est_lm_cov_latent_cont: Estimate LM model for continuous outcomes with covariates in... In LMest: Generalized Latent Markov Models

Description

Main function for estimating the LM model for continuous outcomes with covariates in the latent model.

The function is no longer maintained. Please look at lmestCont function.

Usage

 1 2 3 4 est_lm_cov_latent_cont(Y, X1 = NULL, X2 = NULL, yv = rep(1,nrow(Y)), k, start = 0, tol = 10^-8, maxit = 1000, param = "multilogit", Mu = NULL, Si = NULL, Be = NULL, Ga = NULL, output = FALSE, out_se = FALSE)

Arguments

 Y array of continuous outcomes (n x TT x r) 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 k number of latent states start type of starting values (0 = deterministic, 1 = random, 2 = initial values in input) tol tolerance level for checking convergence of the algorithm maxit maximum number of iterations of the algorithm param type of parametrization for the transition probabilities ("multilogit" = standard multinomial logit for every row of the transition matrix, "difflogit" = multinomial logit based on the difference between two sets of parameters) Mu initial value of the conditional means (r x k) (if start=2) Si initial value of the var-cov matrix common to all states (r x r) (if start=2) Be intial value of the parameters affecting the logit for the initial probabilities (if start=2) Ga intial value of the parametes affecting the logit for the transition probabilities (if start=2) output to return additional output (V,PI,Piv,Ul) out_se to compute the information matrix and standard errors

Value

 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 Mu estimate of conditional means of the response variables Si estimate of var-cov matrix common to all states 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 Piv estimate of initial probability matrix PI estimate of transition probability matrices Ul matrix containing the predicted sequence of latent states by the local decoding method call command used to call the function

Author(s)

Francesco Bartolucci, Silvia Pandolfi, University of Perugia, http://www.stat.unipg.it/bartolucci

References

Bartolucci, F., Farcomeni, A. and Pennoni, F. (2013) Latent Markov Models for Longitudinal Data, Chapman and Hall/CRC press.

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ## Not run: # Example based on multivariate longitudinal continuous data data(data_long_cont) TT <- 5 res <- long2matrices(data_long_cont\$id, X = cbind(data_long_cont\$X1, data_long_cont\$X2), Y = cbind(data_long_cont\$Y1, data_long_cont\$Y2, data_long_cont\$Y3)) Y <- res\$YY X1 <- res\$XX[,1,] X2 <- res\$XX[,2:TT,] # estimate the model est <- est_lm_cov_latent_cont(Y, X1, X2, k = 3, output = TRUE) summary(est) # average transition probability matrix PI <- round(apply(est\$PI[,,,2:TT], c(1,2), mean), 4) PI ## End(Not run)

LMest documentation built on Oct. 10, 2021, 1:09 a.m.