Given a flexmix object perform parametric or empirical bootstrap.
boot(object, ...)
# S4 method for flexmix
boot(object, R, sim = c("ordinary", "empirical", "parametric"),
initialize_solution = FALSE, keep_weights = FALSE,
keep_groups = TRUE, verbose = 0, control,
k, model = FALSE, ...)
LR_test(object, ...)
# S4 method for flexmix
LR_test(object, R, alternative = c("greater", "less"), control, ...)boot returns an object of class FLXboot which
contains the fitted parameters, the fitted priors, the log
likelihoods, the number of components of the fitted mixtures and the
information if the EM algorithm has converged.
LR_test returns an object of class htest containing the
number of valid bootstrap replicates, the p-value, the - twice log
likelihood ratio test statistics for the original data and the
bootstrap replicates.
A fitted finite mixture model of class flexmix.
The number of bootstrap replicates.
A character string indicating the type of simulation
required. Possible values are "ordinary" (the default),
"parametric", or "empirical".
A logical. If TRUE the EM algorithm is
initialized in the given solution.
A logical. If TRUE the weights are kept.
A logical. If TRUE the groups are kept.
If a positive integer, then progress information
is reported every verbose iterations. If 0,
no output is generated during the bootstrap replications.
Object of class FLXcontrol or a named list.
If missing the control of the fitted object is taken.
Vector of integers specifying for which number of components
finite mixtures are fitted to the bootstrap samples. If missing the
number of components of the fitted object are taken.
A character string specifying the alternative
hypothesis, must be either "greater" (default) or
"less" indicating if the alternative hypothesis is that the
mixture has one more component or one less.
A logical. If TRUE the model and the weights slot for
each sample are stored and returned.
Further arguments to be passed to or from methods.
Bettina Gruen
data("NPreg", package = "flexmix")
fitted <- initFlexmix(yn ~ x + I(x^2) | id2, data = NPreg, k = 2)
if (FALSE) {
lrtest <- LR_test(fitted, alternative = "greater", R = 20,
verbose = 1)
}
Run the code above in your browser using DataLab