GLM Permutation Function
A permutation function to get p-values on GLM coefficients and deviance.
ecospat.permut.glm (glm.obj, nperm, verbose = FALSE)
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. |
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.
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.
Christophe Randin christophe.randin@unibas.ch, Antoine Guisan antoine.guisan@unil.ch and Trevor Hastie
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.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.