MonteCarloStat: Parametric population samples with covariance or correlation...

In lem-usp/EvolQG: Evolutionary Quantitative Genetics

Parametric population samples with covariance or correlation matrices

Description

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.

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 back-end must be registered, like doParallel or doMC.

Details

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

Value

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

Author(s)

Diogo Melo, Guilherme Garcia

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)

## Not run: 
#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)

## End(Not run)

lem-usp/EvolQG documentation built on April 14, 2024, 6:21 a.m.