exVar: calculate variance of a distribution stemming from prediction...

In zarquon42b/Morpho: Calculations and Visualisations Related to Geometric Morphometrics

calculate variance of a distribution stemming from prediction models

Description

calculates a quotient of the overall varriance within a predicted distribution to that from the original one. This function calculates a naive extension of the univariate R^2-value by dividing the variance in the predicted dat by the variance of the original data. No additional adjustments are made!!

Usage

exVar(model, ...)

## S3 method for class 'lm'
exVar(model, ...)

## S3 method for class 'mvr'
exVar(model, ncomp, val = FALSE, ...)

Arguments

model	a model of classes "lm" or "mvr" (from the package "pls")
...	currently unused additional arguments.
ncomp	How many latent variables to use (only for mvr models)
val	use cross-vaildated predictions (only for mvr models)

Value

returns the quotient.

Note

The result is only!! a rough estimate of the variance explained by a multivariate model. And the result can be misleading - especially when there are many predictor variables involved. If one is interested in the value each factor/covariate explains, we recommend a 50-50 MANOVA perfomed by the R-package "ffmanova", which reports this value factor-wise.

Author(s)

Stefan Schlager

References

Langsrud O, Juergensen K, Ofstad R, Naes T. 2007. Analyzing Designed Experiments with Multiple Responses Journal of Applied Statistics 34:1275-1296.

Examples

lm1 <- lm(as.matrix(iris[,1:4]) ~ iris[,5])
exVar(lm1)

zarquon42b/Morpho documentation built on Jan. 28, 2024, 2:11 p.m.