BootstrapRep function

Bootstrap analysis via resampling

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

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.

Returns

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

Details

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

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)

See Also

MonteCarloStat, AlphaRep

Author(s)

Diogo Melo, Guilherme Garcia