Performs Parametric Bootstrap for Standard Error Approximation

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)
#> number of iterations= 23 
out.bs <- boot.se(em.out, B = 10, arbvar = FALSE)
#> number of iterations= 14 
#> number of iterations= 7 
#> number of iterations= 12 
#> number of iterations= 63 
#> number of iterations= 10 
#> number of iterations= 9 
#> number of iterations= 19 
#> number of iterations= 9 
#> number of iterations= 10 
#> number of iterations= 9 
out.bs
#> $lambda
#>           [,1]      [,2]     [,3]      [,4]      [,5]    [,6]      [,7]
#> [1,] 0.4851113 0.4697443 0.596572 0.4959446 0.5205063 0.59324 0.4924732
#> [2,] 0.5148887 0.5302557 0.403428 0.5040554 0.4794937 0.40676 0.5075268
#>           [,8]      [,9]     [,10]
#> [1,] 0.4883865 0.5253512 0.5085208
#> [2,] 0.5116135 0.4746488 0.4914792
#> 
#> $lambda.se
#> [1] 0.04398025 0.04398025
#> 
#> $beta
#>             [,1]        [,2]        [,3]        [,4]        [,5]        [,6]
#> [1,]  0.56560307  0.57804415  0.54823185  0.60273755  0.57483259  0.57004183
#> [2,]  0.08681471  0.07690188  0.09581534  0.07634211  0.07890199  0.08066379
#> [3,]  1.25511339  1.26317671  1.23389408  1.29143500  1.23384677  1.26902566
#> [4,] -0.08826143 -0.08951039 -0.07887580 -0.11040133 -0.08165614 -0.09158025
#>             [,7]        [,8]        [,9]       [,10]
#> [1,]  0.56143406  0.56872406  0.57677339  0.54933602
#> [2,]  0.08576481  0.07949269  0.07837805  0.09058422
#> [3,]  1.24948630  1.24164986  1.24691726  1.25876179
#> [4,] -0.09137644 -0.08216474 -0.08399838 -0.08860009
#> 
#> $beta.se
#>             [,1]       [,2]
#> [1,] 0.015631449 0.01753614
#> [2,] 0.006509545 0.00880769
#> 
#> $sigma
#>            [,1]       [,2]       [,3]       [,4]       [,5]       [,6]
#> [1,] 0.03470704 0.03252508 0.03526343 0.03421735 0.03179572 0.03252541
#>            [,7]       [,8]       [,9]      [,10]
#> [1,] 0.03406565 0.03067507 0.03347432 0.03049482
#> 
#> $sigma.se
#> [1] 0.001648412
#>