Non-Parametric rarefacted population samples and statistic comparison
Calculates the repeatability of a statistic of the data, such as correlation or covariance matrix, via resampling with varying sample sizes, from 2 to the size of the original data.
RarefactionStat( ind.data, StatFunc, ComparisonFunc, ..., num.reps = 10, replace = FALSE, parallel = FALSE )
ind.data |
Matrix of residuals or indiviual measurments |
StatFunc |
Function for calculating the statistic |
ComparisonFunc |
comparison function |
... |
Aditional arguments passed to ComparisonFunc |
num.reps |
number of populations sampled per sample size |
replace |
If true, samples are taken with replacement |
parallel |
if TRUE computations are done in parallel. Some foreach backend must be registered, like doParallel or doMC. |
Samples of various sizes, without replacement, are taken from the full population, a statistic calculated and compared to the full population statistic.
A specialized ploting function displays the results in publication quality.
Bootstraping may be misleading with very small sample sizes. Use with caution.
returns the mean value of comparisons from samples to original statistic, for all sample sizes.
Diogo Melo, Guilherme Garcia
ind.data <- iris[1:50,1:4] #Can be used to calculate any statistic via Rarefaction, not just comparisons #Integration, for instanse: results.R2 <- RarefactionStat(ind.data, cor, function(x, y) CalcR2(y), num.reps = 5) #Easy access library(reshape2) melt(results.R2) #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) #results.R2 <- RarefactionStat(ind.data, cor, function(x, y) CalcR2(y), parallel = TRUE)
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