| shuffle-package {shuffle} | R Documentation |
The Shuffle Estimator for Explainable Variance
Description
This package implements the algorithms underlying the shuffle estimators, variance estimators for one-way analysis of variance designs. The estimators can overcome correlated noise by recomputing the mean-square-between statistics on a permuted version of the data. The permutations should preserve the noise covariance matrix, but a parametric model for the noise covariance is not necessary. For more details see Benjamini and Yu, and here http://statweb.stanford.edu/~yuvalben.
Two functions implement the important stages of estimation:
prepareShuffle(design_vec, premutation), which preprocess the design and computes the normalization constant for a given permutation.
estimateShuffle(response_vec, prepare), which estimates variances and effect sizes for a specific data vector.
Details
| Package: | shuffle |
| Type: | Package |
| Version: | 1.0.1 |
| Date: | 2013-4-24 |
| License: | GPL (>= 2) |
Author(s)
Yuval Benjamini <yuvalbenj@gmail.com>
References
Benjamini and Yu (2013), "The shuffle estimator for explainable variance in fMRI experiments", Annals of Applied Statistics 7 (4) http://projecteuclid.org/euclid.aoas/1387823308
Examples
data(design_vec,fMRI_responses,prediction_res)
# Make example shorter - for paper example use T = ncol(fMRI_responses)
T = 156*4
fMRI_responses_sm = fMRI_responses[,1:T]
design_sm = design_vec[1:T]
permutation = rev(1:T)
prep_shuffle = prepareShuffle(design_sm,permutation)
var_explained = numeric(nrow(fMRI_responses_sm))
for (i in 1:nrow(fMRI_responses_sm)) {
var_explained[i] = estimateShuffle(fMRI_responses_sm[i,],prep_shuffle)$effect
}
plot(var_explained, pmax(prediction_res,0)^2,
xlim = c(0,0.7), ylim = c(0,0.7),
xlab = "Explainable variance", ylab = "Corr^2")
abline(0,1,col=4)