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extra_examples.R
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extra_examples.R
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# Source the package
devtools::install_github("beckyfisher/FSSgam_package")
library(FSSgam)
library(RCurl)
################################################################################
### Example showing use of uGamm to allow fitting with gamm4 ##################
# load data coral data set
dat <-read.csv(text=getURL("https://raw.githubusercontent.com/beckyfisher/FSSgam/master/extra_examples_coral_data.csv"))
colnames(dat)
head(dat)
str(dat)
cat.preds=c("Survey","bleach.pres","dredge.pres","dhw.fact")
null.vars=c("Site")
cont.preds=c("av.wave","Depth")
# get rid of NA's and unused columns
use.dat=na.omit(dat[,c(null.vars,cat.preds,cont.preds,"allcoral","totalpoints")])
use.dat$successes=use.dat$allcoral
use.dat$failures=use.dat$totalpoints-use.dat$allcoral
use.dat$trials=use.dat$totalpoints
#test.fit model for all coral, with total points as trials
require(MuMIn)
Model1=uGamm(cbind(successes,failures)~s(Depth,k=4,bs='cr'),
family=binomial(), random=~(1|Site),
data=use.dat,
lme4=TRUE)
model.set=generate.model.set(use.dat=use.dat,
test.fit=Model1,
pred.vars.cont=cont.preds,
pred.vars.fact=cat.preds)
out.list=fit.model.set(model.set)
# examine the output
names(out.list)
out.list$failed.models
length(out.list$success.models)
mod.table=out.list$mod.data.out
mod.table=mod.table[order(mod.table$AICc),]
head(mod.table)
# check the predictor correlation matrix
model.set$predictor.correlations
# now run the same thing using the non.linear correlation matrix
model.set=generate.model.set(use.dat=use.dat,
test.fit=Model1,
pred.vars.cont=cont.preds,
pred.vars.fact=cat.preds,
non.linear.correlations=TRUE)
model.set$predictor.correlations
out.list=fit.model.set(model.set)
mod.table=out.list$mod.data.out
mod.table=mod.table[order(mod.table$AICc),]
head(mod.table)
#--- now an example running across a range of response variables ------------
resp.vars=c("Acropora.spp.","Turbinaria.spp.","Pocillopora.spp.","Porites.spp.")
# get rid of NA's and unused columns
use.dat=na.omit(dat[,c(null.vars,cat.preds,cont.preds,resp.vars,"totalpoints")])
out.all=list()
var.imp=list()
fss.all=list()
top.all=list()
i=1
pdf(file="mod_fits_all.pdf",onefile=T)
for(i in 1:length(resp.vars)){
use.dat$response=use.dat[,resp.vars[i]]
#test.fit model for the particular coral i, with total points as trials
Model1=uGamm(cbind(use.dat$response,use.dat$totalpoints-use.dat$response)~s(Depth,k=4,bs='cr'),
family=binomial(), random=~(1|Site),
data=use.dat,
lme4=TRUE)
model.set=generate.model.set(use.dat=use.dat,
test.fit=Model1,
pred.vars.cont=cont.preds,
pred.vars.fact=cat.preds)
out.list=fit.model.set(model.set)
fss.all=c(fss.all,list(out.list))
mod.table=out.list$mod.data.out
mod.table=mod.table[order(mod.table$AICc),]
out.i=mod.table
out.all=c(out.all,list(out.i))
var.imp=c(var.imp,list(out.list$variable.importance$aic$variable.weights.raw))
all.less.2AICc=mod.table[which(mod.table$delta.AICc<2),]
top.all=c(top.all,list(all.less.2AICc))
# plot the all best models
par(oma=c(1,1,4,1))
for(r in 1:nrow(all.less.2AICc)){
best.model.name=as.character(all.less.2AICc$modname[r])
best.model=out.list$success.models[[best.model.name]]
if(best.model.name!="null"){
plot(best.model$gam,all.terms=T,pages=1,residuals=T,pch=16)
mtext(side=3,text=resp.vars[i],outer=T)}
}
}
dev.off()
names(out.all)=resp.vars
names(var.imp)=resp.vars
names(top.all)=resp.vars
names(fss.all)=resp.vars
all.mod.fits=do.call("rbind",out.all)
all.var.imp=do.call("rbind",var.imp)
top.mod.fits=do.call("rbind",top.all)
require(car)
require(doBy)
require(gplots)
require(RColorBrewer)
pdf(file="var_importance.pdf",height=5,width=7,pointsize=10)
heatmap.2(all.var.imp,notecex=0.4, dendrogram ="none",
col=colorRampPalette(c("yellow","orange","red"))(30),
trace="none",key.title = "",keysize=2,
notecol="black",key=T,
sepcolor = "black",margins=c(12,14), lhei=c(3,10),lwid=c(3,10),
Rowv=FALSE,Colv=FALSE)
dev.off()
write.csv(all.mod.fits,"all_model_fits.csv")
write.csv(top.mod.fits,"top_model_fits.csv")
################################################################################