boot-methods {rebmix}

Parametric or Nonparametric Bootstrap for Standard Error and Coefficient of Variation Estimation

Description

Returns as default the boot output for mixtures of conditionally independent normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or von Mises component densities. If x is of class RNGMVNORM the boot output for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices is returned.

Usage

## S4 method for signature 'REBMIX'
boot(x = NULL, rseed = -1, pos = 1, Bootstrap = "parametric",
     B = 100, n = numeric(), replace = TRUE, prob = numeric(), ...)
## ... and for other signatures
## S4 method for signature 'REBMIX.boot'
summary(object, ...)
## ... and for other signatures

Arguments

x

see Methods section below.

rseed

set the random seed to any negative integer value to initialize the sequence. The first bootstrap dataset corresponds to it. For each next bootstrap dataset the random seed is decremented r_{\mathrm{seed}} = r_{\mathrm{seed}} - 1. The default value is -1.

pos

a desired row number in x@summary to be bootstrapped. The default value is 1.

Bootstrap

a character giving the bootstrap type. One of default "parametric" or "nonparametric".

B

number of bootstrap datasets. The default value is 100.

n

number of observations. The default value is numeric().

replace

logical. The sampling is with replacement if TRUE, see also sample. The default value is TRUE.

prob

a vector of length n containing probability weights, see also sample. The default value is numeric().

...

maximum number of components cmax, minimum number of components cmin and further arguments to sample; additional arguments affecting the summary produced.

object

see Methods section below.

Value

Returns an object of class REBMIX.boot or REBMVNORM.boot.

Methods

signature(x = "REBMIX")

an object of class REBMIX for mixtures of conditionally independent normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or von Mises component densities.

signature(x = "REBMVNORM")

an object of class REBMVNORM for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices.

signature(object = "REBMIX")

an object of class REBMIX.

signature(object = "REBMVNORM")

an object of class REBMVNORM.

Author(s)

Marko Nagode

References

G. McLachlan and D. Peel. Finite Mixture Models. John Wiley & Sons, New York, 2000.

Examples

## Not run: 
data(weibull)

# Create object of class EM.Control.

EM <- new("EM.Control", strategy = "single", variant = "EM",
  acceleration = "fixed", acceleration.multiplier = 1.0, tolerance = 1.0E-4,
  maximum.iterations = 1000)

# Estimate number of components, component weights and component parameters.

weibullest <- REBMIX(Dataset = list(weibull),
  Preprocessing = "kernel density estimation",
  cmin = 2,
  cmax = 4,
  Criterion = "BIC",
  pdf = "Weibull",
  EMcontrol = EM)

# Plot finite mixture.

plot(weibullest, what = c("pdf", "marginal cdf", "IC", "logL", "D"),
  nrow = 3, ncol = 2, npts = 1000)

# Bootstrap finite mixture.

weibullboot <- boot(x = weibullest, Bootstrap = "nonparametric", B = 10)

weibullboot

## End(Not run)