MonteCarloStat function

Parametric population samples with covariance or correlation matrices

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
)

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.

Returns

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.

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)

See Also

BootstrapRep, AlphaRep

Author(s)

Diogo Melo, Guilherme Garcia