boot.se: Performs Parametric Bootstrap for Standard Error...

In mixtools: Tools for Analyzing Finite Mixture Models

Performs Parametric Bootstrap for Standard Error Approximation

Description

Performs a parametric bootstrap by producing B bootstrap samples for the parameters in the specified mixture model.

Usage

boot.se(em.fit, B = 100, arbmean = TRUE, arbvar = TRUE, 
        N = NULL, ...)

Arguments

em.fit	An object of class mixEM. The estimates produced in em.fit will be used as the parameters for the distribution from which we generate the bootstrap data.
B	The number of bootstrap samples to produce. The default is 100, but ideally, values of 1000 or more would be more acceptable.
arbmean	If FALSE, then a scale mixture analysis can be performed for mvnormalmix, normalmix, regmix, or repnormmix. The default is TRUE.
arbvar	If FALSE, then a location mixture analysis can be performed for mvnormalmix, normalmix, regmix, or repnormmix. The default is TRUE.
N	An n-vector of number of trials for the logistic regression type logisregmix. If NULL, then N is an n-vector of 1s for binary logistic regression.
...	Additional arguments passed to the various EM algorithms for the mixture of interest.

Value

boot.se returns a list with the bootstrap samples and standard errors for the mixture of interest.

References

McLachlan, G. J. and Peel, D. (2000) Finite Mixture Models, John Wiley and Sons, Inc.

Examples

## Bootstrapping standard errors for a regression mixture case.

data(NOdata)
attach(NOdata)
set.seed(100)
em.out <- regmixEM(Equivalence, NO, arbvar = FALSE)
out.bs <- boot.se(em.out, B = 10, arbvar = FALSE)
out.bs

mixtools documentation built on Dec. 5, 2022, 5:23 p.m.