bootstrap_lm_cov_latent: Parametric bootstrap for LM models with individual covariates...

In LMest: Generalized Latent Markov Models

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The function is no longer maintained.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)

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