time2 = if(T>500){
# seq(1,T,9)
# }else {1:T}
#
# for (j in 1:dim_latent_var){
# lines(1:T,thesmooth[therow,j], lty=1, lwd=1, col=j)
# points(time2, thesmooth[therow,j][time2], pch=as.character(j), col=j#)
# }
# stateNames = NULL
# for(j in 1:dim_latent_var){
# stateNames = c(stateNames, paste0('State ',j))
# }
# legend('topright',
# paste0("State",as.character(1:dim_latent_var)),
# lty=1:dim_latent_var,
# lwd=2, col=1:dim_latent_var, bty="n",
# pch=as.character(1:dim_latent_var), cex=legend.cex)
# }
# on.exit(par(opar))
# }
#)
##' Plot method for dynrCook objects
##'
##' @param x dynrCook object
##' @param dynrModel model object
##' @param style The style of the plot in the first panel. If style is 1 (default), user-selected smoothed state variables are plotted. If style is 2, user-selected observed-versus-predicted values are plotted.
##' @param names.state (optional) The names of the states to be plotted, which should be a subset of the state.names slot of the measurement slot of dynrModel.
##' @param names.observed (optional) The names of the observed variables to be plotted, which should be a subset of the obs.names slot of the measurement slot of dynrModel.
##' @param printDyn A logical value indicating whether or not to plot the formulas for the dynamic model
##' @param printMeas A logical value indicating whether or not to plot the formulas for the measurement model
##' @param textsize numeric. Font size used in the plot.
##' @param ... Further named arguments
##'
##' @return ggplot object.
##'
##' @details
##' This is a wrapper around \code{\link{dynr.ggplot}}. A great benefit of it is that it shows the model equations in a plot.
plot.dynrCook <- function(x, dynrModel, style = 1, names.state, names.observed, printDyn=TRUE, printMeas=TRUE, textsize=4, ...) {
#The first panel is the ggplot
if(missing(names.observed)){names.observed=dynrModel@measurement@obs.names}
if(missing(names.state)){names.state=dynrModel@measurement@state.names}
p1 <- dynr.ggplot(x, dynrModel, style, numSubjDemo=1, names.state=names.state, names.observed=names.observed, ...)
#If there are more than 2 regimes, the second panel shows the histogram of the most probable regimes across time and subjects.
if (dynrModel$num_regime>1){
regime <- NULL
highProbR <- data.frame(regime=apply(x@pr_t_given_T,2,which.max))
p2 <- ggplot2::ggplot(data = highProbR, ggplot2::aes(factor(regime))) +
ggplot2::geom_bar() +
ggplot2::scale_fill_brewer(palette = 3) +
ggplot2::xlab('Regime') + ggplot2::ylab('Counts') + ggplot2::labs(fill = '') +
ggplot2::ggtitle('Counts by most probable regime') +
ggplot2::theme(axis.title = ggplot2::element_text(size=14),
axis.text=ggplot2::element_text(size=12),
plot.title = ggplot2::element_text(size = 12, colour = "black", face = "bold", hjust = 0.5, vjust=0.01))
}
#The third panel plots the model formulae
p3 <- plotFormula(dynrModel, ParameterAs=sprintf("%.2f", x@transformed.parameters), printDyn=printDyn, printMeas=printMeas, textsize=textsize)
#Organize the panels using the multiplot function
if (dynrModel$num_regime > 1){
multiplot(p1, p2, p3, cols = 1, layout=matrix(c(1,1,2,3), nrow=2, byrow=TRUE))
}else{
multiplot(p1, p3, layout=matrix(c(1,1,2,2), nrow=2, byrow=TRUE))
}
}
plotdf <- function(vec_tex){
dataframe=data.frame(text=sapply(paste0("$",vec_tex,"$"),function(x){as.character(latex2exp::TeX(x))}))
dataframe$x<-0
return(dataframe)
}
##' Plot of the estimated frequencies of the regimes across all individuals and time points
##' based on their smoothed regime probabilities
##'
##' @param res The dynr object returned by dynr.cook().
##' @param dynrModel The model object to plot.
##' @param names.regime (optional) Names of the regimes (must match the length of the number of regimes)
##' @param title (optional) Title of the plot.
##' @param xlab (optional) Label of the x-axis.
##' @param ylab (optional) Label of the y-axis.
##' @param textsize (default = 12) Text size for the axis labels and title (= textsize + 2).
##' @param print (default = TRUE) A flag for whether the plot should be printed.
##'
##' @return ggplot object
dynr.plotFreq <- function(res, dynrModel, names.regime, title, xlab, ylab, textsize=12,print=TRUE) {
if(missing(names.regime)){names.regime<-paste0("Regime",1:dynrModel@num_regime)}
if(missing(title)){title<-'Counts by most probable regime'}
if(missing(xlab)){xlab<-'Regime'}
if(missing(ylab)){ylab<-'Counts'}
regime <- NULL
highProbR <- data.frame(regime=factor(apply(res@pr_t_given_T,2,which.max),
levels=1:dynrModel@num_regime,
labels=names.regime))
p2 <- ggplot2::ggplot(data = highProbR, ggplot2::aes(factor(regime))) +
ggplot2::geom_bar() +
ggplot2::scale_fill_brewer(palette = 3) +
ggplot2::xlab(xlab) + ggplot2::ylab(ylab) + ggplot2::labs(fill = '') +
ggplot2::ggtitle(title) +
ggplot2::theme(axis.title = ggplot2::element_text(size=textsize+2),
axis.text=ggplot2::element_text(size=textsize),
plot.title = ggplot2::element_text(size = textsize, colour = "black", face = "bold", hjust = 0.5, vjust=0.01))
if (print) print(p2)
return(p2)
}
##' Plot the formula from a model
##'
##' @param dynrModel The model object to plot.
##' @param ParameterAs The parameter values or names to plot. The underscores in parameter names are saved for
##' use of subscripts. Greek letters can be specified as corresponding LaTeX symbols without backslashes (e.g., "lambda")
##' and printed as greek letters.
##' @param printDyn A logical value indicating whether or not to plot the formulas for the dynamic model.
##' @param printMeas A logical value indicating whether or not to plot the formulas for the measurement model
##' @param printRS logical. Whether or not to print the regime-switching model. The default is FALSE.
##' @param textsize The text size use in the plot.
##'
##' @details
##' This function typesets a set of formulas that represent the model. Typical inputs to the \code{ParameterAs} argument are (1) the starting values for a model, (2) the final estimated values for a model, and (3) the parameter names. These are accessible with (1) \code{model$xstart}, (2) \code{coef(cook)}, and (3) \code{model$param.names} or \code{names(coef(cook))}, respectively.
##'
##' @return ggplot object
plotFormula <- function(dynrModel, ParameterAs, printDyn=TRUE, printMeas=TRUE,
printRS=FALSE, textsize=4){
dynrModel <- PopBackModel(dynrModel, LaTeXnames(ParameterAs, latex = FALSE))
#Dynamic model
if (printDyn) {
if ("dynrDynamicsMatrix" %in% class(dynrModel$dynamics)){
state.names <- (dynrModel$measurement)$state.names
exo.names <- (dynrModel$dynamics)$covariates
dynrModel@dynamics@values.dyn <- lapply((dynrModel$dynamics)$values.dyn, preProcessNames,state.names,state.names)
dynrModel@dynamics@values.exo <- lapply((dynrModel$dynamics)$values.exo, preProcessNames,state.names,exo.names)
dynrModel@dynamics@values.int <- lapply((dynrModel$dynamics)$values.int, preProcessNames,state.names)
}
dyn.df <- data.frame(text="bold('Dynamic Model')",x=0)
nRegime=ifelse(class(dynrModel@dynamics)=="dynrDynamicsFormula",
length(dynrModel@dynamics@formula),
length(dynrModel@dynamics@values.dyn))
dyn_tex=printex(dynrModel@dynamics,AsMatrix=FALSE)
nEq <- length(dyn_tex[[1]])
# noise_tex <- paste0(" + ", ifelse(dynrModel@dynamics@isContinuousTime, "d", ""), "w_{",1:nEq, "}(t)")
noise_tex <- paste0(ifelse(dynrModel@dynamics@isContinuousTime, "d", ""),
"w_{", 1:nEq, "}(t)")
for (i in 1:nRegime){
if (nRegime>1){
dyn.df <- rbind(dyn.df,data.frame(text=paste0("'Regime ",i,":'"),x=0))
}
if (length(dynrModel@noise@values.latent)==1){
values.latent.mat <- dynrModel@noise@values.latent[[1]]
}else if (length(dynrModel@noise@values.latent)==nRegime){
values.latent.mat <- dynrModel@noise@values.latent[[i]]
}else{stop("The number of regimes implied by the dynamic noise structure does not match the number of regimes in the dynamic model.")}
pos.zero <- diag(values.latent.mat) == 0
# noise_tex[diag(values.latent.mat)==0] <- ""
# dyn.df <- rbind(dyn.df,plotdf(LaTeXnames(paste0(dyn_tex[[i]], noise_tex))))
dyn.df <- rbind(dyn.df,
plotdf(LaTeXnames(
paste0(dyn_tex[[i]],
ifelse(pos.zero, "",
paste0(" + ", noise_tex))))),
plotdf(LaTeXnames(
paste0(ifelse(pos.zero, "",
paste0(noise_tex, " \\sim ", "N(0,\\,",
diag(values.latent.mat), ")"))))) )
}
} else {
dyn.df <- plotdf("")
}
#Measurement model
if (printMeas) {
meas.df <- data.frame(text="bold('Measurement Model')", x=0)
nRegime <- length(dynrModel$measurement$values.load)
meas_tex <- printex(dynrModel$measurement, AsMatrix=FALSE)
nEq <- length(meas_tex[[1]])
# noise_tex <- paste0(" + ", "epsilon_{", 1:nEq, "}")
noise_tex <- paste0("epsilon_{", 1:nEq, "}")
for (i in 1:nRegime){
if (nRegime > 1){
meas.df <- rbind(meas.df, data.frame(text=paste0("'Regime ",i,":'"), x=0))
}
if (length(dynrModel$noise$values.observed) == 1){
values.observed.mat <- dynrModel$noise$values.observed[[1]]
}else if (length(dynrModel$noise$values.observed) == nRegime){
values.observed.mat <- dynrModel$noise$values.observed[[i]]
}else{stop("The number of regimes implied by the measurement noise structure does not match the number of regimes in the measurement model.")}
pos.zero <- diag(values.observed.mat) == 0
# noise_tex[diag(values.observed.mat)==0] <- ""
# meas.df<-rbind(meas.df,plotdf(LaTeXnames(paste0(meas_tex[[i]], noise_tex))))
meas.df <- rbind(meas.df,
plotdf(LaTeXnames(
paste0(meas_tex[[i]],
ifelse(pos.zero, "", paste0(" + ", noise_tex, ",")),
"\\;\\,",
ifelse(pos.zero,
"",
paste0(noise_tex, " \\sim ", "N(0,\\,", diag(values.observed.mat), ")"))))))
}
} else {
meas.df <- plotdf("")
}
# Regime-switching model
if (printRS) {
rs.df <- data.frame(text="bold('Regime-switching Model')", x=0)
if (any(dim(dynrModel@regimes@params) == 0)) {
warning("No Regime-switching Model. Please turn off 'printRS'.")
} else {
# helper function to make sub. e.g., a_111 to a_{111}
to_sub <- function(parnames) {
idx_sub <- grepl('_', parnames)
if ( sum(idx_sub)==0 ) { return(parnames) }
subs <- parnames[idx_sub]
subs_spl <- strsplit(subs, '_')
subbed <- sapply(subs_spl, function(sub) {
paste0(sub[1], '_', paste0('{', sub[2], '}') )
})
parnames[idx_sub] <- subbed
parnames
}
covars <- dynrModel@regimes@covariates
if ( length(covars) == 0 ) {
covars <- c("")
} else {
covars <- c("", paste0("*", covars) )
}
covars_n <- length(covars)
# TODO: check nregime to fit with others
nregime <- nrow(dynrModel@regimes@params)
# column index for the intercepts of regimes
inter_col <- cumsum(c(1, rep(covars_n, nregime-2)))
# i:(i+ncov-1)
parnames <- to_sub( dynrModel@regimes@paramnames )
param_rs <- matrix(parnames, nregime)
param_inter <- param_rs[, inter_col, drop=FALSE]# intercepts
refrow <- dynrModel@regimes@refRow
if (length(refrow) != 0) {
for ( co in inter_col ) {
param_rs[-refrow, co] <-
paste0(param_rs[refrow, co], ' + ', param_rs[-refrow, co])
}
}
param_rs <- matrix(sweep(param_rs, 2, covars, FUN=paste0),
nrow=nregime)
ll <- vector('list', length=length(inter_col))
for ( i in 1:length(inter_col) ) {
ll[[i]] <-
param_rs[, inter_col[i]:(inter_col[i]+covars_n-1),
drop=FALSE]
}
sl <- sapply(ll, function(l) {
apply(l, 1, paste, collapse=" + ")
})
# transition probability subs
transub <- array(c(row(sl), col(sl)), c(nrow(sl), ncol(sl), 2))
transubed <- apply(transub, c(1,2), paste, collapse="")
rs.df <- rbind(rs.df,
plotdf(paste0("Log-odds(p_{",
transubed, "}) = ",
sl)))
}
} else {
rs.df <- plotdf("")
}
plot.df <- rbind(meas.df, dyn.df, rs.df)
plot.df$y=seq((dim(plot.df)[1]-1)*5+1, 1, by=-5)
x <- NULL
y <- NULL
fig<-ggplot2::ggplot(plot.df, ggplot2::aes(x=x, y=y, label=text))+
ggplot2::geom_text(parse=TRUE,size=textsize)+
ggplot2::theme_void() + ggplot2::ylim(min(plot.df$y)-5, max(plot.df$y)+5)
return(fig)
}
##' The ggplot of the smoothed state estimates and the most likely regimes
##'
##' @aliases autoplot.dynrCook
##'
##' @param res The dynr object returned by \code{dynr.cook()}.
##' @param dynrModel The model object to plot.
##' @param style The style of the plot. If style is 1 (default), user-selected smoothed state variables are plotted. If style is 2, user-selected observed-versus-predicted values are plotted.
##' @param numSubjDemo The number of subjects to be randomly selected for plotting.
##' @param idtoPlot Values of the ID variable to plot.
##' @param names.state (optional) The names of the states to be plotted, which should be a subset of the state.names slot of the measurement slot of dynrModel.
##' @param names.observed (optional) The names of the observed variables to be plotted, which should be a subset of the obs.names slot of the measurement slot of dynrModel.
##' @param names.regime (optional) The names of the regimes to be plotted, which can be missing.
##' @param shape.values (optional) A vector of values that correspond to the shapes of the points, which can be missing. See the R documentation on pch for details on possible shapes.
##' @param title (optional) A title of the plot.
##' @param ylab (optional) The label of the y axis.
##' @param is.bw Is plot in black and white? The default is FALSE.
##' @param colorPalette A color palette for lines and dots. It is a value passed to the palette argument of the \code{ggplot2::scale_colour_brewer()} function. These palettes are in the R package \pkg{RColorBrewer}. One can find them by attaching the package with \code{library(RColorBrewer)} and run \code{display.brewer.all()}.
##' @param fillPalette A color palette for blocks. It is a value passed to the palette argument of the \code{ggplot2::scale_fill_brewer()} function. These palettes are in the package \pkg{RColorBrewer}. One can find them by attaching the package with \code{library(RColorBrewer)} and run \code{display.brewer.all()}.
##' @param mancolorPalette (optional) A color palette for manually scaling the colors of lines and dots. It is a vector passed to the values argument of the \code{ggplot2::scale_colour_manual} function.
##' @param manfillPalette (optional) A color palette for manually scaling the colors of filled blocks. It is a vector passed to the values argument of the \code{ggplot2::scale_fill_manual} function.
##' @param ... A list of elements that modify the existing ggplot theme. Consult the \code{ggplot2::theme()} function in the R package \pkg{ggplot2} for more options.
##'
##' @details
##' This function outputs a ggplot layer that can be modified using functions in the package \pkg{ggplot2}. That is, one can add layers, scales, coords and facets with the "+" sign. In an example below, the \code{ggplot2::ylim()} function is used to modify the limits of the y axis of the graph. More details can be found on \url{https://ggplot2.tidyverse.org/} and \url{https://ggplot2.tidyverse.org/reference/}.
##'
##' The two functions \code{dynr.ggplot()} and \code{autoplot()} as identical aliases of one another. The \code{autoplot()} function is an S3 method from the package \pkg{ggplot2} that allows many objects to be plotted and works like the base \code{plot()} function.
##'
##' @return ggplot object
##'
##' @examples
##' # The following code is part of a demo example in dynr
##' \dontrun{
##' demo(RSLinearDiscreteYang, package='dynr')
##' p <- dynr.ggplot(yum, dynrModel = rsmod, style = 1,
##' names.regime = c("Deactivated", "Activated"),
##' title = "(B) Results from RS-AR model", numSubjDemo = 1,
##' shape.values = c(1),
##' text = element_text(size = 16),
##' is.bw = TRUE)
##' # One can modify the limits on the y axis by using '+'
##' p + ggplot2::ylim(-2, 4)
##'
##' autoplot(yum, dynrModel = rsmod, style = 1,
##' names.regime = c("Deactivated", "Activated"),
##' title = "(B) Results from RS-AR model", numSubjDemo = 1,
##' shape.values = c(1),
##' text = element_text(size = 16),
##' is.bw = TRUE)
##' }
dynr.ggplot <- function(res, dynrModel, style = 1,
numSubjDemo=2, idtoPlot=c(),
names.state,
names.observed,
names.regime,
shape.values,
title,
ylab,
is.bw=FALSE,
colorPalette="Set2",
fillPalette="Set2",
mancolorPalette, manfillPalette, ...){
data.dynr=dynrModel@data
dim_latent_var=dim(res@eta_smooth_final)[1]
num_regime=dim(res@pr_t_given_T)[1]
if(missing(names.observed)){names.observed=dynrModel@measurement@obs.names}
observed=which(dynrModel@measurement@obs.names%in%names.observed)
if ((length(observed)>8)&(missing(mancolorPalette))&(style==2)){stop("You provided too many variables than the default color palette can handle.\nPlease consider specify the mancolorPalette argument.")}
if(missing(names.state)){names.state=dynrModel@measurement@state.names}
states=which(dynrModel@measurement@state.names%in%names.state)
if(missing(shape.values)){
if (style==1) shape.values=states#48+states
if (style==2) shape.values=c(rep(32,length(observed)),observed)#pre-obs
}else{
if (style==2) shape.values=c(rep(32,length(shape.values)), shape.values)#pre-obs
}
if (style==1) line.values=rep(1,length(states))
if (style==2) line.values=rep(c(1,0),each=length(observed))#pre-obs
num_sbj=length(unique(data.dynr$id))
if(length(idtoPlot)<1){
randid =sample(unique(data.dynr$id),numSubjDemo)
}else {randid=idtoPlot}
if(missing(title)){
if (style==1) title=" "
if (style==2) title="Observed vs Predicted"
}
if(missing(ylab)){
if (style==1) ylab="Smoothed State Values"
if (style==2) ylab="Values"
}
data.plot<-data.frame(id=as.factor(data.dynr$id),time=data.dynr$time)
if(num_regime==1){
names.id.vars <- c("id","time")
if (style==1){
names.measure.vars <- names.state
data.plot <- cbind(data.plot, matrix(res@eta_smooth_final[states,],ncol=length(states),byrow=TRUE))
lines.var <- names.state
points.var <- names.state
}#end of style 1
if (style==2){
dynrModel=PopBackModel(dynrModel, LaTeXnames(res@transformed.parameters, latex = FALSE))
dynrModel@measurement@values.load=lapply(dynrModel@measurement@values.load, function(x){matrix(as.numeric(x),ncol=ncol(x))})
num_regime_meas <- length(dynrModel@measurement@values.load)
if (length(dynrModel@measurement@values.exo)!=0){
dynrModel@measurement@values.exo=lapply(dynrModel@measurement@values.exo, function(x){matrix(as.numeric(x),ncol=ncol(x))})
}
if (length(dynrModel@measurement@values.int)!=0){
dynrModel@measurement@values.int=lapply(dynrModel@measurement@values.int, function(x){matrix(as.numeric(x),ncol=ncol(x))})
}
#predicted scores
if (num_regime_meas==1){#one-regime measurement model
predicted=dynrModel@measurement@values.load[[1]]%*%res@eta_smooth_final
if (length(dynrModel@measurement@values.int)!=0){
predicted=predicted+dynrModel@measurement@values.int[[1]]%*%matrix(rep(1,ncol(res@eta_smooth_final)), nrow=1)
}
if (length(dynrModel@measurement@values.exo)!=0){
exo.mat=t(as.matrix(dynrModel@data$covariates[,paste0("covar",sapply(dynrModel@measurement@exo.names, function(x){which(dynrModel@data$covariate.names==x)}))]))
predicted=predicted+dynrModel@measurement@values.exo[[1]]%*%exo.mat
}
}#end of one-regime meas
names.measure.vars <- c(paste0(names.observed, ".predicted"), paste0(names.observed, ".observed"))
data.plot <- cbind(data.plot, matrix(predicted[observed,],ncol=length(observed),byrow=TRUE, dimnames=list(NULL, paste0(names.observed, ".predicted"))))
data.plot <- cbind(data.plot, data.dynr$observed[,observed])
lines.var <- paste0(names.observed, ".predicted")
points.var <- paste0(names.observed, ".observed")
if (length(observed)<8){
fancy8 <- c("#edaac1","#a7dab0","#dfb3e2","#8bc5ed","#bcb8ec","#e6b296","#d2d39d","#82d9d6")
mancolorPalette=rep(fancy8[observed],2)
}
}#end of style 2
names(data.plot) <- c(names.id.vars, names.measure.vars)
data_long <- reshape2::melt(data.plot[data.plot$id%in%randid,],
id.vars=names.id.vars,
measure.vars=names.measure.vars,
value.name="value")
data_long$variable<-factor(data_long$variable, levels=sort(names.measure.vars))
value <- NULL
variable <- NULL
partial.plot<-ggplot2::ggplot(data_long, ggplot2::aes(x=time, y=value, colour = variable, shape = variable, linetype = variable)) +
ggplot2::geom_line(data=data_long[data_long$variable%in%lines.var,], size=1) +
ggplot2::geom_point(data=data_long[data_long$variable%in%points.var,], size=4) +
ggplot2::scale_linetype_manual(labels=sort(names.measure.vars), values=line.values[order(names.measure.vars)])+
ggplot2::scale_shape_manual(labels=sort(names.measure.vars), values=shape.values[order(names.measure.vars)])+
ggplot2::facet_wrap(~id)+
ggplot2::labs(title = title, y=ylab, ggtitle="")+
ggplot2::theme(plot.title=ggplot2::element_text(lineheight=.8, face="bold", hjust = 0.5, vjust=0.01), legend.position="bottom",legend.text=ggplot2::element_text(lineheight=0.8, face="bold"),...)
}else{#more than two regimes
if(missing(names.regime)){names.regime = 1:num_regime}
findRegime = function(prop){
MostLikelyRegime = names.regime[prop==max(prop)]
return(MostLikelyRegime[1])
#If the probabilities are equal, return the first regime
}
addendtime <- function(data_frame){
data_frame$endtime <- c(data_frame$time[2:nrow(data_frame)], data_frame$time[nrow(data_frame)]+min(diff(data_frame$time)))
return(data_frame)
}
data.plot <- plyr::ddply(data.plot,"id",addendtime)
data.plot$regime<-as.factor(apply(res@pr_t_given_T,2,findRegime))
names.id.vars<-c("id","time","endtime","regime")
if (style==1){
names.measure.vars<-names.state
data.plot<-cbind(data.plot,matrix((res@eta_smooth_final[states,]),ncol=length(states),byrow=TRUE))
lines.var <- names.state
points.var <- names.state
}#end of style 1
if (style==2){
dynrModel=PopBackModel(dynrModel, LaTeXnames(res@transformed.parameters, latex = FALSE))
dynrModel@measurement@values.load=lapply(dynrModel@measurement@values.load, function(x){matrix(as.numeric(x),ncol=ncol(x))})
num_regime_meas <- length(dynrModel@measurement@values.load)
if (length(dynrModel@measurement@values.exo)!=0){
dynrModel@measurement@values.exo=lapply(dynrModel@measurement@values.exo, function(x){matrix(as.numeric(x),ncol=ncol(x))})
}
if (length(dynrModel@measurement@values.int)!=0){
dynrModel@measurement@values.int=lapply(dynrModel@measurement@values.int, function(x){matrix(as.numeric(x),ncol=ncol(x))})
}
#predicted scores
if (num_regime_meas==1){#one-regime measurement model
predicted=matrix(dynrModel@measurement@values.load[[1]][observed,],nrow=length(observed))%*%res@eta_smooth_final
if (length(dynrModel@measurement@values.int)!=0){
predicted=predicted+matrix(dynrModel@measurement@values.int[[1]][observed,],nrow=length(observed))%*%matrix(rep(1,ncol(res@eta_smooth_final)), nrow=1)
}
if (length(dynrModel@measurement@values.exo)!=0){
exo.mat=t(as.matrix(dynrModel@data$covariates[,paste0("covar",sapply(dynrModel@measurement@exo.names, function(x){which(dynrModel@data$covariate.names==x)}))]))
predicted=predicted+matrix(dynrModel@measurement@values.exo[[1]][observed,],nrow=length(observed))%*%exo.mat
}
#end of one-regime meas
}else if (num_regime_meas==num_regime){
ntotaltime=ncol(res@eta_smooth_final)
predicted=matrix(rep(0,length(observed)*ntotaltime),ncol=ntotaltime)
for (index in 1:ntotaltime){
predicted[,index]=matrix(dynrModel@measurement@values.load[[data.plot$regime[index]]][observed,],nrow=length(observed))%*%matrix(res@eta_smooth_final[,index], ncol=1)
if (length(dynrModel@measurement@values.int)!=0){
predicted[,index]=matrix(predicted[,index]+dynrModel@measurement@values.int[[data.plot$regime[index]]][observed,],nrow=length(observed))
}
if (length(dynrModel@measurement@values.exo)!=0){
exo.mat=t(as.matrix(dynrModel@data$covariates[index,paste0("covar",sapply(dynrModel@measurement@exo.names, function(x){which(dynrModel@data$covariate.names==x)}))]))
predicted[,index]=matrix(predicted[,index]+dynrModel@measurement@values.exo[[data.plot$regime[index]]][observed,],nrow=length(observed))%*%exo.mat
}
}
#end of regime specific measurement
}
names.measure.vars <- c(paste0(names.observed, ".predicted"), paste0(names.observed, ".observed"))
data.plot <- cbind(data.plot, matrix(predicted,ncol=length(observed), byrow=TRUE, dimnames=list(NULL, paste0(names.observed, ".predicted"))))
data.plot <- cbind(data.plot, data.dynr$observed[,observed])
lines.var <- paste0(names.observed, ".predicted")
points.var <- paste0(names.observed, ".observed")
if (length(observed)<=8){
fancy8 <- c("#edaac1","#a7dab0","#dfb3e2","#8bc5ed","#bcb8ec","#e6b296","#d2d39d","#82d9d6")
manfillPalette=fancy8[9-(1:num_regime)]
mancolorPalette=rep(fancy8[observed],2)
}
}#end of style 2
names(data.plot)<-c(names.id.vars,names.measure.vars)
data_long<- reshape2::melt(data.plot[data.plot$id%in%randid,],
id.vars=names.id.vars,
measure.vars=names.measure.vars,
value.name="value")
data_long$variable<-factor(data_long$variable, levels=sort(names.measure.vars))
#data_long$statenumber<-as.factor(sub("state","",data_long$variable))
endtime <- NULL
regime <- NULL
partial.plot<-ggplot2::ggplot(data_long,ggplot2::aes(x=time, y=value, group=variable)) +
ggplot2::geom_rect(ggplot2::aes(xmin=time, xmax=endtime, ymin=-Inf, ymax=Inf, fill=regime), alpha=.15) +
ggplot2::geom_line(data=data_long[data_long$variable%in%lines.var,], size=1, ggplot2::aes(color=variable, linetype=variable)) +
ggplot2::geom_point(data=data_long[data_long$variable%in%points.var,], size=3, ggplot2::aes(color=variable, shape=variable)) +
ggplot2::scale_linetype_manual(labels=sort(names.measure.vars), values=line.values[order(names.measure.vars)])+
ggplot2::scale_shape_manual(labels=sort(names.measure.vars), values=shape.values[order(names.measure.vars)])+
#geom_text(size=1, ggplot2::aes(label=statenumber,color=variable))+
ggplot2::facet_wrap(~id)+
ggplot2::labs(title = title,y=ylab,ggtitle="")+
ggplot2::theme(plot.title=ggplot2::element_text(lineheight=.8, face="bold", hjust = 0.5, vjust=0.01), legend.position="bottom",legend.text=ggplot2::element_text(lineheight=0.8, face="bold"),...)
}
if(is.bw){
bw.plot<-partial.plot+
ggplot2::scale_fill_grey(start = 0.1, end = 0.9)+
ggplot2::scale_color_grey(labels=sort(names.measure.vars), start = 0.2, end = 0.7)
return(bw.plot)
}else{
default.plot<-partial.plot+
ggplot2::scale_colour_brewer(labels=sort(names.measure.vars), palette=colorPalette)+
ggplot2::scale_fill_brewer(palette=fillPalette)
if(!missing(mancolorPalette)){
manual.plot<-partial.plot+ggplot2::scale_colour_manual(labels=sort(names.measure.vars), values=mancolorPalette[order(names.measure.vars)])
}
if(!missing(manfillPalette)){
if(!exists("manual.plot")){manual.plot<-partial.plot}
manual.plot<-manual.plot+ggplot2::scale_fill_manual(values=manfillPalette)
}
if (exists("manual.plot")){
return(manual.plot) #print(manual.plot)
}else{
return(default.plot)#print(default.plot)
}
}
}
##' @rdname dynr.ggplot
##'
##' @param object The same as res. The dynr object returned by dynr.cook().
##'
##' @return ggplot object
autoplot.dynrCook <- function(object, dynrModel, style = 1,
numSubjDemo=2, idtoPlot=c(),
names.state,
names.observed,
names.regime,
shape.values,
title,
ylab,
is.bw=FALSE,
colorPalette="Set2",
fillPalette="Set2",
mancolorPalette, manfillPalette, ...){
dynr.ggplot(object, dynrModel=dynrModel, style = style,
numSubjDemo=numSubjDemo, idtoPlot=idtoPlot,
names.state=names.state,
names.observed=names.observed,
names.regime=names.regime,
shape.values=shape.values,
title=title,
ylab=ylab,
is.bw=is.bw,
colorPalette=colorPalette,
fillPalette=fillPalette,
mancolorPalette=mancolorPalette, manfillPalette=manfillPalette, ...)
}
##' The ggplot of the outliers estimates.
##'
##' @param object A dynrTaste object.
##' @param numSubjDemo The number of subjects, who have
##' largest joint chi-square statistic, to be selected for plotting.
##' @param idtoPlot Values of the ID variable to plot.
##' @param names.state (optional) The names of the states to be plotted, which should be a subset of the state.names slot of the measurement slot of dynrModel. If NULL, the t statistic plots for all state variables will be included.
##' @param names.observed (optional) The names of the observed variables to be plotted, which should be a subset of the obs.names slot of the measurement slot of dynrModel. If NULL, the t statistic plots for all observed variables will be included.
##' @param ... Place holder for other arguments. Please do not use.
##'
##' @return a list of ggplot objects for each ID.
##' The plots of chi-square statistics (joint and independent),
##' and the plots of t statistic for \code{names.state} and \code{names.observed} will be included.
##' Users can modify the ggplot objects using ggplot grammar.
##' If a \code{filename} is provided, a pdf of plots will be saved additionally.
autoplot.dynrTaste <- function(object,
numSubjDemo=2, idtoPlot=NULL,
names.state=NULL, names.observed=NULL, ...) {
if ( !inherits(object, "dynrTaste") ) {
stop("dynrTaste object is required.")
}
stopifnot(numSubjDemo >= 1)
numSubjDemo <- as.integer(numSubjDemo)
conf_level <- object$conf.level
tstart <- object$tstart
chi_jnt <- object$chi.jnt
id <- object$id
id_unq <- unique(id)
id_n <- length(id_unq)
lat_name <- rownames(object$t.inn)
obs_name <- rownames(object$t.add)
lat_n <- length(lat_name)
obs_n <- length(obs_name)
if( is.null(names.state) ) {
lat_toplot <- lat_name
} else {
if ( !all(names.state %in% lat_name) ) {
stop("'names.state' should be a subset of the latent variables.")
}
lat_toplot <- names.state
}
if( is.null(names.observed) ) {
obs_toplot <- obs_name
} else {
if ( !all(names.observed %in% obs_name) ) {
stop("'names.observed' should be a subset of the observed variables.")
}
obs_toplot <- names.observed
}
if ( is.null(idtoPlot) ) {
chi_max <- vector("numeric", id_n)
for(i in 1:id_n){
begT <- tstart[i] + 1
endT <- tstart[i+1]
chi_max[i] <- max( chi_jnt[begT:endT] )
}
id_toplot <- id_unq[ order(chi_max, decreasing=TRUE) ][
1:numSubjDemo ]
} else {
id_toplot <- idtoPlot
if ( !all(id_toplot %in% id_unq) ) {
stop("Not all ID are in the data.")
}
}
### create ggplot objects
ggadd_hline <- function(yintercept) {
geom_hline(yintercept=yintercept,
colour="black", linetype="dashed", alpha=0.5)
}
ggadd_point <- function(data, aes_y) {
geom_point(data=data, aes_string(x="time", y=aes_y),
colour="red", size=2, alpha=0.5)
}
ggadd_theme <- function() {
theme(plot.title=element_text(size = rel(0.85)),
axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
panel.background=element_blank(),
plot.margin = margin(0, 0, 0, 0),
axis.line=element_line(colour = "black"))
}
## Joint chi-square
id_toplot_n <- length(id_toplot)
time <- object$time
chi_jnt_shk <- NULL
chi_df_jnt <- data.frame(id = id, time = time,
chi_jnt = object$chi.jnt,
chi_jnt_shk = object$chi.jnt.shock)
# subset data for id to plot
chi_df_jnt_plot <- subset(chi_df_jnt, is.element(id, id_toplot))
# subset data only for shocks
chi_df_jnt_shk <- subset(chi_df_jnt_plot, chi_jnt_shk)
qchi_jnt <- qchisq(conf_level, lat_n + obs_n)
plots_jnt <- lapply(id_toplot, function(id_i) {
chi_df_jnt_shk_i <- subset(chi_df_jnt_shk, id==id_i)
ggplot2::ggplot(subset(chi_df_jnt_plot, id==id_i),
aes(x=time, y=chi_jnt)) +
geom_line(alpha=0.5) +
labs(title="") +
ylab(expression(paste("Joint ", Chi^2))) +
list(ggadd_point(chi_df_jnt_shk_i, "chi_jnt"),
ggadd_hline(qchi_jnt), ggadd_theme())
})
#### Independend chi-square for innovative ####
chi_inn <- chi_inn_shk <- NULL
chi_df_inn <- data.frame(id = id, time = time,
chi_inn = object$chi.inn,
chi_inn_shk = object$chi.inn.shock)
chi_df_inn_plot <- subset(chi_df_inn, is.element(id, id_toplot))
chi_df_inn_shk <- subset(chi_df_inn_plot, chi_inn_shk)
qchi_inn <- qchisq(conf_level, lat_n)
plots_inn <- lapply(id_toplot, function(id_i) {
chi_df_inn_shk_i <- subset(chi_df_inn_shk, id==id_i)
ggplot2::ggplot(subset(chi_df_inn_plot, id==id_i),
aes(x=time, y=chi_inn)) +
geom_line(alpha=0.5) +
labs(title="") +
ylab(expression(paste("Indep. ", Chi^2, " - innovative"))) +
list(ggadd_point(chi_df_inn_shk_i, "chi_inn"),
ggadd_hline(qchi_inn), ggadd_theme())
})
#### Independent chi-square for additive ####
chi_add <- chi_add_shk <- NULL
chi_df_add <- data.frame(id = id, time = time,
chi_add = object$chi.add,
chi_add_shk = object$chi.add.shock)
chi_df_add_plot <- subset(chi_df_add, is.element(id, id_toplot))
chi_df_add_shk <- subset(chi_df_add_plot, chi_add_shk)
plots_add <- lapply(id_toplot, function(id_i) {
chi_df_add_shk_i <- subset(chi_df_add_shk, id==id_i)
ggplot2::ggplot(subset(chi_df_add_plot, id==id_i),
aes(x=time, y=chi_add)) +
geom_line(alpha=0.5) +
labs(title="") +
ylab(expression(paste("Indep. ", Chi^2, " - additive"))) +
list(ggadd_point(chi_df_add_shk_i, "chi_add"),
ggadd_hline(qchisq(conf_level, lat_n)), ggadd_theme())
})
#### t-statistic for innovative ####
t_inn <- object$t.inn
t_inn_name <- row.names(t_inn)
row.names(t_inn) <- paste0(t_inn_name, "_t")
t_df_inn <- data.frame(id = id, time = time,
t(t_inn), t(object$t.inn.shock))
t_df_inn_plot <- subset(t_df_inn, is.element(id, id_toplot))
plots_t_inn <- lapply(id_toplot, function(id_i) {
t_df_inn_plot_i <- subset(t_df_inn_plot, id==id_i)
lapply(lat_toplot, function(lat_i) {
t_df_inn_shk_i <- subset(t_df_inn_plot_i,
t_df_inn_plot_i[[lat_i]])
qt_i <- qt(conf_level, nrow(t_df_inn_plot_i) - obs_n)
ggplot2::ggplot(t_df_inn_plot_i,
aes_string(x="time", y=paste0(lat_i, "_t"))) +
geom_line(alpha=0.5) +
labs(title="") +
ylab(paste("t_[", lat_i, "]")) +
list(ggadd_point(t_df_inn_shk_i, paste0(lat_i, "_t")),
ggadd_hline(qt_i), ggadd_hline(-qt_i), ggadd_theme())
})
})
#### t-statistic for additive ####
t_add <- object$t.add
t_add_name <- row.names(t_add)
row.names(t_add) <- paste0(t_add_name, "_t")
t_df_add <- data.frame(id = id, time = time,
t(t_add), t(object$t.add.shock))
t_df_add_plot <- subset(t_df_add, is.element(id, id_toplot))
plots_t_add <- lapply(id_toplot, function(id_i) {
t_df_add_plot_i <- subset(t_df_add_plot, id==id_i)
t_add_df_i <- nrow(t_df_add_plot_i)
lapply(obs_toplot, function(obs_i) {
t_df_add_shk_i <- subset(t_df_add_plot_i,
t_df_add_plot_i[[obs_i]])
qt_i <- qt(conf_level, t_add_df_i - obs_n)
ggplot2::ggplot(t_df_add_plot_i,
aes_string(x="time", y=paste0(obs_i, "_t"))) +
geom_line(alpha=0.5) +
labs(title="") +
ylab(paste("t_[", obs_i, "]")) +
list(ggadd_point(t_df_add_shk_i, paste0(obs_i, "_t")),
ggadd_hline(qt_i), ggadd_hline(-qt_i), ggadd_theme())
})
})
gg_objects <- mapply(function(jnt_i, inn_i, add_i,
t_inn_i, t_add_i, id_toplot_i) {
gg_i <- c(list(jnt_i, inn_i, add_i), t_inn_i, t_add_i)
names(gg_i) <- c("chi_jnt", "chi_inn", "chi_add",
paste0("t_[", lat_toplot, "]"),
paste0("t_[", obs_toplot, "]"))
gg_i
},
plots_jnt, plots_inn, plots_add, plots_t_inn, plots_t_add, id_toplot,
SIMPLIFY=FALSE)
names(gg_objects) <- id_toplot
# if ( !is.null(filename) && is.character(filename) ) {
# gg_plots <- mapply(function(plots_i, id_toplot_i) {
# plots_arr <- ggpubr::ggarrange(
# plotlist=plots_i, nrow=length(plots_i), ncol=1, align="v")
# ggpubr::annotate_figure(
# plots_arr,
# top=ggpubr::text_grob(paste0("ID: ", id_toplot_i),
# color="black", face="bold", size=14),
# bottom=ggpubr::text_grob("time", color="black", size=11))
# },
# gg_objects, id_toplot,
# SIMPLIFY=FALSE)
#
# id_v <- chi_df_jnt_plot[["id"]]
# id_time_length <- vector("numeric", id_toplot_n)
# # time points for each id
# for (i in 1:id_toplot_n) {
# id_time_length[i] <- sum(is.element(id_v, id_toplot[i]))
# }
# # width: time/6, height: n of gg object * 2
# ggpubr::ggexport(gg_plots, filename=filename,
# width=mean(id_time_length)/6,
# height=length(gg_objects[[1]])*2)
# }
invisible(gg_objects)
}
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Defines functions autoplot.dynrTaste autoplot.dynrCook dynr.ggplot plotFormula dynr.plotFreq plotdf plot.dynrCook Mode findRegime multiplot
Documented in autoplot.dynrCook autoplot.dynrTaste dynr.ggplot dynr.plotFreq plot.dynrCook plotFormula
#------------------------------------------------------------------------------
# Plotting methods
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid::grid.newpage()
grid::pushViewport(grid::viewport(layout = grid::grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = grid::viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
findRegime = function(prop){
RegimeIndices = 1:length(prop)
MostLikelyRegime = RegimeIndices[prop==max(prop)]
return(MostLikelyRegime[1])
#If the probabilities are equal, return the first regime
}
Mode <- function(y) {
uy <- unique(y)
uy[which.max(tabulate(match(y, uy)))]
}
#Old plot function for dynrCook object
#setMethod("plot", "dynrCook",
# function(x, y=NULL, data.dynr, graphingPar=par(no.readonly = TRUE), ylab = #"Smoothed state values", xlab = "Time", numSubjDemo=2, legend.cex=1.2){
# opar = par(no.readonly = TRUE)
# par(graphingPar)
# thesmooth = data.frame(t(res@eta_smooth_final))
#
# dim_latent_var=dim(res@eta_smooth_final)[1]
# num_regime=dim(res@pr_t_given_T)[1]
#
# colnames(thesmooth) = paste0("state",1:dim_latent_var)
#
# ID = data.dynr[["id"]]
# rowIndex = 1:length(ID)
# uniID <- sort(unique(ID))
# thes = sort(sample(1:length(uniID), numSubjDemo))
#
# par(mfrow=c(ifelse(numSubjDemo%%2 > 0,
# numSubjDemo,numSubjDemo/2),
# ifelse(numSubjDemo%%2 > 0,
# 1,2)))
# if (num_regime > 1){
# thePr = t(x@pr_t_given_T)
# mostLikelyRegime = apply(thePr,1,findRegime)
# theR = Mode(mostLikelyRegime)
# }
# for (s in 1:numSubjDemo){
# T <- length(ID[ID %in% uniID[thes[s]]])
# therow = rowIndex[ID %in% uniID[thes[s]]]
# plot(
# c(1,T),
# c(min(thesmooth)-1, max(thesmooth)+quantile(unlist(thesmooth),.1)),
# ylab=ifelse(exists("ylab"),ylab,"State values"),
# xlab=ifelse(exists("xlab"),xlab,"Time"),
# main=ifelse(exists("main"),main,""),
# type='n')
# if (num_regime > 1){
# times=1:T
# thepri = mostLikelyRegime[therow]
# rect(times[thepri==theR]-.5,quantile(unlist(thesmooth),.01),times[th#epri==theR]+.5,quantile(unlist(thesmooth),.99),col="yellow",density=30)
# #legend('topleft',paste0("Regime",theR),
# #bty="n",cex=1.4,col=c(NA),fill=c("yellow"),
# #density=c(100))
# }
# time2 = if(T>500){
# seq(1,T,9)
# }else {1:T}
#
# for (j in 1:dim_latent_var){
# lines(1:T,thesmooth[therow,j], lty=1, lwd=1, col=j)
# points(time2, thesmooth[therow,j][time2], pch=as.character(j), col=j#)
# }
# stateNames = NULL
# for(j in 1:dim_latent_var){
# stateNames = c(stateNames, paste0('State ',j))
# }
# legend('topright',
# paste0("State",as.character(1:dim_latent_var)),
# lty=1:dim_latent_var,
# lwd=2, col=1:dim_latent_var, bty="n",
# pch=as.character(1:dim_latent_var), cex=legend.cex)
# }
# on.exit(par(opar))
# }
#)
##' Plot method for dynrCook objects
##'
##' @param x dynrCook object
##' @param dynrModel model object
##' @param style The style of the plot in the first panel. If style is 1 (default), user-selected smoothed state variables are plotted. If style is 2, user-selected observed-versus-predicted values are plotted.
##' @param names.state (optional) The names of the states to be plotted, which should be a subset of the state.names slot of the measurement slot of dynrModel.
##' @param names.observed (optional) The names of the observed variables to be plotted, which should be a subset of the obs.names slot of the measurement slot of dynrModel.
##' @param printDyn A logical value indicating whether or not to plot the formulas for the dynamic model
##' @param printMeas A logical value indicating whether or not to plot the formulas for the measurement model
##' @param textsize numeric. Font size used in the plot.
##' @param ... Further named arguments
##'
##' @return ggplot object.
##'
##' @details
##' This is a wrapper around \code{\link{dynr.ggplot}}. A great benefit of it is that it shows the model equations in a plot.
plot.dynrCook <- function(x, dynrModel, style = 1, names.state, names.observed, printDyn=TRUE, printMeas=TRUE, textsize=4, ...) {
#The first panel is the ggplot
if(missing(names.observed)){names.observed=dynrModel@measurement@obs.names}
if(missing(names.state)){names.state=dynrModel@measurement@state.names}
p1 <- dynr.ggplot(x, dynrModel, style, numSubjDemo=1, names.state=names.state, names.observed=names.observed, ...)
#If there are more than 2 regimes, the second panel shows the histogram of the most probable regimes across time and subjects.
if (dynrModel$num_regime>1){
regime <- NULL
highProbR <- data.frame(regime=apply(x@pr_t_given_T,2,which.max))
p2 <- ggplot2::ggplot(data = highProbR, ggplot2::aes(factor(regime))) +
ggplot2::geom_bar() +
ggplot2::scale_fill_brewer(palette = 3) +
ggplot2::xlab('Regime') + ggplot2::ylab('Counts') + ggplot2::labs(fill = '') +
ggplot2::ggtitle('Counts by most probable regime') +
ggplot2::theme(axis.title = ggplot2::element_text(size=14),
axis.text=ggplot2::element_text(size=12),
plot.title = ggplot2::element_text(size = 12, colour = "black", face = "bold", hjust = 0.5, vjust=0.01))
}
#The third panel plots the model formulae
p3 <- plotFormula(dynrModel, ParameterAs=sprintf("%.2f", x@transformed.parameters), printDyn=printDyn, printMeas=printMeas, textsize=textsize)
#Organize the panels using the multiplot function
if (dynrModel$num_regime > 1){
multiplot(p1, p2, p3, cols = 1, layout=matrix(c(1,1,2,3), nrow=2, byrow=TRUE))
}else{
multiplot(p1, p3, layout=matrix(c(1,1,2,2), nrow=2, byrow=TRUE))
}
}
plotdf <- function(vec_tex){
dataframe=data.frame(text=sapply(paste0("$",vec_tex,"$"),function(x){as.character(latex2exp::TeX(x))}))
dataframe$x<-0
return(dataframe)
}
##' Plot of the estimated frequencies of the regimes across all individuals and time points
##' based on their smoothed regime probabilities
##'
##' @param res The dynr object returned by dynr.cook().
##' @param dynrModel The model object to plot.
##' @param names.regime (optional) Names of the regimes (must match the length of the number of regimes)
##' @param title (optional) Title of the plot.
##' @param xlab (optional) Label of the x-axis.
##' @param ylab (optional) Label of the y-axis.
##' @param textsize (default = 12) Text size for the axis labels and title (= textsize + 2).
##' @param print (default = TRUE) A flag for whether the plot should be printed.
##'
##' @return ggplot object
dynr.plotFreq <- function(res, dynrModel, names.regime, title, xlab, ylab, textsize=12,print=TRUE) {
if(missing(names.regime)){names.regime<-paste0("Regime",1:dynrModel@num_regime)}
if(missing(title)){title<-'Counts by most probable regime'}
if(missing(xlab)){xlab<-'Regime'}
if(missing(ylab)){ylab<-'Counts'}
regime <- NULL
highProbR <- data.frame(regime=factor(apply(res@pr_t_given_T,2,which.max),
levels=1:dynrModel@num_regime,
labels=names.regime))
p2 <- ggplot2::ggplot(data = highProbR, ggplot2::aes(factor(regime))) +
ggplot2::geom_bar() +
ggplot2::scale_fill_brewer(palette = 3) +
ggplot2::xlab(xlab) + ggplot2::ylab(ylab) + ggplot2::labs(fill = '') +
ggplot2::ggtitle(title) +
ggplot2::theme(axis.title = ggplot2::element_text(size=textsize+2),
axis.text=ggplot2::element_text(size=textsize),
plot.title = ggplot2::element_text(size = textsize, colour = "black", face = "bold", hjust = 0.5, vjust=0.01))
if (print) print(p2)
return(p2)
}
##' Plot the formula from a model
##'
##' @param dynrModel The model object to plot.
##' @param ParameterAs The parameter values or names to plot. The underscores in parameter names are saved for
##' use of subscripts. Greek letters can be specified as corresponding LaTeX symbols without backslashes (e.g., "lambda")
##' and printed as greek letters.
##' @param printDyn A logical value indicating whether or not to plot the formulas for the dynamic model.
##' @param printMeas A logical value indicating whether or not to plot the formulas for the measurement model
##' @param printRS logical. Whether or not to print the regime-switching model. The default is FALSE.
##' @param textsize The text size use in the plot.
##'
##' @details
##' This function typesets a set of formulas that represent the model. Typical inputs to the \code{ParameterAs} argument are (1) the starting values for a model, (2) the final estimated values for a model, and (3) the parameter names. These are accessible with (1) \code{model$xstart}, (2) \code{coef(cook)}, and (3) \code{model$param.names} or \code{names(coef(cook))}, respectively.
##'
##' @return ggplot object
plotFormula <- function(dynrModel, ParameterAs, printDyn=TRUE, printMeas=TRUE,
printRS=FALSE, textsize=4){
dynrModel <- PopBackModel(dynrModel, LaTeXnames(ParameterAs, latex = FALSE))
#Dynamic model
if (printDyn) {
if ("dynrDynamicsMatrix" %in% class(dynrModel$dynamics)){
state.names <- (dynrModel$measurement)$state.names
exo.names <- (dynrModel$dynamics)$covariates
dynrModel@dynamics@values.dyn <- lapply((dynrModel$dynamics)$values.dyn, preProcessNames,state.names,state.names)
dynrModel@dynamics@values.exo <- lapply((dynrModel$dynamics)$values.exo, preProcessNames,state.names,exo.names)
dynrModel@dynamics@values.int <- lapply((dynrModel$dynamics)$values.int, preProcessNames,state.names)
}
dyn.df <- data.frame(text="bold('Dynamic Model')",x=0)
nRegime=ifelse(class(dynrModel@dynamics)=="dynrDynamicsFormula",
length(dynrModel@dynamics@formula),
length(dynrModel@dynamics@values.dyn))
dyn_tex=printex(dynrModel@dynamics,AsMatrix=FALSE)
nEq <- length(dyn_tex[[1]])
# noise_tex <- paste0(" + ", ifelse(dynrModel@dynamics@isContinuousTime, "d", ""), "w_{",1:nEq, "}(t)")
noise_tex <- paste0(ifelse(dynrModel@dynamics@isContinuousTime, "d", ""),
"w_{", 1:nEq, "}(t)")
for (i in 1:nRegime){
if (nRegime>1){
dyn.df <- rbind(dyn.df,data.frame(text=paste0("'Regime ",i,":'"),x=0))
}
if (length(dynrModel@noise@values.latent)==1){
values.latent.mat <- dynrModel@noise@values.latent[[1]]
}else if (length(dynrModel@noise@values.latent)==nRegime){
values.latent.mat <- dynrModel@noise@values.latent[[i]]
}else{stop("The number of regimes implied by the dynamic noise structure does not match the number of regimes in the dynamic model.")}
pos.zero <- diag(values.latent.mat) == 0
# noise_tex[diag(values.latent.mat)==0] <- ""
# dyn.df <- rbind(dyn.df,plotdf(LaTeXnames(paste0(dyn_tex[[i]], noise_tex))))
dyn.df <- rbind(dyn.df,
plotdf(LaTeXnames(
paste0(dyn_tex[[i]],
ifelse(pos.zero, "",
paste0(" + ", noise_tex))))),
plotdf(LaTeXnames(
paste0(ifelse(pos.zero, "",
paste0(noise_tex, " \\sim ", "N(0,\\,",
diag(values.latent.mat), ")"))))) )
}
} else {
dyn.df <- plotdf("")
}
#Measurement model
if (printMeas) {
meas.df <- data.frame(text="bold('Measurement Model')", x=0)
nRegime <- length(dynrModel$measurement$values.load)
meas_tex <- printex(dynrModel$measurement, AsMatrix=FALSE)
nEq <- length(meas_tex[[1]])
# noise_tex <- paste0(" + ", "epsilon_{", 1:nEq, "}")
noise_tex <- paste0("epsilon_{", 1:nEq, "}")
for (i in 1:nRegime){
if (nRegime > 1){
meas.df <- rbind(meas.df, data.frame(text=paste0("'Regime ",i,":'"), x=0))
}
if (length(dynrModel$noise$values.observed) == 1){
values.observed.mat <- dynrModel$noise$values.observed[[1]]
}else if (length(dynrModel$noise$values.observed) == nRegime){
values.observed.mat <- dynrModel$noise$values.observed[[i]]
}else{stop("The number of regimes implied by the measurement noise structure does not match the number of regimes in the measurement model.")}
pos.zero <- diag(values.observed.mat) == 0
# noise_tex[diag(values.observed.mat)==0] <- ""
# meas.df<-rbind(meas.df,plotdf(LaTeXnames(paste0(meas_tex[[i]], noise_tex))))
meas.df <- rbind(meas.df,
plotdf(LaTeXnames(
paste0(meas_tex[[i]],
ifelse(pos.zero, "", paste0(" + ", noise_tex, ",")),
"\\;\\,",
ifelse(pos.zero,
"",
paste0(noise_tex, " \\sim ", "N(0,\\,", diag(values.observed.mat), ")"))))))
}
} else {
meas.df <- plotdf("")
}
# Regime-switching model
if (printRS) {
rs.df <- data.frame(text="bold('Regime-switching Model')", x=0)
if (any(dim(dynrModel@regimes@params) == 0)) {
warning("No Regime-switching Model. Please turn off 'printRS'.")
} else {
# helper function to make sub. e.g., a_111 to a_{111}
to_sub <- function(parnames) {
idx_sub <- grepl('_', parnames)
if ( sum(idx_sub)==0 ) { return(parnames) }
subs <- parnames[idx_sub]
subs_spl <- strsplit(subs, '_')
subbed <- sapply(subs_spl, function(sub) {
paste0(sub[1], '_', paste0('{', sub[2], '}') )
})
parnames[idx_sub] <- subbed
parnames
}
covars <- dynrModel@regimes@covariates
if ( length(covars) == 0 ) {
covars <- c("")
} else {
covars <- c("", paste0("*", covars) )
}
covars_n <- length(covars)
# TODO: check nregime to fit with others
nregime <- nrow(dynrModel@regimes@params)
# column index for the intercepts of regimes
inter_col <- cumsum(c(1, rep(covars_n, nregime-2)))
# i:(i+ncov-1)
parnames <- to_sub( dynrModel@regimes@paramnames )
param_rs <- matrix(parnames, nregime)
param_inter <- param_rs[, inter_col, drop=FALSE]# intercepts
refrow <- dynrModel@regimes@refRow
if (length(refrow) != 0) {
for ( co in inter_col ) {
param_rs[-refrow, co] <-
paste0(param_rs[refrow, co], ' + ', param_rs[-refrow, co])
}
}
param_rs <- matrix(sweep(param_rs, 2, covars, FUN=paste0),
nrow=nregime)
ll <- vector('list', length=length(inter_col))
for ( i in 1:length(inter_col) ) {
ll[[i]] <-
param_rs[, inter_col[i]:(inter_col[i]+covars_n-1),
drop=FALSE]
}
sl <- sapply(ll, function(l) {
apply(l, 1, paste, collapse=" + ")
})
# transition probability subs
transub <- array(c(row(sl), col(sl)), c(nrow(sl), ncol(sl), 2))
transubed <- apply(transub, c(1,2), paste, collapse="")
rs.df <- rbind(rs.df,
plotdf(paste0("Log-odds(p_{",
transubed, "}) = ",
sl)))
}
} else {
rs.df <- plotdf("")
}
plot.df <- rbind(meas.df, dyn.df, rs.df)
plot.df$y=seq((dim(plot.df)[1]-1)*5+1, 1, by=-5)
x <- NULL
y <- NULL
fig<-ggplot2::ggplot(plot.df, ggplot2::aes(x=x, y=y, label=text))+
ggplot2::geom_text(parse=TRUE,size=textsize)+
ggplot2::theme_void() + ggplot2::ylim(min(plot.df$y)-5, max(plot.df$y)+5)
return(fig)
}
##' The ggplot of the smoothed state estimates and the most likely regimes
##'
##' @aliases autoplot.dynrCook
##'
##' @param res The dynr object returned by \code{dynr.cook()}.
##' @param dynrModel The model object to plot.
##' @param style The style of the plot. If style is 1 (default), user-selected smoothed state variables are plotted. If style is 2, user-selected observed-versus-predicted values are plotted.
##' @param numSubjDemo The number of subjects to be randomly selected for plotting.
##' @param idtoPlot Values of the ID variable to plot.
##' @param names.state (optional) The names of the states to be plotted, which should be a subset of the state.names slot of the measurement slot of dynrModel.
##' @param names.observed (optional) The names of the observed variables to be plotted, which should be a subset of the obs.names slot of the measurement slot of dynrModel.
##' @param names.regime (optional) The names of the regimes to be plotted, which can be missing.
##' @param shape.values (optional) A vector of values that correspond to the shapes of the points, which can be missing. See the R documentation on pch for details on possible shapes.
##' @param title (optional) A title of the plot.
##' @param ylab (optional) The label of the y axis.
##' @param is.bw Is plot in black and white? The default is FALSE.
##' @param colorPalette A color palette for lines and dots. It is a value passed to the palette argument of the \code{ggplot2::scale_colour_brewer()} function. These palettes are in the R package \pkg{RColorBrewer}. One can find them by attaching the package with \code{library(RColorBrewer)} and run \code{display.brewer.all()}.
##' @param fillPalette A color palette for blocks. It is a value passed to the palette argument of the \code{ggplot2::scale_fill_brewer()} function. These palettes are in the package \pkg{RColorBrewer}. One can find them by attaching the package with \code{library(RColorBrewer)} and run \code{display.brewer.all()}.
##' @param mancolorPalette (optional) A color palette for manually scaling the colors of lines and dots. It is a vector passed to the values argument of the \code{ggplot2::scale_colour_manual} function.
##' @param manfillPalette (optional) A color palette for manually scaling the colors of filled blocks. It is a vector passed to the values argument of the \code{ggplot2::scale_fill_manual} function.
##' @param ... A list of elements that modify the existing ggplot theme. Consult the \code{ggplot2::theme()} function in the R package \pkg{ggplot2} for more options.
##'
##' @details
##' This function outputs a ggplot layer that can be modified using functions in the package \pkg{ggplot2}. That is, one can add layers, scales, coords and facets with the "+" sign. In an example below, the \code{ggplot2::ylim()} function is used to modify the limits of the y axis of the graph. More details can be found on \url{https://ggplot2.tidyverse.org/} and \url{https://ggplot2.tidyverse.org/reference/}.
##'
##' The two functions \code{dynr.ggplot()} and \code{autoplot()} as identical aliases of one another. The \code{autoplot()} function is an S3 method from the package \pkg{ggplot2} that allows many objects to be plotted and works like the base \code{plot()} function.
##'
##' @return ggplot object
##'
##' @examples
##' # The following code is part of a demo example in dynr
##' \dontrun{
##' demo(RSLinearDiscreteYang, package='dynr')
##' p <- dynr.ggplot(yum, dynrModel = rsmod, style = 1,
##' names.regime = c("Deactivated", "Activated"),
##' title = "(B) Results from RS-AR model", numSubjDemo = 1,
##' shape.values = c(1),
##' text = element_text(size = 16),
##' is.bw = TRUE)
##' # One can modify the limits on the y axis by using '+'
##' p + ggplot2::ylim(-2, 4)
##'
##' autoplot(yum, dynrModel = rsmod, style = 1,
##' names.regime = c("Deactivated", "Activated"),
##' title = "(B) Results from RS-AR model", numSubjDemo = 1,
##' shape.values = c(1),
##' text = element_text(size = 16),
##' is.bw = TRUE)
##' }
dynr.ggplot <- function(res, dynrModel, style = 1,
numSubjDemo=2, idtoPlot=c(),
names.state,
names.observed,
names.regime,
shape.values,
title,
ylab,
is.bw=FALSE,
colorPalette="Set2",
fillPalette="Set2",
mancolorPalette, manfillPalette, ...){
data.dynr=dynrModel@data
dim_latent_var=dim(res@eta_smooth_final)[1]
num_regime=dim(res@pr_t_given_T)[1]
if(missing(names.observed)){names.observed=dynrModel@measurement@obs.names}
observed=which(dynrModel@measurement@obs.names%in%names.observed)
if ((length(observed)>8)&(missing(mancolorPalette))&(style==2)){stop("You provided too many variables than the default color palette can handle.\nPlease consider specify the mancolorPalette argument.")}
if(missing(names.state)){names.state=dynrModel@measurement@state.names}
states=which(dynrModel@measurement@state.names%in%names.state)
if(missing(shape.values)){
if (style==1) shape.values=states#48+states
if (style==2) shape.values=c(rep(32,length(observed)),observed)#pre-obs
}else{
if (style==2) shape.values=c(rep(32,length(shape.values)), shape.values)#pre-obs
}
if (style==1) line.values=rep(1,length(states))
if (style==2) line.values=rep(c(1,0),each=length(observed))#pre-obs
num_sbj=length(unique(data.dynr$id))
if(length(idtoPlot)<1){
randid =sample(unique(data.dynr$id),numSubjDemo)
}else {randid=idtoPlot}
if(missing(title)){
if (style==1) title=" "
if (style==2) title="Observed vs Predicted"
}
if(missing(ylab)){
if (style==1) ylab="Smoothed State Values"
if (style==2) ylab="Values"
}
data.plot<-data.frame(id=as.factor(data.dynr$id),time=data.dynr$time)
if(num_regime==1){
names.id.vars <- c("id","time")
if (style==1){
names.measure.vars <- names.state
data.plot <- cbind(data.plot, matrix(res@eta_smooth_final[states,],ncol=length(states),byrow=TRUE))
lines.var <- names.state
points.var <- names.state
}#end of style 1
if (style==2){
dynrModel=PopBackModel(dynrModel, LaTeXnames(res@transformed.parameters, latex = FALSE))
dynrModel@measurement@values.load=lapply(dynrModel@measurement@values.load, function(x){matrix(as.numeric(x),ncol=ncol(x))})
num_regime_meas <- length(dynrModel@measurement@values.load)
if (length(dynrModel@measurement@values.exo)!=0){
dynrModel@measurement@values.exo=lapply(dynrModel@measurement@values.exo, function(x){matrix(as.numeric(x),ncol=ncol(x))})
}
if (length(dynrModel@measurement@values.int)!=0){
dynrModel@measurement@values.int=lapply(dynrModel@measurement@values.int, function(x){matrix(as.numeric(x),ncol=ncol(x))})
}
#predicted scores
if (num_regime_meas==1){#one-regime measurement model
predicted=dynrModel@measurement@values.load[[1]]%*%res@eta_smooth_final
if (length(dynrModel@measurement@values.int)!=0){
predicted=predicted+dynrModel@measurement@values.int[[1]]%*%matrix(rep(1,ncol(res@eta_smooth_final)), nrow=1)
}
if (length(dynrModel@measurement@values.exo)!=0){
exo.mat=t(as.matrix(dynrModel@data$covariates[,paste0("covar",sapply(dynrModel@measurement@exo.names, function(x){which(dynrModel@data$covariate.names==x)}))]))
predicted=predicted+dynrModel@measurement@values.exo[[1]]%*%exo.mat
}
}#end of one-regime meas
names.measure.vars <- c(paste0(names.observed, ".predicted"), paste0(names.observed, ".observed"))
data.plot <- cbind(data.plot, matrix(predicted[observed,],ncol=length(observed),byrow=TRUE, dimnames=list(NULL, paste0(names.observed, ".predicted"))))
data.plot <- cbind(data.plot, data.dynr$observed[,observed])
lines.var <- paste0(names.observed, ".predicted")
points.var <- paste0(names.observed, ".observed")
if (length(observed)<8){
fancy8 <- c("#edaac1","#a7dab0","#dfb3e2","#8bc5ed","#bcb8ec","#e6b296","#d2d39d","#82d9d6")
mancolorPalette=rep(fancy8[observed],2)
}
}#end of style 2
names(data.plot) <- c(names.id.vars, names.measure.vars)
data_long <- reshape2::melt(data.plot[data.plot$id%in%randid,],
id.vars=names.id.vars,
measure.vars=names.measure.vars,
value.name="value")
data_long$variable<-factor(data_long$variable, levels=sort(names.measure.vars))
value <- NULL
variable <- NULL
partial.plot<-ggplot2::ggplot(data_long, ggplot2::aes(x=time, y=value, colour = variable, shape = variable, linetype = variable)) +
ggplot2::geom_line(data=data_long[data_long$variable%in%lines.var,], size=1) +
ggplot2::geom_point(data=data_long[data_long$variable%in%points.var,], size=4) +
ggplot2::scale_linetype_manual(labels=sort(names.measure.vars), values=line.values[order(names.measure.vars)])+
ggplot2::scale_shape_manual(labels=sort(names.measure.vars), values=shape.values[order(names.measure.vars)])+
ggplot2::facet_wrap(~id)+
ggplot2::labs(title = title, y=ylab, ggtitle="")+
ggplot2::theme(plot.title=ggplot2::element_text(lineheight=.8, face="bold", hjust = 0.5, vjust=0.01), legend.position="bottom",legend.text=ggplot2::element_text(lineheight=0.8, face="bold"),...)
}else{#more than two regimes
if(missing(names.regime)){names.regime = 1:num_regime}
findRegime = function(prop){
MostLikelyRegime = names.regime[prop==max(prop)]
return(MostLikelyRegime[1])
#If the probabilities are equal, return the first regime
}
addendtime <- function(data_frame){
data_frame$endtime <- c(data_frame$time[2:nrow(data_frame)], data_frame$time[nrow(data_frame)]+min(diff(data_frame$time)))
return(data_frame)
}
data.plot <- plyr::ddply(data.plot,"id",addendtime)
data.plot$regime<-as.factor(apply(res@pr_t_given_T,2,findRegime))
names.id.vars<-c("id","time","endtime","regime")
if (style==1){
names.measure.vars<-names.state
data.plot<-cbind(data.plot,matrix((res@eta_smooth_final[states,]),ncol=length(states),byrow=TRUE))
lines.var <- names.state
points.var <- names.state
}#end of style 1
if (style==2){
dynrModel=PopBackModel(dynrModel, LaTeXnames(res@transformed.parameters, latex = FALSE))
dynrModel@measurement@values.load=lapply(dynrModel@measurement@values.load, function(x){matrix(as.numeric(x),ncol=ncol(x))})
num_regime_meas <- length(dynrModel@measurement@values.load)
if (length(dynrModel@measurement@values.exo)!=0){
dynrModel@measurement@values.exo=lapply(dynrModel@measurement@values.exo, function(x){matrix(as.numeric(x),ncol=ncol(x))})
}
if (length(dynrModel@measurement@values.int)!=0){
dynrModel@measurement@values.int=lapply(dynrModel@measurement@values.int, function(x){matrix(as.numeric(x),ncol=ncol(x))})
}
#predicted scores
if (num_regime_meas==1){#one-regime measurement model
predicted=matrix(dynrModel@measurement@values.load[[1]][observed,],nrow=length(observed))%*%res@eta_smooth_final
if (length(dynrModel@measurement@values.int)!=0){
predicted=predicted+matrix(dynrModel@measurement@values.int[[1]][observed,],nrow=length(observed))%*%matrix(rep(1,ncol(res@eta_smooth_final)), nrow=1)
}
if (length(dynrModel@measurement@values.exo)!=0){
exo.mat=t(as.matrix(dynrModel@data$covariates[,paste0("covar",sapply(dynrModel@measurement@exo.names, function(x){which(dynrModel@data$covariate.names==x)}))]))
predicted=predicted+matrix(dynrModel@measurement@values.exo[[1]][observed,],nrow=length(observed))%*%exo.mat
}
#end of one-regime meas
}else if (num_regime_meas==num_regime){
ntotaltime=ncol(res@eta_smooth_final)
predicted=matrix(rep(0,length(observed)*ntotaltime),ncol=ntotaltime)
for (index in 1:ntotaltime){
predicted[,index]=matrix(dynrModel@measurement@values.load[[data.plot$regime[index]]][observed,],nrow=length(observed))%*%matrix(res@eta_smooth_final[,index], ncol=1)
if (length(dynrModel@measurement@values.int)!=0){
predicted[,index]=matrix(predicted[,index]+dynrModel@measurement@values.int[[data.plot$regime[index]]][observed,],nrow=length(observed))
}
if (length(dynrModel@measurement@values.exo)!=0){
exo.mat=t(as.matrix(dynrModel@data$covariates[index,paste0("covar",sapply(dynrModel@measurement@exo.names, function(x){which(dynrModel@data$covariate.names==x)}))]))
predicted[,index]=matrix(predicted[,index]+dynrModel@measurement@values.exo[[data.plot$regime[index]]][observed,],nrow=length(observed))%*%exo.mat
}
}
#end of regime specific measurement
}
names.measure.vars <- c(paste0(names.observed, ".predicted"), paste0(names.observed, ".observed"))
data.plot <- cbind(data.plot, matrix(predicted,ncol=length(observed), byrow=TRUE, dimnames=list(NULL, paste0(names.observed, ".predicted"))))
data.plot <- cbind(data.plot, data.dynr$observed[,observed])
lines.var <- paste0(names.observed, ".predicted")
points.var <- paste0(names.observed, ".observed")
if (length(observed)<=8){
fancy8 <- c("#edaac1","#a7dab0","#dfb3e2","#8bc5ed","#bcb8ec","#e6b296","#d2d39d","#82d9d6")
manfillPalette=fancy8[9-(1:num_regime)]
mancolorPalette=rep(fancy8[observed],2)
}
}#end of style 2
names(data.plot)<-c(names.id.vars,names.measure.vars)
data_long<- reshape2::melt(data.plot[data.plot$id%in%randid,],
id.vars=names.id.vars,
measure.vars=names.measure.vars,
value.name="value")
data_long$variable<-factor(data_long$variable, levels=sort(names.measure.vars))
#data_long$statenumber<-as.factor(sub("state","",data_long$variable))
endtime <- NULL
regime <- NULL
partial.plot<-ggplot2::ggplot(data_long,ggplot2::aes(x=time, y=value, group=variable)) +
ggplot2::geom_rect(ggplot2::aes(xmin=time, xmax=endtime, ymin=-Inf, ymax=Inf, fill=regime), alpha=.15) +
ggplot2::geom_line(data=data_long[data_long$variable%in%lines.var,], size=1, ggplot2::aes(color=variable, linetype=variable)) +
ggplot2::geom_point(data=data_long[data_long$variable%in%points.var,], size=3, ggplot2::aes(color=variable, shape=variable)) +
ggplot2::scale_linetype_manual(labels=sort(names.measure.vars), values=line.values[order(names.measure.vars)])+
ggplot2::scale_shape_manual(labels=sort(names.measure.vars), values=shape.values[order(names.measure.vars)])+
#geom_text(size=1, ggplot2::aes(label=statenumber,color=variable))+
ggplot2::facet_wrap(~id)+
ggplot2::labs(title = title,y=ylab,ggtitle="")+
ggplot2::theme(plot.title=ggplot2::element_text(lineheight=.8, face="bold", hjust = 0.5, vjust=0.01), legend.position="bottom",legend.text=ggplot2::element_text(lineheight=0.8, face="bold"),...)
}
if(is.bw){
bw.plot<-partial.plot+
ggplot2::scale_fill_grey(start = 0.1, end = 0.9)+
ggplot2::scale_color_grey(labels=sort(names.measure.vars), start = 0.2, end = 0.7)
return(bw.plot)
}else{
default.plot<-partial.plot+
ggplot2::scale_colour_brewer(labels=sort(names.measure.vars), palette=colorPalette)+
ggplot2::scale_fill_brewer(palette=fillPalette)
if(!missing(mancolorPalette)){
manual.plot<-partial.plot+ggplot2::scale_colour_manual(labels=sort(names.measure.vars), values=mancolorPalette[order(names.measure.vars)])
}
if(!missing(manfillPalette)){
if(!exists("manual.plot")){manual.plot<-partial.plot}
manual.plot<-manual.plot+ggplot2::scale_fill_manual(values=manfillPalette)
}
if (exists("manual.plot")){
return(manual.plot) #print(manual.plot)
}else{
return(default.plot)#print(default.plot)
}
}
}
##' @rdname dynr.ggplot
##'
##' @param object The same as res. The dynr object returned by dynr.cook().
##'
##' @return ggplot object
autoplot.dynrCook <- function(object, dynrModel, style = 1,
numSubjDemo=2, idtoPlot=c(),
names.state,
names.observed,
names.regime,
shape.values,
title,
ylab,
is.bw=FALSE,
colorPalette="Set2",
fillPalette="Set2",
mancolorPalette, manfillPalette, ...){
dynr.ggplot(object, dynrModel=dynrModel, style = style,
numSubjDemo=numSubjDemo, idtoPlot=idtoPlot,
names.state=names.state,
names.observed=names.observed,
names.regime=names.regime,
shape.values=shape.values,
title=title,
ylab=ylab,
is.bw=is.bw,
colorPalette=colorPalette,
fillPalette=fillPalette,
mancolorPalette=mancolorPalette, manfillPalette=manfillPalette, ...)
}
##' The ggplot of the outliers estimates.
##'
##' @param object A dynrTaste object.
##' @param numSubjDemo The number of subjects, who have
##' largest joint chi-square statistic, to be selected for plotting.
##' @param idtoPlot Values of the ID variable to plot.
##' @param names.state (optional) The names of the states to be plotted, which should be a subset of the state.names slot of the measurement slot of dynrModel. If NULL, the t statistic plots for all state variables will be included.
##' @param names.observed (optional) The names of the observed variables to be plotted, which should be a subset of the obs.names slot of the measurement slot of dynrModel. If NULL, the t statistic plots for all observed variables will be included.
##' @param ... Place holder for other arguments. Please do not use.
##'
##' @return a list of ggplot objects for each ID.
##' The plots of chi-square statistics (joint and independent),
##' and the plots of t statistic for \code{names.state} and \code{names.observed} will be included.
##' Users can modify the ggplot objects using ggplot grammar.
##' If a \code{filename} is provided, a pdf of plots will be saved additionally.
autoplot.dynrTaste <- function(object,
numSubjDemo=2, idtoPlot=NULL,
names.state=NULL, names.observed=NULL, ...) {
if ( !inherits(object, "dynrTaste") ) {
stop("dynrTaste object is required.")
}
stopifnot(numSubjDemo >= 1)
numSubjDemo <- as.integer(numSubjDemo)
conf_level <- object$conf.level
tstart <- object$tstart
chi_jnt <- object$chi.jnt
id <- object$id
id_unq <- unique(id)
id_n <- length(id_unq)
lat_name <- rownames(object$t.inn)
obs_name <- rownames(object$t.add)
lat_n <- length(lat_name)
obs_n <- length(obs_name)
if( is.null(names.state) ) {
lat_toplot <- lat_name
} else {
if ( !all(names.state %in% lat_name) ) {
stop("'names.state' should be a subset of the latent variables.")
}
lat_toplot <- names.state
}
if( is.null(names.observed) ) {
obs_toplot <- obs_name
} else {
if ( !all(names.observed %in% obs_name) ) {
stop("'names.observed' should be a subset of the observed variables.")
}
obs_toplot <- names.observed
}
if ( is.null(idtoPlot) ) {
chi_max <- vector("numeric", id_n)
for(i in 1:id_n){
begT <- tstart[i] + 1
endT <- tstart[i+1]
chi_max[i] <- max( chi_jnt[begT:endT] )
}
id_toplot <- id_unq[ order(chi_max, decreasing=TRUE) ][
1:numSubjDemo ]
} else {
id_toplot <- idtoPlot
if ( !all(id_toplot %in% id_unq) ) {
stop("Not all ID are in the data.")
}
}
### create ggplot objects
ggadd_hline <- function(yintercept) {
geom_hline(yintercept=yintercept,
colour="black", linetype="dashed", alpha=0.5)
}
ggadd_point <- function(data, aes_y) {
geom_point(data=data, aes_string(x="time", y=aes_y),
colour="red", size=2, alpha=0.5)
}
ggadd_theme <- function() {
theme(plot.title=element_text(size = rel(0.85)),
axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
panel.background=element_blank(),
plot.margin = margin(0, 0, 0, 0),
axis.line=element_line(colour = "black"))
}
## Joint chi-square
id_toplot_n <- length(id_toplot)
time <- object$time
chi_jnt_shk <- NULL
chi_df_jnt <- data.frame(id = id, time = time,
chi_jnt = object$chi.jnt,
chi_jnt_shk = object$chi.jnt.shock)
# subset data for id to plot
chi_df_jnt_plot <- subset(chi_df_jnt, is.element(id, id_toplot))
# subset data only for shocks
chi_df_jnt_shk <- subset(chi_df_jnt_plot, chi_jnt_shk)
qchi_jnt <- qchisq(conf_level, lat_n + obs_n)
plots_jnt <- lapply(id_toplot, function(id_i) {
chi_df_jnt_shk_i <- subset(chi_df_jnt_shk, id==id_i)
ggplot2::ggplot(subset(chi_df_jnt_plot, id==id_i),
aes(x=time, y=chi_jnt)) +
geom_line(alpha=0.5) +
labs(title="") +
ylab(expression(paste("Joint ", Chi^2))) +
list(ggadd_point(chi_df_jnt_shk_i, "chi_jnt"),
ggadd_hline(qchi_jnt), ggadd_theme())
})
#### Independend chi-square for innovative ####
chi_inn <- chi_inn_shk <- NULL
chi_df_inn <- data.frame(id = id, time = time,
chi_inn = object$chi.inn,
chi_inn_shk = object$chi.inn.shock)
chi_df_inn_plot <- subset(chi_df_inn, is.element(id, id_toplot))
chi_df_inn_shk <- subset(chi_df_inn_plot, chi_inn_shk)
qchi_inn <- qchisq(conf_level, lat_n)
plots_inn <- lapply(id_toplot, function(id_i) {
chi_df_inn_shk_i <- subset(chi_df_inn_shk, id==id_i)
ggplot2::ggplot(subset(chi_df_inn_plot, id==id_i),
aes(x=time, y=chi_inn)) +
geom_line(alpha=0.5) +
labs(title="") +
ylab(expression(paste("Indep. ", Chi^2, " - innovative"))) +
list(ggadd_point(chi_df_inn_shk_i, "chi_inn"),
ggadd_hline(qchi_inn), ggadd_theme())
})
#### Independent chi-square for additive ####
chi_add <- chi_add_shk <- NULL
chi_df_add <- data.frame(id = id, time = time,
chi_add = object$chi.add,
chi_add_shk = object$chi.add.shock)
chi_df_add_plot <- subset(chi_df_add, is.element(id, id_toplot))
chi_df_add_shk <- subset(chi_df_add_plot, chi_add_shk)
plots_add <- lapply(id_toplot, function(id_i) {
chi_df_add_shk_i <- subset(chi_df_add_shk, id==id_i)
ggplot2::ggplot(subset(chi_df_add_plot, id==id_i),
aes(x=time, y=chi_add)) +
geom_line(alpha=0.5) +
labs(title="") +
ylab(expression(paste("Indep. ", Chi^2, " - additive"))) +
list(ggadd_point(chi_df_add_shk_i, "chi_add"),
ggadd_hline(qchisq(conf_level, lat_n)), ggadd_theme())
})
#### t-statistic for innovative ####
t_inn <- object$t.inn
t_inn_name <- row.names(t_inn)
row.names(t_inn) <- paste0(t_inn_name, "_t")
t_df_inn <- data.frame(id = id, time = time,
t(t_inn), t(object$t.inn.shock))
t_df_inn_plot <- subset(t_df_inn, is.element(id, id_toplot))
plots_t_inn <- lapply(id_toplot, function(id_i) {
t_df_inn_plot_i <- subset(t_df_inn_plot, id==id_i)
lapply(lat_toplot, function(lat_i) {
t_df_inn_shk_i <- subset(t_df_inn_plot_i,
t_df_inn_plot_i[[lat_i]])
qt_i <- qt(conf_level, nrow(t_df_inn_plot_i) - obs_n)
ggplot2::ggplot(t_df_inn_plot_i,
aes_string(x="time", y=paste0(lat_i, "_t"))) +
geom_line(alpha=0.5) +
labs(title="") +
ylab(paste("t_[", lat_i, "]")) +
list(ggadd_point(t_df_inn_shk_i, paste0(lat_i, "_t")),
ggadd_hline(qt_i), ggadd_hline(-qt_i), ggadd_theme())
})
})
#### t-statistic for additive ####
t_add <- object$t.add
t_add_name <- row.names(t_add)
row.names(t_add) <- paste0(t_add_name, "_t")
t_df_add <- data.frame(id = id, time = time,
t(t_add), t(object$t.add.shock))
t_df_add_plot <- subset(t_df_add, is.element(id, id_toplot))
plots_t_add <- lapply(id_toplot, function(id_i) {
t_df_add_plot_i <- subset(t_df_add_plot, id==id_i)
t_add_df_i <- nrow(t_df_add_plot_i)
lapply(obs_toplot, function(obs_i) {
t_df_add_shk_i <- subset(t_df_add_plot_i,
t_df_add_plot_i[[obs_i]])
qt_i <- qt(conf_level, t_add_df_i - obs_n)
ggplot2::ggplot(t_df_add_plot_i,
aes_string(x="time", y=paste0(obs_i, "_t"))) +
geom_line(alpha=0.5) +
labs(title="") +
ylab(paste("t_[", obs_i, "]")) +
list(ggadd_point(t_df_add_shk_i, paste0(obs_i, "_t")),
ggadd_hline(qt_i), ggadd_hline(-qt_i), ggadd_theme())
})
})
gg_objects <- mapply(function(jnt_i, inn_i, add_i,
t_inn_i, t_add_i, id_toplot_i) {
gg_i <- c(list(jnt_i, inn_i, add_i), t_inn_i, t_add_i)
names(gg_i) <- c("chi_jnt", "chi_inn", "chi_add",
paste0("t_[", lat_toplot, "]"),
paste0("t_[", obs_toplot, "]"))
gg_i
},
plots_jnt, plots_inn, plots_add, plots_t_inn, plots_t_add, id_toplot,
SIMPLIFY=FALSE)
names(gg_objects) <- id_toplot
# if ( !is.null(filename) && is.character(filename) ) {
# gg_plots <- mapply(function(plots_i, id_toplot_i) {
# plots_arr <- ggpubr::ggarrange(
# plotlist=plots_i, nrow=length(plots_i), ncol=1, align="v")
# ggpubr::annotate_figure(
# plots_arr,
# top=ggpubr::text_grob(paste0("ID: ", id_toplot_i),
# color="black", face="bold", size=14),
# bottom=ggpubr::text_grob("time", color="black", size=11))
# },
# gg_objects, id_toplot,
# SIMPLIFY=FALSE)
#
# id_v <- chi_df_jnt_plot[["id"]]
# id_time_length <- vector("numeric", id_toplot_n)
# # time points for each id
# for (i in 1:id_toplot_n) {
# id_time_length[i] <- sum(is.element(id_v, id_toplot[i]))
# }
# # width: time/6, height: n of gg object * 2
# ggpubr::ggexport(gg_plots, filename=filename,
# width=mean(id_time_length)/6,
# height=length(gg_objects[[1]])*2)
# }
invisible(gg_objects)
}
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