BootstrapRep: Bootstrap analysis via resampling

In evolqg: Evolutionary Quantitative Genetics

Bootstrap analysis via resampling

Description

Calculates the repeatability of the covariance matrix of the supplied data via bootstrap resampling

Usage

BootstrapRep(
  ind.data,
  ComparisonFunc,
  iterations = 1000,
  sample.size = dim(ind.data)[1],
  correlation = FALSE,
  parallel = FALSE
)

Arguments

ind.data	Matrix of residuals or individual measurements
ComparisonFunc	comparison function
iterations	Number of resamples to take
sample.size	Size of resamples, default is the same size as ind.data
correlation	If TRUE, correlation matrix is used, else covariance matrix.
parallel	if TRUE computations are done in parallel. Some foreach backend must be registered, like doParallel or doMC.

Details

Samples with replacement are taken from the full population, a statistic calculated and compared to the full population statistic.

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

MonteCarloStat, AlphaRep

Examples

BootstrapRep(iris[,1:4], MantelCor, iterations = 5, correlation = TRUE)
             
BootstrapRep(iris[,1:4], RandomSkewers, iterations = 50)

BootstrapRep(iris[,1:4], KrzCor, iterations = 50, correlation = TRUE)

BootstrapRep(iris[,1:4], PCAsimilarity, iterations = 50)

#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)
#BootstrapRep(iris[,1:4], PCAsimilarity,
#             iterations = 5,
#             parallel = TRUE)

evolqg documentation built on Aug. 8, 2023, 5:12 p.m.