View source: R/LMest-deprecated.R View source: R/bootstrap_lm_cov_latent_cont.R
bootstrap_lm_cov_latent_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The function is no longer maintained.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_cov_latent_cont(X1, X2, param = "multilogit", Mu, Si, Be, Ga, B = 100)
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) |
Mu |
matrix of conditional means for the response variables (r x k) |
Si |
var-cov matrix common to all states (r 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 |
mMu |
average of bootstrap estimates of the conditional means for the response variables |
mSi |
average of bootstrap estimates of the var-cov matrix |
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 |
seMu |
standard errors for the conditional means |
seSi |
standard errors for the var-cov matrix |
seBe |
standard errors for the parameters in Be |
seGa |
standard errors for the parameters in Ga |
Francesco Bartolucci, Silvia Pandolfi - University of Perugia (IT)
## Not run:
# Example based on multivariate longitudinal continuous data
data(data_long_cont)
TT <- 5
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$YY
X1 <- res$XX[,1,]
X2 <- res$XX[,2:TT,]
# estimate the model
est <- est_lm_cov_latent_cont(Y, X1, X2, k = 3, output = TRUE)
out <- bootstrap_lm_cov_latent_cont(X1, X2, Mu = est$Mu, Si = est$Si,
Be = est$Be, Ga = est$Ga, B = 1000)
## End(Not run)
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