est_lm_cov_latent_cont {LMest} | R Documentation |
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The function is no longer maintained.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)