1d:T7f8,# Estimate mixed LM model Main function for estimating the mixed LM model with discrete random effect in the latent model. The function is no longer maintained. Please look at `lmestMixed` function ```r est_lm_mixed(S, yv = rep(1,nrow(S)), k1, k2, start = 0, tol = 10^-8, maxit = 1000, out_se = FALSE) ``` ## Arguments - `S`: array of available response configurations (n x TT x r) with categories starting from 0 - `yv`: vector of frequencies of the available configurations - `k1`: number of latent classes - `k2`: number of latent states - `start`: type of starting values (0 = deterministic, 1 = random) - `tol`: tolerance level for convergence - `maxit`: maximum number of iterations of the algorithm - `out_se`: to compute standard errors ## Returns - **la**: estimate of the mass probability vector (distribution of the random effects) - **Piv**: estimate of initial probabilities - **Pi**: estimate of transition probability matrices - **Psi**: estimate of conditional response probabilities - **lk**: maximum log-likelihood - **W**: posterior probabilities of the random effect - **np**: number of free parameters - **bic**: value of BIC for model selection - **call**: command used to call the function ## References Bartolucci, F., Farcomeni, A. and Pennoni, F. (2013) **Latent Markov Models for Longitudinal Data**, Chapman and Hall/CRC press. ## Author(s) Francesco Bartolucci, Silvia Pandolfi - University of Perugia (IT) ## Examples ```r ## Not run: # Example based of criminal data # load data data(data_criminal_sim) out <- long2wide(data_criminal_sim, "id", "time", "sex", c("y1","y2","y3","y4","y5","y6","y7","y8","y9","y10"), aggr = T, full = 999) XX <- out$XX YY <- out$YY freq <- out$freq n1 <- sum(freq[XX[,1] == 1]) n2 <- sum(freq[XX[,1] == 2]) n <- sum(freq) # fit mixed LM model only for females YY <- YY[XX[,1] == 2,,] freq <- freq[XX[,1] == 2] k1 <- 2 k2 <- 2 res <- est_lm_mixed(YY, freq, k1, k2, tol = 10^-8) summary(res) ## End(Not run) ```

est_lm_mixed function