BootstrapStat function

Non-Parametric population samples and statistic comparison

Random populations are generated via ressampling using the suplied population. A statistic is calculated on the random population and compared to the statistic calculated on the original population.

BootstrapStat(
  ind.data,
  iterations,
  ComparisonFunc,
  StatFunc,
  sample.size = dim(ind.data)[1],
  parallel = FALSE
)

Arguments

• ind.data: Matrix of residuals or indiviual measurments
• iterations: Number of resamples to take
• ComparisonFunc: comparison function
• StatFunc: Function for calculating the statistic
• sample.size: Size of ressamples, default is the same size as ind.data
• 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.

Examples

cov.matrix <- RandomMatrix(5, 1, 1, 10)

BootstrapStat(iris[,1:4], iterations = 50,
               ComparisonFunc = function(x, y) PCAsimilarity(x, y)[1],
               StatFunc = cov)

#Calculating R2 confidence intervals
r2.dist <- BootstrapR2(iris[,1:4], 30)
quantile(r2.dist)

#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)
#BootstrapStat(iris[,1:4], iterations = 100,
#               ComparisonFunc = function(x, y) KrzCor(x, y)[1],
#               StatFunc = cov,
#               parallel = TRUE)

See Also

BootstrapRep, AlphaRep

Author(s)

Diogo Melo, Guilherme Garcia