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Using a multivariate normal model, random populations are generated using the supplied covariance matrix. A statistic is calculated on the random population and compared to the statistic calculated on the original matrix.
MonteCarloStat(
cov.matrix,
sample.size,
iterations,
ComparisonFunc,
StatFunc,
parallel = FALSE
)
returns the mean repeatability, or mean value of comparisons from samples to original statistic.
Covariance matrix.
Size of the random populations
Number of random populations
Comparison functions for the calculated statistic
Function for calculating the statistic
if TRUE computations are done in parallel. Some foreach back-end must be registered, like doParallel or doMC.
Diogo Melo, Guilherme Garcia
Since this function uses multivariate normal model to generate populations, only covariance matrices should be used.
BootstrapRep
, AlphaRep
cov.matrix <- RandomMatrix(5, 1, 1, 10)
MonteCarloStat(cov.matrix, sample.size = 30, iterations = 50,
ComparisonFunc = function(x, y) PCAsimilarity(x, y)[1],
StatFunc = cov)
#Calculating R2 confidence intervals
r2.dist <- MonteCarloR2(RandomMatrix(10, 1, 1, 10), 30)
quantile(r2.dist)
if (FALSE) {
#Multiple threads can be used with some foreach backend library, like doMC or doParallel
##Windows:
#cl <- makeCluster(2)
#registerDoParallel(cl)
##Mac and Linux:
library(doParallel)
registerDoParallel(cores = 2)
MonteCarloStat(cov.matrix, sample.size = 30, iterations = 100,
ComparisonFunc = function(x, y) KrzCor(x, y)[1],
StatFunc = cov,
parallel = TRUE)
}
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