prettyScree {prettyGraphs}

prettyScree

Description

prettyScree creates simple, crisp, publication-style scree plots and “tests” for SVD-based analyses.

Usage

prettyScree(eigs, retain.col = "mediumorchid4", dismiss.col = "gray", 
	perc.exp = 1, n.comps = NULL, broken.stick = TRUE, kaiser = TRUE, 
	main = "")

Arguments

eigs

a vector of positive eigenvalues.

retain.col

a color for components that are kept.

dismiss.col

a color for components that are dismissed.

perc.exp

a value between 0 and 1. Used to retain components that explain perc.comp * 100 variance. Note: perc.exp retains cumsum(explained variance) < (perc.exp * 100) + 1 component.

n.comps

a value between 1 and length(eigs). Used to retain n.comps number of components.

broken.stick

a boolean. If TRUE (default), the broken-stick test is performed (see details).

kaiser

a boolean. If TRUE (default), all components with eigenvalues greater than the mean(eigs) are retained.

main

A title to be placed at the top of the graph.

Details

prettyScree visualizes the distribution of eigenvalues/explained variance from SVD-based analyses. Further, prettyScree performs several rudimentary “tests”. Two rudimentary criteria are implemented: 1) user selected explained variance, and 2) user selected number of components. Additionally, two rudimentary “tests” are implemented: 1) the “broken-stick” distribution of variance model, and 2) the “Kaiser criterion” where all components that explain more variance than the mean are kept.

prettyScree colors components that “pass” all selected tests with retain.col. Any components that do not pass tests are colored by a more transparent version of retain.col. Components that do not meet any criteria for retention are colored by dismiss.col.

prettyScree should be considered “under development” as of 12.09.2013. The function works, but we will be adding more features in the near future.

Value

comps.tests

a matrix with boolean values. Rows indicate which criteria are used, and columns correspond to components. If TRUE, a component is considered “retained” by that test/criteria. If FALSE, the component is “dismissed” according to that test/criteria.

Note

A private function (add.alpha) was copied from http://lamages.blogspot.com/2013/04/how-to-change-alpha-value-of-colours-in.html

Author(s)

Derek Beaton

References

Cangelosi, R., & Goriely, A. (2007). Component retention in principal component analysis with application to cDNA microarray data. Biology direct, 2(2), 1–21.

Peres-Neto, P. R., Jackson, D. A., & Somers, K. M. (2005). How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Computational Statistics & Data Analysis, 49(4), 974–997.

See Also

Also see (some of) the other packages that perform a wide array of SVD-based analyses: ExPosition, FactoMineR, ca, ade4

Examples

	prcomp.res <- prcomp(USArrests, scale = TRUE)
	prettyScree(prcomp.res$sdev^2)
	##or
	princomp.res <- princomp(USArrests, cor = TRUE)	
	prettyScree(princomp.res$sdev^2)