# BootstrapRep: Bootstrap analysis via resampling In evolqg: Tools for Evolutionary Quantitative Genetics

## Description

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

## Usage

 ```1 2``` ```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

`MonteCarloStat`, `AlphaRep`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```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) ```