est_lm_basic_cont: Estimate basic LM model for continuous outcomes

In LMest: Generalized Latent Markov Models

Estimate basic LM model for continuous outcomes

Description

Main function for estimating the basic LM model for continuous outcomes.

The function is no longer maintained.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

Usage

est_lm_basic_cont(Y, k, start = 0, mod = 0, tol = 10^-8, maxit = 1000,
                  out_se = FALSE, piv = NULL, Pi = NULL, Mu = NULL, Si = NULL)

Arguments

Y	array of continuous outcomes (n x TT x r)
k	number of latent states
start	type of starting values (0 = deterministic, 1 = random, 2 = initial values in input)
mod	model on the transition probabilities (0 for time-heter., 1 for time-homog., from 2 to (TT-1) partial homog. of that order)
tol	tolerance level for convergence
maxit	maximum number of iterations of the algorithm
out_se	to compute the information matrix and standard errors
piv	initial value of the initial probability vector (if start=2)
Pi	initial value of the transition probability matrices (k x k x TT) (if start=2)
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)

Value

lk	maximum log-likelihood
piv	estimate of initial probability vector
Pi	estimate of transition probability matrices
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
V	array containing the posterior distribution of the latent states for each units and time occasion
call	command used to call the function

Author(s)

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

# fit of the Basic LM model for continuous outcomes
k <- 3
out <- est_lm_basic_cont(Y, k, mod = 1, tol = 10^-5)
summary(out)

## End(Not run)

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