# boot.ratio.test

0th

Percentile

##### Performs bootstrap ratio test.

Performs bootstrap ratio test which is analogous to a t- or z-score.

Keywords
Bootstrap, multivariate, inference
##### Usage
boot.ratio.test(boot.cube, critical.value = 2)
##### Arguments
boot.cube

an array. This is the bootstrap resampled data. dim 1 (rows) are the items to be tested (e.g., fj, see boot.compute.fj). dim 2 (columns) are the components from the supplemental projection. dim 3 (depth) are each bootstrap sample.

critical.value

numeric. This is the value that would be used as a cutoff in a t- or z-test. Default is 2 (i.e., 1.96 rounded up). The higher the number, the more difficult to reject the null.

##### Value

A list with the following items: return(list(sig.boot.ratios=significant.boot.ratios,boot.ratios=boot.ratios,critical.value=critical.value))

sig.boot.ratios

This is a matrix with the same number of rows and columns as boot.cube. If TRUE, the bootstrap ratio was larger than critical.value. If FALSE, it was smaller.

boot.ratios

This is a matrix with bootstrap ratio values that has the same number of rows and columns as boot.cube.

critical.value

the critical value input is also returned.

##### References

The name bootstrap ratio comes from the Partial Least Squares in Neuroimaging literature. See: McIntosh, A. R., & Lobaugh, N. J. (2004). Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage, 23, S250--S263. The bootstrap ratio is related to other tests of values with respect to the bootstrap distribution, such as the Interval-t. See: Chernick, M. R. (2008). Bootstrap methods: A guide for practitioners and researchers (Vol. 619). Wiley-Interscience. Hesterberg, T. (2011). Bootstrap. Wiley Interdisciplinary Reviews: Computational Statistics, 3, 497<U+2013>526.

boot.compute.fj

##### Aliases
• boot.ratio.test
##### Examples
# NOT RUN {
##the following code generates 100 bootstrap resampled
##projections of the measures from the Iris data set.
data(ep.iris)
data <- ep.iris$data design <- ep.iris$design
iris.pca <- epGPCA(data,scale="SS1",DESIGN=design,make_design_nominal=FALSE)
boot.fjs.unconstrained <- array(0,dim=c(dim(iris.pca$ExPosition.Data$fj),100))
boot.fjs.constrained <- array(0,dim=c(dim(iris.pca$ExPosition.Data$fj),100))
for(i in 1:100){
#unconstrained means we resample any of the 150 flowers
boot.fjs.unconstrained[,,i] <- boot.compute.fj(ep.iris\$data,iris.pca)
#constrained resamples within each of the 3 groups
boot.fjs.constrained[,,i] <- boot.compute.fj(data,iris.pca,design,TRUE)
}
#now compute the bootstrap ratios:
ratios.unconstrained <- boot.ratio.test(boot.fjs.unconstrained)
ratios.constrained <- boot.ratio.test(boot.fjs.constrained)
# }

Documentation reproduced from package InPosition, version 0.12.7.1, License: GPL-2

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