reorient: Reorientation: applying PCA (Principal Component Analysis)

In baumer-lab/cranium: Quantifying Radial Bridge Structures

Description

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components.https://en.wikipedia.org/wiki/Principal_component_analysis

Usage

reorient(x, ...)

Arguments

x	a tbl_brain
...	arguments passed to prcomp
correctionA	identify PCA alignment error type A and make correction is_errorA

Examples

tidy_brain <- read_h5(download_brains(tempdir(), pattern = "101"))%>%
       tidy(type="wildtype", threshold = 0.9)
ro_tidy_brin <- reorient(tidy_brain)

baumer-lab/cranium documentation built on Nov. 3, 2019, 2:07 p.m.