ecospat.varpart {ecospat}

Variation Partitioning For GLM Or GAM

Description

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.

Author(s)

Christophe Randin [e-mail], Helene Jaccard and Nigel Gilles Yoccoz

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

if(require("rms",quietly=TRUE)){
  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)
}