exVar | R Documentation |

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!!

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

`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) |

returns the quotient.

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.

Stefan Schlager

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

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

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