est_lm_mixed: Estimate mixed LM model

In LMest: Generalized Latent Markov Models

Estimate mixed LM model

Description

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The function is no longer maintained.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

Usage

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

Value

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

Author(s)

Francesco Bartolucci, Silvia Pandolfi - University of Perugia (IT)

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 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)

LMest documentation built on Aug. 27, 2023, 5:06 p.m.