View source: R/bootstrap_lm_basic_cont.R View source: R/LMest-deprecated.R
bootstrap_lm_basic_cont | R Documentation |
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The function is no longer maintained.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.
bootstrap_lm_basic_cont(piv, Pi, Mu, Si, n, B = 100, start = 0, mod = 0, tol = 10^-6)
piv |
initial probability vector |
Pi |
probability transition matrices (k x k x TT) |
Mu |
matrix of conditional means for the response variables (r x k) |
Si |
var-cov matrix common to all states (r 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 |
mMu |
average of bootstrap estimates of the conditional means of the response variables |
mSi |
average of bootstrap estimates of the var-cov matrix |
mpiv |
average of bootstrap estimates of the initial probability vector |
mPi |
average of bootstrap estimates of the transition probability matrices |
seMu |
standard errors for the conditional means of the response variables |
seSi |
standard errors for the var-cov matrix |
sepiv |
standard errors for the initial probability vector |
sePi |
standard errors for the transition probability matrices |
Francesco Bartolucci, Silvia Pandolfi, University of Perugia (IT), http://www.stat.unipg.it/bartolucci
## 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$YY
n <- dim(Y)[1]
# fit of the Basic LM model for continuous outcomes
k <- 3
out1 <- est_lm_basic_cont(Y, k, mod = 1)
out2 <- bootstrap_lm_basic_cont(out1$piv, out1$Pi, out1$Mu, out1$Si, n, mod = 1, B = 1000)
## End(Not run)
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