Rarefaction function

Rarefaction analysis via resampling

Calculates the repeatability of a statistic of the data, such as correlation or covariance matrix, via bootstrap resampling with varying sample sizes, from 2 to the size of the original data.

Rarefaction(
  ind.data,
  ComparisonFunc,
  ...,
  num.reps = 10,
  correlation = FALSE,
  replace = FALSE,
  parallel = FALSE
)

Arguments

• ind.data: Matrix of residuals or individual measurments
• ComparisonFunc: comparison function
• ...: Additional arguments passed to ComparisonFunc
• num.reps: number of populations sampled per sample size
• correlation: If TRUE, correlation matrix is used, else covariance matrix. MantelCor always uses correlation matrix.
• replace: If true, samples are taken with replacement
• parallel: if TRUE computations are done in parallel. Some foreach back-end must be registered, like doParallel or doMC.

Returns

returns the mean value of comparisons from samples to original statistic, for all sample sizes.

Details

Samples of various sizes, with replacement, are taken from the full population, a statistic calculated and compared to the full population statistic.

A specialized plotting function displays the results in publication quality.

Bootstraping may be misleading with very small sample sizes. Use with caution if original sample sizes are small.

Examples

ind.data <- iris[1:50,1:4]

results.RS <- Rarefaction(ind.data, RandomSkewers, num.reps = 5)
#' #Easy parsing of results
library(reshape2)
melt(results.RS)

# or :

results.Mantel <- Rarefaction(ind.data, MatrixCor, correlation = TRUE, num.reps = 5)
results.KrzCov <- Rarefaction(ind.data, KrzCor, num.reps = 5)
results.PCA <- Rarefaction(ind.data, PCAsimilarity, num.reps = 5)

## Not run:

#Multiple threads can be used with some foreach backend library, like doMC or doParallel
library(doMC)
registerDoMC(cores = 2)
results.KrzCov <- Rarefaction(ind.data, KrzCor, num.reps = 5, parallel = TRUE)
## End(Not run)

See Also

BootstrapRep

Author(s)

Diogo Melo, Guilherme Garcia