mixtools (version 2.0.0)

boot.se: 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, ...)

Value

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

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.

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