Bootstrap analysis via resampling
Calculates the repeatability of the covariance matrix of the suplied data via bootstrap resampling
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
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.