est_lm_cov_latent | R Documentation |
Main function for estimating the LM model with covariates in the latent model.
The function is no longer maintained. Please look at lmest
function.
est_lm_cov_latent(S, X1=NULL, X2=NULL, yv = rep(1,nrow(S)), k, start = 0, tol = 10^-8,
maxit = 1000, param = "multilogit", Psi, Be, Ga, fort = TRUE,
output = FALSE, out_se = FALSE, fixPsi = FALSE)
S |
array of available configurations (n x TT x r) with categories starting from 0 (use NA for missing responses) |
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) |
Psi |
intial value of the array of the conditional response probabilities (mb x k x r) |
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) |
fort |
to use fortran routine when possible (FALSE for not use fortran) |
output |
to return additional output (V,PI,Piv,Ul) |
out_se |
to compute the information matrix and standard errors |
fixPsi |
TRUE if Psi is given in input and is not updated anymore |
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 |
Piv |
estimate of initial probability matrix |
PI |
estimate of transition probability matrices |
Psi |
estimate of conditional response probabilities |
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 response configuration and time occasion |
Ul |
matrix containing the predicted sequence of latent states by the local decoding method |
sePsi |
standard errors for the conditional response matrix |
seBe |
standard errors for Be |
seGa |
standard errors for Ga |
call |
command used to call the function |
Francesco Bartolucci, Silvia Pandolfi, University of Perugia, http://www.stat.unipg.it/bartolucci
Bartolucci, F., Farcomeni, A. and Pennoni, F. (2013) Latent Markov Models for Longitudinal Data, Chapman and Hall/CRC press.
## Not run:
# Example based on self-rated health status (SRHS) data
# load SRHS data
data(data_SRHS_long)
dataSRHS = data_SRHS_long
TT <- 8
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$srhs)
# matrix of responses (with ordered categories from 0 to 4)
S <- 5-res$YY
n <- dim(S)[1]
# matrix of covariates (for the first and the following occasions)
# colums are: gender,race,educational level (2 columns),age,age^2)
X1 <- res$XX[,1,]
X2 <- res$XX[,2:TT,]
# estimate the model
est2f <- est_lm_cov_latent(S, X1, X2, k = 2, output = TRUE, out_se = TRUE)
summary(est2f)
# average transition probability matrix
PI <- round(apply(est2f$PI[,,,2:TT], c(1,2), mean), 4)
# Transition probability matrix for white females with high educational level
ind1 <- X1[,1] == 1 & X1[,2] == 0 & X1[,4] == 1)
PI1 <- round(apply(est2f$PI[,,ind1,2:TT], c(1,2), mean), 4)
# Transition probability matrix for non-white male, low educational level
ind2 <- (X1[,1] == 0 & X1[,2] == 1 & X1[,3] == 0 & X1[,4] == 0)
PI2 <- round(apply(est2f$PI[,,ind2,2:TT], c(1,2), mean), 4)
## End(Not run)
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