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

1 2 | ```
BootstrapRep(ind.data, ComparisonFunc, iterations = 1000,
sample.size = dim(ind.data)[1], correlation = FALSE, parallel = FALSE)
``` |

`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. |

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

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

Diogo Melo, Guilherme Garcia

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)
``` |

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