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0.functions.R
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library(plyr)
rename_cycle <- function(dataset){
names2 <- colnames(dataset)
for (idv in 1:length(names2)) {
if (substr(names2[idv],1,3) %in% c("IS2","IS3","ES2","ES3","LS2","LS3","IT2","IT3","IC2","IC3")) {
names2[idv] <- paste0(substr(names2[idv],1,2),substr(names2[idv],4,nchar(names2[idv])))
}
if (substr(names2[idv],1,2) %in% c()) {
names2[idv] <- paste0(substr(names2[idv],1,2),substr(names2[idv],4,nchar(names2[idv])))
}
}
dataset_r <- dataset %>% rename_at(vars(colnames(dataset)), ~ names2)
}
subchunkify <- function(g, name, fig_height = 7, fig_width = 5) {
g_deparsed <- paste0(deparse(
function() {g}
), collapse = '')
sub_chunk <- paste0("
`","``{r sub_chunk_", name, ", fig.height = ", fig_height, ", fig.width = ", fig_width, ", echo = FALSE}",
"\n(",
g_deparsed
, ")()",
"\n`","``
")
cat(knitr::knit(text = knitr::knit_expand(text = sub_chunk), quiet = TRUE))
}
#------Graph polCA models classes----------
graphclass <- function(cmodel = NULL, nclass = NULL, orden = c(1:length(levels(cmodel$param))), title = NULL){
a <- levels(cmodel$param)
a <- a[order(a)[orden]]
labels <- NULL
for (each in a){
labels[each] <- paste(str_remove(attr(ISC_lvRlca[[each]], "label"),
"Rights and Responsibilities/Rights and responsibilities/|Rights and Responsibilities/Roles women and men/|Moving/<Immigrants> |Moving/<Immigrant> "),
"-", each)
}
labels2 <- NULL
n <- 0
for (each in levels(cmodel$category)){
n <- n + 1
labels2[each] <- paste(each, "-", levels(ISC_lvRlca[[cmodel[cmodel$category == each, "param"][1]]])[n])
}
cmodel$paramf <- factor(cmodel$param, levels = a, labels = labels)
cmodel$categoryf <- factor(cmodel$category, levels = levels(cmodel$category), labels = labels2)
zp1 <- ggplot(cmodel,aes(x = paramf, y = value, fill = categoryf)) +
geom_bar(stat = "identity", position = "stack") +
facet_grid(Class ~ ., labeller = label_wrap_gen(16)) +
#scale_fill_brewer(type="seq", palette="Greys") +
theme_bw() +
ggtitle(title) +
labs(x = "Items", y = "Response probabilities", fill ="Response category") +
theme(legend.position = "top", title = element_text(size=9),
strip.text.y = element_text(size = 8),
axis.text.x=element_text(angle = 90, vjust = 0.5, hjust = 1, size = 6),
axis.text.y=element_text(size = 7),
axis.title = element_text(size = 7),
axis.ticks.y=element_blank(),
panel.grid.major.y=element_blank(), legend.title = element_text(size = 8),
legend.key.size = unit(0.3, "cm"),
legend.text = element_text(size = 8)) +
scale_x_discrete(label = function(x) str_wrap(x,25)) +
guides(fill=guide_legend(nrow=2,byrow=TRUE)) +
scale_y_continuous(breaks = c(0.25,0.5,0.75)) +
geom_hline(yintercept=c(0.25,0.5,0.75), linetype = "dashed", size = 0.3, color = "gray")
print(zp1)
}
graphclasstogether <- function(cmodel = NULL, nclass = NULL, orden = c(1:length(levels(cmodel$param))), title = NULL){
a <- levels(cmodel$param)
a <- a[order(a)[orden]]
labels <- NULL
for (each in a){
labels[each] <- paste(str_remove(attr(ISC_lvRlca[[each]], "label"),
"Rights and Responsibilities/Rights and responsibilities/|Rights and Responsibilities/Roles women and men/|Moving/<Immigrants> |Moving/<Immigrant> "),
"-", each)
}
labels2 <- NULL
n <- 0
for (each in levels(cmodel$category)){
n <- n + 1
labels2[each] <- paste(each, "-", levels(ISC_lvRlca[[cmodel[cmodel$category == each, "param"][1]]])[n])
}
cmodel$paramf <- factor(cmodel$param, levels = a, labels = labels)
cmodel$categoryf <- factor(cmodel$category, levels = levels(cmodel$category), labels = labels2)
zp1 <- ggplot(cmodel,aes(x = paramf, y = value, fill = categoryf)) +
geom_bar(stat = "identity", position = "stack") +
facet_grid(Class ~ Cycle) +
#scale_fill_brewer(type="seq", palette="Greys") +
theme_bw() +
ggtitle(title) +
labs(x = "Items", y = "Response probabilities", fill ="Response category") +
theme(legend.position = "top", title = element_text(size=9),
axis.text.x=element_text(angle = 90, vjust = 0, hjust = 1, size = 7),
axis.text.y=element_text(size = 7),
axis.title = element_text(size = 7),
axis.ticks.y=element_blank(),
panel.grid.major.y=element_blank(), legend.title = element_text(size = 8),
legend.key.size = unit(0.3, "cm"),
legend.text = element_text(size = 8)) +
scale_x_discrete(label = function(x) str_wrap(x,25)) +
guides(fill=guide_legend(nrow=2,byrow=TRUE)) +
scale_y_continuous(breaks = c(0.25,0.5,0.75)) +
geom_hline(yintercept=c(0.25,0.5,0.75), linetype = "dashed", size = 0.3, color = "gray")
print(zp1)
}
g_legend<-function(a.gplot){
tmp <- ggplot_gtable(ggplot_build(a.gplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)}
entropy <- function(p) sum(-p * log(p))
#------Summary of polCA models----------
summaryLCAR2 <- function(Modellist, level1 = 1:3, level2 = 1:5, level3 = 1:5,data = "EU"){
l0 <- 0
Fits <- vector(mode = 'list', length = length(level1))
Sizes <- vector(mode = 'list', length = length(level1))
if (all(!is.na(level3))){
for (l1 in level1) {#cycles#country
l0 <- l0 + 1
if(data == "NEU" & l1 == 3){
level2 <- 2
} else{
if(l0 == l1){
level2 <- length(names(Modellist[[l1]]))
} else level2 <- length(names(Modellist[level1][[l0]]))
}
for (l2 in 1:level2) {# scales#years
fit <- data.frame(L1 = NULL, L2 = NULL, NClass = NULL, Chisq = NULL, npar = NULL, loglik = NULL,
Gsq = NULL, BIC = NULL, AIC = NULL)
sizes <- data.frame(matrix(NA,1,2*max(level3)+3))
names(sizes) <- c("L1", "L2", "NClass", paste("P", 1:max(level3), sep = "."),
paste("Pse", 1:max(level3), sep = "."))
for(l3 in level3){#nclasses
fit[l3, "L1"] <- names(Modellist[l1])
fit[l3, "L2"] <- names(Modellist[l1][[1]])[l2]
fit[l3, "NClass"] <- l3
fit[l3, "Chisq"] <- Modellist[[l1]][[l2]][[l3]]$Chisq
fit[l3, "npar"] <- Modellist[[l1]][[l2]][[l3]]$npar
fit[l3, "loglik"] <- Modellist[[l1]][[l2]][[l3]]$llik
fit[l3, "Gsq"] <- Modellist[[l1]][[l2]][[l3]]$Gsq
fit[l3, "BIC"] <- Modellist[[l1]][[l2]][[l3]]$bic
fit[l3, "AIC"] <- Modellist[[l1]][[l2]][[l3]]$aic
if(l3 != 1) {
posteriors <- data.frame(Modellist[[l1]][[l2]][[l3]]$posterior, predclass=Modellist[[l1]][[l2]][[l3]]$predclass)
classification_table <- as.matrix(ddply(posteriors, .(predclass), function(x) colSums(x[,1:l3])) )
fit[l3, "ClassErr"] <- round(1-sum(diag(classification_table[,c(2:(l3+1))])) / sum(classification_table[,c(2:(l3+1))]),3)
error_prior <- entropy(Modellist[[l1]][[l2]][[l3]]$P) # Class proportions
error_post <- mean(apply(Modellist[[l1]][[l2]][[l3]]$posterior, 1, entropy), na.rm = TRUE)
fit[l3, "R2_entropy"] <- round((error_prior - error_post) / error_prior,3)
}
sizes[l3, "L1"] <- names(Modellist[l1])
sizes[l3, "L2"] <- names(Modellist[l1][[1]])[l2]
sizes[l3, "NClass"] <- l3
sizes[l3, names(sizes) %in% paste("P", 1:l3, sep = ".")] <- round(Modellist[[l1]][[l2]][[l3]]$P,3)
if(l3 != 1) sizes[l3, names(sizes) %in% paste("Pse", 1:l3, sep = ".")] <- round(Modellist[[l1]][[l2]][[l3]]$P.se,3)
}
fit$chg_Gsq <- round((fit$Gsq - fit$Gsq[1])/fit$Gsq[1],3)
Fits[[l0]][[l2]] <- fit %>% dplyr::select(L1, L2, NClass, Chisq, npar, loglik, Gsq, chg_Gsq, BIC, AIC, R2_entropy, ClassErr)
Sizes[[l0]][[l2]] <- sizes[level3,]
}
if(data == "NEU" & l1 == 3){
names(Fits[[l0]]) <- names(Modellist[l1][[1]])[c(1,2)]
names(Sizes[[l0]]) <- names(Modellist[l1][[1]])[c(1,2)]} else {
names(Fits[[l0]]) <- names(Modellist[l1][[1]])
names(Sizes[[l0]]) <- names(Modellist[l1][[1]])
}
names(Fits) <- names(Modellist)[level1]
names(Sizes) <- names(Modellist)[level1]
}
} else if (all(!is.na(level2) & is.na(level3))){
for (l11 in level1) {
l0 <- l0 + 1
fit <- data.frame(L1 = NULL, NClass = NULL, Chisq = NULL, npar = NULL, loglik = NULL, Gsq = NULL, BIC = NULL, AIC = NULL)
sizes <- data.frame(matrix(NA,1,2*max(level2)+2))
names(sizes) <- c("L1", "NClass", paste("P", 1:max(level2), sep = "."),
paste("Pse", 1:max(level2), sep = "."))
for (l21 in 1:max(level2)) {
fit[l21, "L1"] <- names(Modellist[l11])
fit[l21, "NClass"] <- l21
fit[l21, "Chisq"] <- Modellist[[l11]][[l21]]$Chisq
fit[l21, "npar"] <- Modellist[[l11]][[l21]]$npar
fit[l21, "loglik"] <- Modellist[[l11]][[l21]]$llik
fit[l21, "Gsq"] <- Modellist[[l11]][[l21]]$Gsq
fit[l21, "BIC"] <- Modellist[[l11]][[l21]]$bic
fit[l21, "AIC"] <- Modellist[[l11]][[l21]]$aic
if(l21 != 1) {
posteriors <- data.frame(Modellist[[l11]][[l21]]$posterior, predclass=Modellist[[l11]][[l21]]$predclass)
classification_table <- as.matrix(ddply(posteriors, .(predclass), function(x) colSums(x[,1:l21])) )
fit[l21, "ClassErr"] <- round(1-sum(diag(classification_table[,c(2:(l21+1))])) / sum(classification_table[,c(2:(l21+1))]),3)
error_prior <- entropy(Modellist[[l11]][[l21]]$P) # Class proportions
error_post <- mean(apply(Modellist[[l11]][[l21]]$posterior, 1, entropy), na.rm = TRUE)
fit[l21, "R2_entropy"] <- round((error_prior - error_post) / error_prior,3)
}
sizes[l21, "L1"] <- names(Modellist[l11])
sizes[l21, "NClass"] <- l21
sizes[l21, names(sizes) %in% paste("P", 1:l21, sep = ".")] <- round(Modellist[[l11]][[l21]]$P,3)
if(l21 != 1) sizes[l21, names(sizes) %in% paste("Pse", 1:l21, sep = ".")] <- round(Modellist[[l11]][[l21]]$P.se,3)
}
fit$chg_Gsq <- round((fit$Gsq - fit$Gsq[1])/fit$Gsq[1],3)
Fits[[l0]] <- fit %>% dplyr::select(L1, NClass, Chisq, npar, loglik, Gsq, chg_Gsq, BIC, AIC, R2_entropy, ClassErr)
Sizes[[l0]] <- sizes[level2,]
names(Fits) <- names(Modellist)
names(Sizes) <- names(Modellist)
}
} else if (all(is.na(level2) & is.na(level3))){
fit <- data.frame(NClass = NULL, Chisq = NULL, npar = NULL, loglik = NULL, Gsq = NULL, BIC = NULL, AIC = NULL)
sizes <- data.frame(matrix(NA,1,2*max(level1)+1))
names(sizes) <- c("NClass", paste("P", 1:max(level1), sep = "."),
paste("Pse", 1:max(level1), sep = "."))
for (l12 in level1) {
fit[l12, "NClass"] <- l12
fit[l12, "Chisq"] <- Modellist[[l12]]$Chisq
fit[l12, "npar"] <- Modellist[[l12]]$npar
fit[l12, "loglik"] <- Modellist[[l12]]$llik
fit[l12, "Gsq"] <- Modellist[[l12]]$Gsq
fit[l12, "BIC"] <- Modellist[[l12]]$bic
fit[l12, "AIC"] <- Modellist[[l12]]$aic
if(l12 != 1) {
posteriors <- data.frame(Modellist[[l12]]$posterior, predclass=Modellist[[l12]]$predclass)
classification_table <- as.matrix(ddply(posteriors, .(predclass), function(x) colSums(x[,1:l12])) )
fit[l12, "ClassErr"] <- round(1-sum(diag(classification_table[,c(2:(l12+1))])) / sum(classification_table[,c(2:(l12+1))]),3)
error_prior <- entropy(Modellist[[l12]]$P) # Class proportions
error_post <- mean(apply(Modellist[[l12]]$posterior, 1, entropy), na.rm = TRUE)
fit[l12, "R2_entropy"] <- round((error_prior - error_post) / error_prior,3)
}
sizes[l12, "NClass"] <- l12
sizes[l12, names(sizes) %in% paste("P", 1:l12, sep = ".")] <- round(Modellist[[l12]]$P,3)
if(l12 != 1) sizes[l12, names(sizes) %in% paste("Pse", 1:l12, sep = ".")] <- round(Modellist[[l12]]$P.se,3)
}
fit$chg_Gsq <- round((fit$Gsq - fit$Gsq[1])/fit$Gsq[1],3)
Fits[[l12]] <- fit %>% dplyr::select(NClass, Chisq, npar, loglik, Gsq, chg_Gsq, BIC, AIC, R2_entropy, ClassErr)
Sizes[[l12]] <- sizes[level1,]
}
Results <- list(Fits = Fits, Size = Sizes)
return(Results)
}