est_lm_cov_manifest: Estimate LM model with covariates in the measurement model

In LMest: Generalized Latent Markov Models

Estimate LM model with covariates in the measurement model

Description

Main function for estimating LM model with covariates in the measurement model based on a global logit parameterization.

The function is no longer maintained.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

Usage

est_lm_cov_manifest(S, X, yv = rep(1,nrow(S)), k, q = NULL, mod = c("LM", "FM"),
                    tol = 10^-8, maxit = 1000, start = 0, mu = NULL, al = NULL,
                    be = NULL, si = NULL, rho = NULL, la = NULL, PI = NULL,
                    output = FALSE, out_se = FALSE)

Arguments

S	array of available configurations (n x TT) with categories starting from 0
X	array (n x TT x nc) of covariates with eventually includes lagged response (nc = number of covariates)
yv	vector of frequencies of the available configurations
k	number of latent states
q	number of support points for the AR(1) process
mod	model ("LM" = Latent Markov with stationary transition, "FM" = finite mixture)
tol	tolerance for the convergence (optional) and tolerance of conditional probability if tol>1 then return
maxit	maximum number of iterations of the algorithm
start	type of starting values (0 = deterministic, 1 = random, 2 = initial values in input)
mu	starting value for mu (optional)
al	starting value for al (optional)
be	starting value for be (optional)
si	starting value for si when mod="FM" (optional)
rho	starting value for rho when mod="FM" (optional)
la	starting value for la (optional)
PI	starting value for PI (optional)
output	to return additional output (PRED0, PRED1)
out_se	TRUE for computing information matrix and standard errors

Value

mu	vector of cutpoints
al	support points for the latent states
be	estimate of the vector of regression parameters
si	sigma of the AR(1) process (mod = "FM")
rho	parameter vector for AR(1) process (mod = "FM")
la	vector of initial probabilities
PI	transition matrix
lk	maximum log-likelihood
np	number of parameters
aic	value of AIC index
bic	value of BIC index
PRED0	prediction of latent state
PRED1	prediction of the overall latent effect
sebe	standard errors for the regression parameters be
selrho	standard errors for logit type transformation of rho
J1	information matrix
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.

Bartolucci, F., Bacci, S. and Pennoni, F. (2014) Longitudinal analysis of the self-reported health status by mixture latent autoregressive models, Journal of the Royal Statistical Society - series C, 63, pp. 267-288

Examples

## Not run: 
# Example based on self-rated health status (SRHS) data

# load SRHS data
data(data_SRHS_long)
dataSRHS <- data_SRHS_long
head(dataSRHS)

res <- long2matrices(dataSRHS$id, X = cbind(dataSRHS$gender-1,
 dataSRHS$race == 2 | dataSRHS$race == 3, dataSRHS$education == 4,
dataSRHS$education == 5, dataSRHS$age-50, (dataSRHS$age-50)^2/100),
Y = dataSRHS$srhs)

X <- res$XX
S <- 5-res$YY

# *** fit stationary LM model
res0 <- vector("list", 10)
tol <- 10^-6;
for(k in 1:10){
  res0[[k]] <- est_lm_cov_manifest(S, X, k, 1, mod = "LM", tol)
   save.image("example_SRHS.RData")
}

# *** fit the mixture latent auto-regressive model
tol <- 0.005
res <- vector("list",4)
k <- 1
q <- 51
res[[k]] <- est_lm_cov_manifest(S, X, k, q, mod = "FM", tol, output = TRUE)
for(k in 2:4) res[[k]] <- est_lm_cov_manifest(S, X, k, q = 61, mod = "FM", tol, output = TRUE)

## End(Not run)

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