evolqg (version 0.1-7)

MonteCarloStat: Parametric population samples with covariance or correlation matrices

Description

Using a multivariate normal model, random populations are generated using the suplied covariance matrix. A statistic is calculated on the random population and compared to the statistic calculated on the original matrix.

Usage

MonteCarloStat(cov.matrix, sample.size, iterations, ComparisonFunc, StatFunc,
  parallel = FALSE)

Arguments

cov.matrix

Covariance matrix.

sample.size

Size of the random populations

iterations

Number of random populations

ComparisonFunc

Comparison functions for the calculated statistic

StatFunc

Function for calculating the statistic

parallel

if TRUE computations are done in parallel. Some foreach backend must be registered, like doParallel or doMC.

Value

• returns the mean repeatability, or mean value of comparisons from samples to original statistic.

Details

Since this function uses multivariate normal model to generate populations, only covariance matrices should be used.

See Also

BootstrapRep, AlphaRep

Examples

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)

#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)
#MonteCarloStat(cov.matrix, sample.size = 30, iterations = 100,
#               ComparisonFunc = function(x, y) KrzCor(x, y)[1],
#               StatFunc = cov,
#               parallel = TRUE)