# Estimate Markov chain (MC) model with covariates Main function for estimating the MC model with covariates. The function is no longer maintained. Please look at `lmestMc` function . ```r est_mc_cov(S, X1 = NULL, X2 = NULL, yv = rep(1,nrow(S)), start = 0, tol = 10^-8, maxit = 1000, out_se = FALSE, output = FALSE, fort = TRUE) ``` ## Arguments - `S`: matrix of available configurations of the response variable (n x TT) with categories starting from 0 - `X1`: matrix of covariates affecting the initial probabilities (n x nc1) - `X2`: array of covariates affecting the transition probabilities (n x TT-1 x nc2) - `yv`: vector of frequencies of the available configurations - `start`: type of starting values (0 = deterministic, 1 = random) - `tol`: tolerance level for checking convergence of the algorithm - `maxit`: maximum number of iterations of the algorithm - `out_se`: to compute the information matrix and standard errors - `output`: to return additional output (PI,Piv) - `fort`: to use fortran routine when possible (FALSE for not use fortran) ## Returns - **lk**: maximum log-likelihood - **Be**: estimated array of the parameters affecting the logit for the initial probabilities - **Ga**: estimated array of the parameters affecting the logit for the transition probabilities - **np**: number of free parameters - **aic**: value of AIC for model selection - **bic**: value of BIC for model selection - **seBe**: standard errors for Be - **seGa**: standard errors for Ga - **Piv**: estimate of initial probability matrix - **PI**: estimate of transition probability matrices - **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, http://www.stat.unipg.it/bartolucci ## Examples ```r ## Not run: # Example based on criminal data # load criminal data data(data_criminal_sim) #We consider the response variable referring of crime of type 5 out <- long2wide(data_criminal_sim, "id", "time", "sex", "y5", aggr = T, full = 999) XX <- out$XX-1 YY <- out$YY freq <- out$freq TT <- 6 X1 <- as.matrix(XX[,1]) X2 <- as.matrix(XX[,2:TT]) # estimate the model res <- est_mc_cov(S = YY, yv = freq, X1 = X1, X2 = X2, output = TRUE) summary(res) # Initial probability for female Piv0 <- round(colMeans(res$Piv[X1 == 0,]), 4) # Initial probability for male Piv1 <- round(colMeans(res$Piv[X1 == 1,]), 4) ## End(Not run) ```

est_mc_cov function