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 )
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 the mean repeatability, that is, the mean value of comparisons from samples to original statistic.
Diogo Melo, Guilherme Garcia
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.