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The function is no longer maintained.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