# boot.compute.fj

0th

Percentile

##### Compute bootstrap resampled fj as supplemental elements.

This function computes a bootstrap resampled set of data and projects fj as supplemental elements.

Keywords
Bootstrap, multivariate, inference
##### Usage
boot.compute.fj(DATA, res, DESIGN = NULL, constrained = FALSE)
##### Arguments
DATA

The original data matrix to be bootstrapped. Rows will be bootstrapped and are assumed to be observations.

res

of class expoOutput. Results from one of the ExPosition methods (e.g., epPCA, epMCA),

DESIGN

A design matrix (in disjunctive coding). Only used if constrained is TRUE.

constrained

a boolean. If TRUE, bootstrap resampling will occur within groups as designated by the DESIGN matrix.

##### Value

fjj

a set of factor scores of the measures (columns, fj) for the bootstrapped data.

##### References

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.

See the functions supplementaryCols and link{boot.samples}

##### Aliases
• boot.compute.fj
##### 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)
}
# }

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

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