bootstrap_lm_cov_latent {LMest} | R Documentation |
Parametric bootstrap for LM models with individual covariates in the latent model
Description
Function that performs bootstrap parametric resampling to compute standard errors for the parameter estimates.
The function is no longer maintained. Please look at bootstrap
function.
Usage
bootstrap_lm_cov_latent(X1, X2, param = "multilogit", Psi, Be, Ga, B = 100,
fort = TRUE)
Arguments
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) |
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 |
array of conditional response probabilities (mb x k x r) |
Be |
parameters affecting the logit for the initial probabilities |
Ga |
parametes affecting the logit for the transition probabilities |
B |
number of bootstrap samples |
fort |
to use fortran routine when possible (FALSE for not use fortran) |
Value
mPsi |
average of bootstrap estimates of the conditional response probabilities |
mBe |
average of bootstrap estimates of the parameters affecting the logit for the initial probabilities |
mGa |
average of bootstrap estimates of the parameters affecting the logit for the transition probabilities |
sePsi |
standard errors for the conditional response probabilities |
seBe |
standard errors for the parameters in Be |
seGa |
standard errors for the parameters in Ga |
Author(s)
Francesco Bartolucci, Silvia Pandolfi - University of Perugia (IT)
Examples
## 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
# 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
out1 <- est_lm_cov_latent(S, X1, X2, k = 2, output = TRUE, out_se = TRUE)
out2 <- bootstrap_lm_cov_latent(X1, X2, Psi = out1$Psi, Be = out1$Be, Ga = out1$Ga, B = 1000)
## End(Not run)