ErrorRatio: Error variance ratio

In PCovR: Principal Covariates Regression

Error variance ratio

Description

Estimating the ratio of the error variance of the predictor block, versus the error variance of the criterion block.

Usage

ErrorRatio(X, Y, Rmin = 1, Rmax = ncol(X)/3, prepX="stand",prepY="stand")

Arguments

X	Dataframe containing predictor scores
Y	Dataframe containing criterion scores
Rmin	Lowest number of components considered
Rmax	Highest number of components considered
prepX	Preprocessing of predictor scores: standardizing (stand) or centering data (cent)
prepY	Preprocessing of criterion scores: standardizing (stand) or centering data (cent)

Details

An estimate for the error variance of X can be obtained by applying principal component analysis to X and determining the optimal number of components through a scree test; the estimate equals the associated percentage of unexplained variance. The estimate for the error variance of Y boils down to the percentage of unexplained variance when Y is regressed on X. This approach for estimating and was based on the work of Wilderjans, Ceulemans, Van Mechelen, and Van den Berg (2011).

Value

The returned value is the estimated error variance of X, divided by the estimated error variance of Y.

Author(s)

Marlies Vervloet

References

Wilderjans, T. F., Ceulemans, E., Van Mechelen, I., & Van den Berg, R. A. (2011). Simultaneous analysis of coupled data matrices subject to different amounts of noise. British Journal of Mathematical and Statistical Psychology , 64, 277-290.

Marlies Vervloet, Henk A. Kiers, Wim Van den Noortgate, Eva Ceulemans (2015). PCovR: An R Package for Principal Covariates Regression. Journal of Statistical Software, 65(8), 1-14. URL http://www.jstatsoft.org/v65/i08/.

Examples

data(psychiatrists)
ratio <- ErrorRatio(psychiatrists$X,psychiatrists$Y)

PCovR documentation built on Oct. 26, 2023, 9:06 a.m.