monteBigBAF-class {ssExtra}R Documentation

Class "monteBigBAF"

Description

This class defines a subclass of the virtual "monteDoubleSampling" for use with Monte Carlo simulations of Big BAF sampling.

Objects from the Class

Objects can be created by calls to the constructor: monte by passing a signature object of class "ssBigBAF". Using new("monteBigBAF", ...) directly is discouraged because of the class' complexity.

Slots

In addition to those slots that are defined within the "monteDoubleSampling", the following are available...

mcSamples:

Object of class "numeric": The number of Monte Carlo replications (samples).

n:

Object of class "numeric": A vector of samples sizes to be drawn from the population (surfaces) and replicated mcSamples times.

fpc:

Object of class "numeric": Finite population correction factor.

alpha:

Object of class "numeric": The two-tailed alpha level for normal theory confidence interval construction.

replace:

Object of class "logical": TRUE: each Monte Carlo replicate of size n is drawn with replacement; FALSE: samples are drawn withoug replacement from the surfaces.

ranSeed:

Object of class "numeric": The starting seed for the random number generator.

t.values:

Object of class "numeric": Student's t values for each sample size n with two-tailed alpha-level alpha.

boot:

Object of class "logical": TRUE: include jackknife and bootstrap estimates; FALSE: do not include these.

numBSS:

Object of class "numeric": The number of bootstrap samples to be drawn from each Monte Carlo replicate and sample size if boot=TRUE.

means:

Object of class "list": A list ofmcSamples x length(n) data frames containing means for volume and basal area, tree VBARs, &c.

vars:

Object of class "list": A list of mcSamples x length(n) data frames containing the sample variances.

stDevs:

Object of class "list": A list of mcSamples x length(n) data frames containing the sample standard deviations.

varMeans:

Object of class "list": A list of mcSamples x length(n) data frames containing the sample variances of the means; e.g., Delta Method, Goodman, &c.

stErrs:

Object of class "list": A list of mcSamples x length(n) data frames containing the sample standard errors of the means.

lowerCIs:

Object of class "list": A list of mcSamples x length(n) data frames containing the lower confidence points on volume.

upperCIs:

Object of class "list": A list of mcSamples x length(n) data frames containing the upper confidence points on volume.

caught:

Object of class "list": A list of mcSamples x length(n) data frames containing the percent catch statistics for the above confidence intervals.

otherVarms:

Object of class "list": A list of mcSamples x length(n) data frames containing other variances of the means of interest.

n.tvbar:

Object of class "list": A list of mcSamples x length(n) data frames containing the number of tree VBARS on each replicate for the count and big BAF samples.

corrs:

Object of class "list": A list of mcSamples x length(n) data frames containing the approximate aggregate correlations of various types.

covs:

Object of class "list": A list of mcSamples x length(n) data frames containing the point-based delta method paired covariances. There are no covariances comparable to those in the corrs slot stored since their usefulness is questionable.

gm.all:

Object of class "list": The grand summary means of the above: means, vars, stDevs, varMeans, stErrs, lowerCIS, upperCIs, caught, otherVarms, corrs.

sm.all:

Object of class "list": The sampling variances and standard errors of the means.

mc.samples:

Object of class "list": sample size list of data frames of size length(n) x mcSamples (for each n) holding the actual cell numbers drawn in each Monte Carlo sample replicate.

Extends

Class "monteDoubleSampling", directly.

Methods

hist

signature(x = "monteBigBAF"): Generate histograms

show

signature(object = "monteBigBAF"): Display a summary of the object.

summary

signature(object = "monteBigBAF"): Summary method for the object.

Note

This describes a long complicated object structure. The constructor is also a long routine and takes time to run. It is best not to do bootstrapping if the number of MC samples is large as the bootstrap code is all in R and is very slow.

Author(s)

Jeffrey H. Gove

See Also

The parent (super) class: monteDoubleSampling

Examples

showClass("monteBigBAF")

[Package ssExtra version 0.1-2 Index]