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# Author: Babak Naimi, naimi.b@gmail.com
# Date (last update): Dec. 2020
# Version 1.0
# Licence GPL v3
#-------------
methodInfo <-list(name=c('glmpoly','glmpolynomial','glmp'),
packages='stats',
modelTypes = c('pa','pb','ab','n'),
fitParams = list(v='sdmVariables',data='sdmDataFrame'),
fitSettings = list(family=binomial(link='logit'),degree=3,weights=NULL,model=FALSE),
fitFunction = function(v,data,degree=3,...) {
.f <- .getFormula.glmPoly(n=c(v@response,v@variables$numeric),nFact = v@variables$nFact,degree=degree)
glm(formula = .f, data = data,...)
},
settingRules = function(x='sdmVariables',f='fitSettings') {
if (x@distribution %in% c('poisson','multinomial')) {
f[['family']] <- x@distribution
}
list(fitSettings=f)
},
tuneParams = NULL,
predictParams=list(object='model',newdata='sdmDataFrame'),
predictSettings=list(type='response'),
predictFunction='predict.glm',
#------ metadata (optional):
title='Generalized Linear Model (Polynomial)',
creator='Babak Naimi',
authors=c('R Core team'), # authors of the main method
email='naimi.b@gmail.com',
url='http://r-gis.net',
citation=list(bibentry('book',title = "Generalized linear models",
author = as.person("P. McCullagh [aut], J. A. Nelder [aut]"),
year = "1989",
publisher = "Chapman and Hall",
address = "London")
),
description='glm is used to fit generalized linear models, specified by giving a symbolic description of the linear predictor and a description of the error distribution [see the help for glm function in stats package]'
)
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