BootstrapStat: Non-Parametric population samples and statistic comparison

In lem-usp/EvolQG: Evolutionary Quantitative Genetics

Non-Parametric population samples and statistic comparison

Description

Random populations are generated via ressampling using the suplied population. A statistic is calculated on the random population and compared to the statistic calculated on the original population.

Usage

BootstrapStat(
  ind.data,
  iterations,
  ComparisonFunc,
  StatFunc,
  sample.size = dim(ind.data)[1],
  parallel = FALSE
)

Arguments

ind.data	Matrix of residuals or indiviual measurments
iterations	Number of resamples to take
ComparisonFunc	comparison function
StatFunc	Function for calculating the statistic
sample.size	Size of ressamples, default is the same size as ind.data
parallel	if TRUE computations are done in parallel. Some foreach backend must be registered, like doParallel or doMC.

Value

returns the mean repeatability, that is, the mean value of comparisons from samples to original statistic.

Author(s)

Diogo Melo, Guilherme Garcia

See Also

BootstrapRep, AlphaRep

Examples

cov.matrix <- RandomMatrix(5, 1, 1, 10)

BootstrapStat(iris[,1:4], iterations = 50,
               ComparisonFunc = function(x, y) PCAsimilarity(x, y)[1],
               StatFunc = cov)

#Calculating R2 confidence intervals
r2.dist <- BootstrapR2(iris[,1:4], 30)
quantile(r2.dist)

#Multiple threads can be used with some foreach backend library, like doMC or doParallel
#library(doParallel)
##Windows:
#cl <- makeCluster(2)
#registerDoParallel(cl)
##Mac and Linux:
#registerDoParallel(cores = 2)
#BootstrapStat(iris[,1:4], iterations = 100,
#               ComparisonFunc = function(x, y) KrzCor(x, y)[1],
#               StatFunc = cov,
#               parallel = TRUE)

lem-usp/EvolQG documentation built on April 14, 2024, 6:21 a.m.