evolqg (version 0.1-7)

Rarefaction: Rarefaction analysis via ressampling

Description

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.

Usage

Rarefaction(ind.data, ComparisonFunc, ..., iterations = 1000, num.reps = 10,
  correlation = FALSE, ret.dim = NULL, parallel = FALSE)
PlotRarefaction(comparison.list, y.axis = "Statistic")

Arguments

ind.data

Matrix of residuals or indiviual measurments

ComparisonFunc

comparison function

...

Aditional arguments passed to ComparisonFunc

iterations

Parameter for comparison function. Number of random skewers or number of permutations in mantel.

num.reps

number of populations sampled per sample size

correlation

If TRUE, correlation matrix is used, else covariance matrix. MantelCor always uses correlation matrix.

ret.dim

When using Krzanowski Correlation, number of retained dimensions may be specified

parallel

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

comparison.list

output from rarefaction functions can be used in ploting

y.axis

Y axis lable in plot

Value

• 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 ploting function displays the results in publication quality.

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

See Also

BootstrapRep

Examples

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

results.RS <- Rarefaction(ind.data, PCAsimilarity, num.reps = 5, iterations = 100)
results.Mantel <- Rarefaction(ind.data, MatrixCor, correlation = TRUE,
                              num.reps = 5, iterations = 100)
results.KrzCov <- Rarefaction(ind.data, KrzCor, num.reps = 5, iterations = 100)
results.PCA <- Rarefaction(ind.data, PCAsimilarity, num.reps = 5, iterations = 100)

#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.KrzCov <- Rarefaction(ind.data, KrzCor, num.reps = 5, parallel = TRUE)

#Easy access
library(reshape2)
melt(results.RS)

#Plotting using ggplot2
a <- PlotRarefaction(results.RS, "Random Skewers")
b <- PlotRarefaction(results.Mantel, "Mantel")
c <- PlotRarefaction(results.KrzCov, "KrzCor")
d <- PlotRarefaction(results.PCA, "PCAsimilarity")

library(grid)
grid.newpage()
pushViewport(viewport(layout = grid.layout(2, 2)))
vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y)
print(a, vp = vplayout(1, 1))
print(b, vp = vplayout(1, 2))
print(c, vp = vplayout(2, 1))
print(d, vp = vplayout(2, 2))