# ErrorRatio: Error variance ratio In PCovR: Principal Covariates Regression

## Description

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

## Usage

 `1` ```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.

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

 ```1 2``` ```data(psychiatrists) ratio <- ErrorRatio(psychiatrists\$X,psychiatrists\$Y) ```

PCovR documentation built on June 20, 2017, 9:15 a.m.