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

## ----Specify_1----------------------------------------------------------------

library(CLCM)
N <- 2000
number.timepoints <- 2
item.type <- c('Ordinal', 'Nominal', 'Poisson', 'Neg_Binom', 'ZINB', 'ZIP', 'Normal', 'Beta') 
sim.categories.j <- c(4, 4, 30, 30, 30, 30, NA, NA) 
J <- length(item.type)
item.names <- paste0('Item_', 1:J)
Q <- matrix(1, nrow = length(item.type), ncol = 1, dimnames = list(paste0('Item_', 1:length(item.type)), NULL))
K <- ncol(Q) 


## ----Specify_2----------------------------------------------------------------
set.seed(09092026)
number.groups <- 2
dat <- data.frame(
  'USUBJID' = rep(paste0('Subject_', formatC(1:N, width = 4, flag = '0')), length.out= N*number.timepoints),
  'Group' = rep(paste0('Group_', 1:number.groups), length.out = N*number.timepoints),
  'Time' = rep(paste0('Time_', 1:number.timepoints), each = N),
  stringsAsFactors=FALSE)

# Design Matrix
X <- model.matrix( ~ Group*Time, data = dat) 
# Beta
Beta <- matrix(0, nrow = ncol(X), dimnames=list(colnames(X), 'param'))

# Small separation between txa:
Beta[grepl('Group_2:Time', rownames(Beta)), ] <- 1
Beta

# Matrix multiply:
XB <- X %*% Beta
p <- exp(XB)/(1 + exp(XB))
lca <- matrix(runif(n = nrow(p)), nrow = nrow(p), ncol = 1) < p
lca <- lca + 1

post <- matrix(0, nrow = nrow(dat), ncol = 2^K)
post[ cbind(1:nrow(lca), lca) ] <- 1 # True posterior distributions - pass to simulation function



## ----Simulate-----------------------------------------------------------------
set.seed(03082021)
sim.dat <- simulate_clcm(N = N, number.timepoints = number.timepoints, 
                         Q = Q, 
                         item.names = item.names, 
                         item.type = item.type, 
                         categories.j = sim.categories.j, 
                         post = post)


## ----prep---------------------------------------------------------------------
dat.cov <- merge(x = dat, y = sim.dat$dat, by = c('USUBJID', 'Time'))

## ----Estimate-----------------------------------------------------------------

mod <- clcm(dat = dat.cov, 
            item.type = item.type, 
            item.names = item.names, 
            Q = Q, max.diff = 0.001, 
            lat.reg =  list('Time_1' = NULL, 
                            'Time_2' = 'Group'))


## ----Results------------------------------------------------------------------

Beta # Generating parameter
mod$lat.reg.param  # estimate
transition_matrix_clcm(mod = mod, stratification = TRUE, covariate = 'Group')


## ----Results_2----------------------------------------------------------------

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