bootstrap_lm_basic: Parametric bootstrap for the basic LM model

In LMest: Generalized Latent Markov Models

Parametric bootstrap for the basic LM 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The function is no longer maintained.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_basic(piv, Pi, Psi, n, B = 100, start = 0, mod = 0, tol = 10^-6)

Arguments

piv	initial probability vector
Pi	probability transition matrices (k x k x TT)
Psi	matrix of conditional response probabilities (mb x k x r)
n	sample size
B	number of bootstrap samples
start	type of starting values (0 = deterministic, 1 = random)
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

Value

mPsi	average of bootstrap estimates of the conditional response probabilities
mpiv	average of bootstrap estimates of the initial probability vector
mPi	average of bootstrap estimates of the transition probability matrices
sePsi	standard errors for the conditional response probabilities
sepiv	standard errors for the initial probability vector
sePi	standard errors for the transition probability matrices

Author(s)

Francesco Bartolucci, Silvia Pandolfi, University of Perugia (IT), http://www.stat.unipg.it/bartolucci

Examples

## Not run: 
# Example of drug consumption data
# load data
data(data_drug)
data_drug <- as.matrix(data_drug)
S <- data_drug[,1:5]-1
yv <- data_drug[,6]
n <- sum(yv)
# fit of the Basic LM model
k <- 3
out1 <- est_lm_basic(S, yv, k, mod = 1, out_se = TRUE)
out2 <- bootstrap_lm_basic(out1$piv, out1$Pi, out1$Psi, n, mod = 1, B = 1000)

## End(Not run)

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