est_lm_cov_latent_cont: Estimate LM model for continuous outcomes with covariates in...

In LMest: Generalized Latent Markov Models

Estimate LM model for continuous outcomes with covariates in the latent model

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The function is no longer maintained.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

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

## 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 Aug. 27, 2023, 5:06 p.m.