R: Performs Parametric Bootstrap for Standard Error...

boot.se {mixtools} | R Documentation

## 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 \& 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

[Package mixtools version 1.2.0 Index]