Longitudinal CLCM & Transition Matrix

  1. All Item Types
  2. K = 1, yielding 2^K = 2 latent classes
  3. T=2, 2 timepoints (longitudinal data)
  4. Generate data using transition matrix
  5. Fit CLCM
  6. Re-fit CLCM with constraint on latent classes
  7. Compare model fit of the two models via AIC/BIC
  8. Examine transition matrix of CLCM
  9. Compute classification accuracy

Specify Details of Data Generation

library(CLCM)

N <- 80
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 transition matrix

The next step is to specify the posterior distribution proportions that will be generated. Here we specify the latent class proportions at Timepoint 1, and the transition matrix, tau. This is sufficient to completely specify the latent class assignment of each subject at each timepoint.


lc.prop <- list('Time_1' = c(0, 1) ) 
tau <- matrix(c(0, 0, 0.4, 0.6), nrow = 2^K, ncol = 2^K, byrow = TRUE)

Simulate Item Responses

Pass everything to the simulate_clcm() function and generate the item responses:

set.seed(03102021)
sim.dat <- simulate_clcm(N = N, 
                         number.timepoints = 2, 
                         Q = Q, 
                         item.names = item.names, 
                         item.type = item.type, 
                         categories.j = sim.categories.j, 
                         lc.prop = lc.prop,
                         transition.matrix = tau)

Estimate CLCM

Estimate the latent class model, using the correct Q-matrix. The data is in long format and has a variable Time, a factor with two levels, Time_1 and Time_2 to distinguish timepoints.


mod1 <- clcm(dat = sim.dat$dat, 
             item.type = sim.dat$item.type, 
             item.names = sim.dat$item.names, 
             Q = sim.dat$Q, 
             max.diff = 0.001) 
#> iteration: 1  max diff in item parameter estimates: 4.161119
#> iteration: 2  max diff in item parameter estimates: 2.653271
#> iteration: 3  max diff in item parameter estimates: 1.111018
#> iteration: 4  max diff in item parameter estimates: 0.718995
#> iteration: 5  max diff in item parameter estimates: 0.105633
#> iteration: 6  max diff in item parameter estimates: 0.001951
#> iteration: 7  max diff in item parameter estimates: 0.000112

We are going to compare this model to a second model with a constraint imposed on the latent classes. However, before do that, let’s quickly examine the transition matrix and the classification accuracy.

Transition Matrix

transition_matrix_clcm(mod = mod1)
#>             post_LC_0 post_LC_1
#> post_LC_0 0.007247412 0.9927526
#> post_LC_1 0.274893948 0.7251061

Classification Accuracy

Check the classification accuracy of this model comparing the true/generating latent class assignments (lca) to the estimated lca.

lca.hat <- mod1$dat$lca 
lca.true <- mod1$dat$true_lca      
table(lca.true == lca.hat)
#> 
#> TRUE 
#>  160
prop.table(table(lca.true == lca.hat))
#> 
#> TRUE 
#>    1
xtabs( ~ lca.true + lca.hat)
#>         lca.hat
#> lca.true   1   2
#>        1  22   0
#>        2   0 138

Estimate CLCM with Constraint

Estimate mod2, this time with the first latent class constrained to equal zero at timepoint 1. This is done through the use of the lc.con function call.

mod2 <- clcm(dat = sim.dat$dat, 
             lc.con = list('Time_1' = c(NA, 1), 'Time_2' = c(1, 1)), 
             item.type = sim.dat$item.type, 
             item.names = sim.dat$item.names, 
             Q = sim.dat$Q, 
             max.diff = 0.001) 
#> iteration: 1  max diff in item parameter estimates: 5.285171
#> iteration: 2  max diff in item parameter estimates: 4.131888
#> iteration: 3  max diff in item parameter estimates: 0.479707
#> iteration: 4  max diff in item parameter estimates: 0.001841
#> iteration: 5  max diff in item parameter estimates: 6.9e-05

Model Fit Comparison

Check the model fit of the second model, which has one fewer parameter estimated compared to mod1.

unlist( aic_bic_clcm(mod = mod1) )
#>  neg_2LL     npar      AIC      BIC 
#> 3940.353   30.000 4000.353 4092.608
unlist( aic_bic_clcm(mod = mod2) )
#>  neg_2LL     npar      AIC      BIC 
#> 3940.353   29.000 3998.353 4087.533

We can see that imposing the constraint yields slightly lower AIC/BIC values. Hence, we keep the constraint.

Transition Matrices

Examine the transition matrix as well.

transition_matrix_clcm(mod = mod1)
#>             post_LC_0 post_LC_1
#> post_LC_0 0.007247412 0.9927526
#> post_LC_1 0.274893948 0.7251061
transition_matrix_clcm(mod = mod2)
#>           post_LC_0 post_LC_1
#> post_LC_0       NaN       NaN
#> post_LC_1 0.2748942 0.7251058

Compare the true classifications with the estimates from Model 2, with constraint imposed:


lca.hat <- mod2$dat$lca 
lca.true <- mod2$dat$true_lca      
table(lca.true == lca.hat)
#> 
#> TRUE 
#>  160
prop.table(table(lca.true == lca.hat)) 
#> 
#> TRUE 
#>    1
xtabs( ~ lca.true + lca.hat)
#>         lca.hat
#> lca.true   1   2
#>        1  22   0
#>        2   0 138

Classification accuracy is high: hypothesize that this is due to the relatively few number of latent classes, the relatively large number of items per latent classes estimated, and the fact that we fit a correctly specified model with a correctly specified Q-matrix. Overall ideal model fitting scenario, even with only n=80.

More full simulation studies should dig into this!

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