# ecospat.varpart

From ecospat v3.1
by Olivier Broennimann

##### Variation Partitioning For GLM Or GAM

Perform variance partitioning for binomial GLM or GAM based on the deviance of two groups or predicting variables.

##### Usage

`ecospat.varpart (model.1, model.2, model.12)`

##### Arguments

- model.1
GLM / GAM calibrated on the first group of variables.

- model.2
GLM / GAM calibrated on the second group of variables.

- model.12
GLM / GAM calibrated on all variables from the two groups.

##### Details

The deviance is calculated with the adjusted geometric mean squared improvement rescaled for a maximum of 1.

##### Value

Return the four fractions of deviance as in Randin et al. 2009: partial deviance of model 1 and 2, joined deviance and unexplained deviance.

##### References

Randin, C.F., H. Jaccard, P. Vittoz, N.G. Yoccoz and A. Guisan. 2009. Land use improves spatial predictions of mountain plant abundance but not presence-absence. *Journal of Vegetation Science*, **20**, 996-1008.

##### Examples

```
# NOT RUN {
library(rms)
data('ecospat.testData')
# data for Soldanella alpina and Achillea millefolium
data.Solalp<- ecospat.testData[c("Soldanella_alpina","ddeg","mind","srad","slp","topo")]
# glm models for Soldanella alpina
glm.Solalp1 <- glm("Soldanella_alpina ~ pol(ddeg,2) + pol(mind,2) + pol(srad,2)",
data = data.Solalp, family = binomial)
glm.Solalp2 <- glm("Soldanella_alpina ~ pol(slp,2) + pol(topo,2)",
data = data.Solalp, family = binomial)
ecospat.varpart (model.1= glm.Solalp1, model.2= glm.Solalp2, model.12= glm.Solalp2)
# }
```

*Documentation reproduced from package ecospat, version 3.1, License: GPL*

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