## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----Specify------------------------------------------------------------------
library(CLCM)
library(ggplot2)
N <- 500
number.timepoints <- 1
item.type <- c('Ordinal', 'Nominal', 'Poisson', 'Neg_Binom', 'ZINB', 'ZIP', 'Normal', 'Beta') 
sim.categories.j <- c(4, 4,  30, 30, 30, 30, NA, NA) 
lc.prop <- list('Time_1' = c(0.5, 0.5), 
                'Time_2' =  c(0.5, 0.5) )     
Q <- matrix(1, nrow = length(item.type), ncol = 1, 
            dimnames = list(paste0('Item_', 1:length(item.type)), 'F1'))
Q

## ----Generate-----------------------------------------------------------------
set.seed(03102021)
sim.dat <- simulate_clcm(N = N, Q = Q, number.timepoints = number.timepoints, 
                         item.type = item.type, 
                         categories.j = sim.categories.j, 
                         lc.prop = lc.prop)

## ----Estimate-----------------------------------------------------------------
mod <- clcm(dat = sim.dat$dat, 
            item.type = sim.dat$item.type, 
            item.names = sim.dat$item.names, 
            Q = sim.dat$Q) 


## ----Fit----------------------------------------------------------------------
aic_bic_clcm(mod = mod) 

## ----Results------------------------------------------------------------------
lca.hat <- mod$dat$lca
lca.true <- mod$dat$true_lca
prop.table(table(lca.true == lca.hat))
xtabs( ~ lca.true + lca.hat)

## ----Plot Results, out.width="80%"--------------------------------------------

library(ggplot2)
dat.plot <- mod$dat
dat.plot$lca <- factor(dat.plot$lca, levels = sort(unique(dat.plot$lca)), labels = paste0('Class ', sort(unique(dat.plot$lca)) ))
item.names <- mod$item.names

for(j in 1:length(item.names) ){
  
  if(item.type[j] %in% c('Ordinal', 'Nominal')){
    
    pp <- ggplot(data = dat.plot, aes(fill=lca, x= as.factor(get(item.names[j])) )) + 
      geom_bar(aes(y = after_stat(count / sum(count))), color="#e9ecef", alpha=0.6, position="dodge", stat="count") +
      scale_y_continuous(labels=scales::percent_format(accuracy = 1)) +
      scale_fill_manual(name = 'Latent Classes', values=c("blue2", "red2")) +
      theme_minimal() +                             
      theme(legend.position = 'bottom') + 
      labs(x = 'Item Responses', y = 'Percentage', 
           title = paste0('Item Name: ', item.names[j]), 
           subtitle = paste0('Item Type: ', item.type[j]), 
           caption = 'Note: Simulated data')
    
    suppressMessages(plot(pp))
    
  }# Ordinal
  
  
  if(item.type[j] %in% c('Poisson', 'Neg_Binom', 'ZINB', 'ZIP', 'Normal', 'Beta')){
    
    pp <- ggplot(data = dat.plot, aes(x = get(item.names[j]), group = lca, fill = lca)) + 
      geom_histogram(aes(y = after_stat(count / sum(count))), color="#e9ecef", alpha=0.6, position = 'identity') +
      scale_y_continuous(labels=scales::percent_format(accuracy = 1)) +
      scale_fill_manual(name = 'Latent Classes', values=c("blue2", "red2")) +
      theme_minimal() + 
      theme(legend.position = 'bottom') + 
      labs(x = 'Item Responses', y = 'Percentage', 
           title = paste0('Item Name: ', item.names[j]), 
           subtitle = paste0('Item Type: ', item.type[j]), 
           caption = 'Note: Simulated data')
    
    suppressMessages(plot(pp))
    
  }# Count items
  
  
  
}# end loop over items