Bootstrap analysis via resampling

Description

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

Usage

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

Arguments

ind.data

Matrix of residuals or indiviual measurments

ComparisonFunc

comparison function

iterations

Number of resamples to take

sample.size

Size of ressamples, 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

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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)

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