ecospat (version 3.2)

ecospat.permut.glm: GLM Permutation Function

Description

A permutation function to get p-values on GLM coefficients and deviance.

Usage

ecospat.permut.glm (glm.obj, nperm, verbose = FALSE)

Arguments

glm.obj

Any calibrated GLM or GAM object with a binomial error distribution.

nperm

The number of permutations in the randomization process.

verbose

Boolean indicating whether to print progress output during calculation. Default is FALSE.

Value

Return p-values that are how the true parameters of the original model deviate from the disribution of the random parameters. A p-value of zero means that the true parameter is completely outside the random distribution.

Details

Rows of the response variable are permuted and a new GLM is calibrated as well as deviance, adjusted deviance and coefficients are calculated. These random parameters are compared to the true parameters in order to derive p-values.

References

Hastie, T., R. Tibshirani and J. Friedman. 2001. Elements of Statistical Learning; Data Mining, Inference, and Prediction, Springer-Verlag, New York.

Legendre, P. and L. Legendre. 1998. Numerical Ecology, 2nd English edition. Elsevier Science BV, Amsterdam.

Examples

# NOT RUN {
# }
# NOT RUN {
library(rms)
data('ecospat.testData')

# data for Soldanella alpina
data.Solalp<- ecospat.testData[c("Soldanella_alpina","ddeg","mind","srad","slp","topo")] 

# gbm model for Soldanella alpina

glm.Solalp <- glm(Soldanella_alpina ~ pol(ddeg,2) + pol(mind,2) + pol(srad,2) + pol(slp,2) 
      + pol(topo,2), data = data.Solalp, family = binomial)
                  
# p-values
ecospat.permut.glm (glm.Solalp, 1000)
# }
# NOT RUN {
# }