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# Author: Babak Naimi, naimi.b@gmail.com
# Date (last update): May 2020
# Version 1.3
# Licence GPL v3
#-------------
methodInfo <-list(name=c('gam','GAM'),
packages='mgcv',
modelTypes = c('pa','pb','ab','n'),
fitParams = list(v='sdmVariables',data='sdmDataFrame'),
fitSettings = list(family=binomial(link='logit'),k=-1,bs='tp',weights=NULL,subset=NULL,na.action='na.omit',offset=NULL,method='GCV.Cp',optimizer=c("outer","newton"),select=FALSE,knots=NULL,sp=NULL,min.sp=NULL,H=NULL,gamma=1,fit=TRUE,paraPen=NULL,G=NULL),
fitFunction = function(v,data,k=-1,bs='tp',...) {
.f <- .getFormula.gammgcv(n=c(v@response,v@variables$numeric),nFact = v@variables$nFact,k=k,bs=bs)
gam(formula = .f, data = data,...)
},
settingRules = function(x='sdmVariables',f='fitSettings') {
if (x@distribution == 'poisson') f[['family']] <- x@distribution
else if (x@distribution == 'multinomial') f[['family']] <- 'multinom'
list(fitSettings=f)
},
tuneParams = NULL,
predictParams=list(object='model',newdata='sdmDataFrame'),
predictSettings=list(type='response'),
predictFunction='predict.gam',
#------ metadata (optional):
title='Generalized Additive Models with integrated smoothness estimation',
creator='Babak Naimi',
authors=c('Simon N. Wood'), # authors of the main method
email='naimi.b@gmail.com',
url='http://r-gis.net',
citation=list(bibentry('book',title = " Generalized Additive Models: An Introduction with R",
author = as.person("S. N. Wood [aut]"),
year = "2006",
publisher = "Chapman and Hall/CRC press")
),
description='Fits a generalized additive model (GAM) to data, the term "GAM" being taken to include any quadratically penalized GLM and a variety of other models estimated by a quadratically penalised likelihood type approach [see the help for gam function in mgcv package]'
)
#------------
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