# Estimate basic LM model Main function for estimating the basic LM model. The function is no longer maintained. Please look at `lmest` function . ```r est_lm_basic(S, yv, k, start = 0, mod = 0, tol = 10^-8, maxit = 1000, out_se = FALSE, piv = NULL, Pi = NULL, Psi = NULL) ``` ## Arguments - `S`: array of available configurations (n x TT x r) with categories starting from 0 (use NA for missing responses) - `yv`: vector of frequencies of the available configurations - `k`: number of latent states - `start`: type of starting values (0 = deterministic, 1 = random, 2 = initial values in input) - `mod`: model on the transition probabilities (0 for time-heter., 1 for time-homog., from 2 to (TT-1) partial homog. of that order) - `tol`: tolerance level for convergence - `maxit`: maximum number of iterations of the algorithm - `out_se`: to compute the information matrix and standard errors - `piv`: initial value of the initial probability vector (if start=2) - `Pi`: initial value of the transition probability matrices (k x k x TT) (if start=2) - `Psi`: initial value of the conditional response probabilities (mb x k x r) (if start=2) ## Returns - **lk**: maximum log-likelihood - **piv**: estimate of initial probability vector - **Pi**: estimate of transition probability matrices - **Psi**: estimate of conditional response probabilities - **np**: number of free parameters - **aic**: value of AIC for model selection - **bic**: value of BIC for model selection - **lkv**: log-likelihood trace at every step - **V**: array containing the posterior distribution of the latent states for each response configuration and time occasion - **sepiv**: standard errors for the initial probabilities - **sePi**: standard errors for the transition probabilities - **sePsi**: standard errors for the conditional response probabilities - **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), http://www.stat.unipg.it/bartolucci ## Examples ```r ## Not run: # Example of drug consumption data # load data data(data_drug) data_drug <- as.matrix(data_drug) S <- data_drug[,1:5]-1 yv <- data_drug[,6] # fit of the Basic LM model k <- 3 out <- est_lm_basic(S, yv, k, mod = 1) summary(out) # Example based on criminal data # load criminal 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 # fit basic LM model with increasing number of states to select the most suitable Res0 <- vector("list", 7) for(k in 1:7){ Res0[[k]] <- est_lm_basic(YY, freq, k, mod = 1, tol = 10^-4) save(list <- ls(), file = "example_criminal_temp.RData") } out1 <- Res0[[6]] ## End(Not run) ```

est_lm_basic function