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Appendix S3: Code for analyses:
### Code for the univariate analysis, prediction, fixed effects and multivariate models###
Notes:
File names in the code (i.e. “dispersal4mltvar.csv” and "eu4predict.csv" need to be changed
to own personalised file names.
If model-building data is changed then the multivariate model (sub-models) would need to be
rebuilt.
Any data for prediction must be in the same layout as model-building dataset (refer to
Appendix S2).
library(lme4)
# The original data for building the models
dat<-read.csv("dispersal.csv") # The following variable names are from the original
dispersal data
dat<-dat[order(dat$family,dat$species),]
dat<-dat[dat$other==0,] # Adjust the original dispersal data
# The function of univariate analysis
unimod<-function(y,X,z) {
n.vars<-dim(X)[2]
y.fit = y
X.fit = data.frame(X,z)
Mod.fit = data.frame(y.fit,X.fit)
colnames(Mod.fit)[1]<-"y.fit"
colnames(Mod.fit)[ncol(Mod.fit)]<-"Family"
vnm<-names(X)[1]
form<-as.formula(paste("y.fit~", vnm,"+(1|Family)"))
mm<-lmer(form,family=binomial,data=Mod.fit)
pp<-pnorm(fixef(mm)/sqrt(diag(vcov(mm))))
pp<-2*(ifelse(pp>0.5,1-pp,pp))
univar<-c(vnm,floor(cbind(fixef(mm),sqrt(diag(vcov(mm))),pp)[-1,]*10000)/10000)
for (ii in 2:n.vars){
vnm<-names(X)[ii]
form<-as.formula(paste("y.fit~", vnm,"+(1|Family)"))
mm<-lmer(form,family=binomial,data=Mod.fit)
pp<-pnorm(fixef(mm)/sqrt(diag(vcov(mm))))
pp<-2*(ifelse(pp>0.5,1-pp,pp))
univar<-rbind(univar,c(vnm,floor(cbind(fixef(mm),sqrt(diag(vcov(mm))),pp)[1,]*10000)/10000)) }
return(univar)
}
# The univariate analysis of the response variables
nn<-ncol(dat)-2 # Define the column of the last response variable
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y<-dat[,23] # The first response variable
z<-dat[,2] # The column of the family for each species
x<-dat[,c(4,7:12,14:22,(nn+1):(nn+2))] # The columns for the independent variables
gr<-cbind(names(dat)[23],unimod(y,x,z))
# Do the univariate analysis of the second up to the last response variable
for (kk in 24:nn) {
y<-dat[,kk]
z<-dat[,2]
x<-dat[,c(4,7:12,14:22,(nn+1):(nn+2))]
gr<-rbind(gr,cbind(names(dat)[kk],unimod(y,x,z)))
}
gr<-rbind(c("response","variable","coef","se","pvalue"),gr)
# the outputs of the fixed effects in the univariate analysis
write.table(gr,file="dispersal4univar.csv",sep=",",row.names=FALSE,col.names=TRUE)
# The extract function of fixed effects in the multivariate analysis
fxf<-function(yy){
pp<-pnorm(fixef(yy)/sqrt(diag(vcov(yy))))
pp<-2*(ifelse(pp>0.5,1-pp,pp))
rsp<-strsplit(as.character(formula(yy)),"~",fixed=TRUE)[[2]]
if (length(fixef(yy)[-1])==1) fxr<-c(rsp,names((fixef(yy)[1])),floor(deviance(yy)),floor(cbind(fixef(yy),sqrt(diag(vcov(yy))),pp)[-1,]*10000)/10000)
if (length(fixef(yy)[-1])>1) fxr<-cbind(rsp,names((fixef(yy)[1])),floor(deviance(yy)),floor(cbind(fixef(yy),sqrt(diag(vcov(yy))),pp)[-1,]*10000)/10000)
fxr }
# New data for the prediction and their variables names need to be the same as the original
data
dat0<-read.csv("euspecies.csv")
dat0<-dat0[order(dat0$family,dat0$species),]
dat0<-dat0[dat0$other==0,]
# The extract function for predicting the new data
prd<-function(yy){
fmn<-data.frame(cbind(family=rownames((ranef(yy))$family),p=(ranef(yy))$family[,1]))
fmn<-fmn[order(fmn$family),]
dat1<-merge(dat0,fmn,all.x=TRUE)
dat1<-dat1[!is.na(dat1$p),]
dat1$p<-as.numeric(levels(dat1$p)[dat1$p])
xname<-names(fixef(yy)[-1])
cnf<-eval(parse(text=paste("dat1$",xname[1],sep="")))*fixef(yy)[2]
if (length(xname)>1) {
for (ii in 2:length(xname))
cnf<-cnf+eval(parse(text=paste("dat1$",xname[ii],sep="")))*fixef(yy)[(ii+1)] }
cnf<-cnf+dat1$p+fixef(yy)[1]
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dat1$p<-floor(exp(cnf)/(1+exp(cnf))*10000)/10000
rsp<-strsplit(as.character(formula(yy)),"~",fixed=TRUE)[[2]]
dat1<-dat1[,c(1:2,ncol(dat1))]
colnames(dat1)[3]<-rsp
dat1<-dat1[order(dat1$family,dat1$species),]
dat1 }
# Multivariate models need to be rebuilt if the original data are changed
mm<-lmer(wind~shrub+tree+canseed+(1|family),family=binomial,data=dat)
mltv<-fxf(mm)
prdct<-prd(mm)
mm<-lmer(water~herb+F+sdwt100+(1|family),family=binomial,data=dat)
mltv<-rbind(mltv,fxf(mm))
prdct<-merge(prdct,prd(mm),all=TRUE)
mm<-lmer(Ant~climber+herb+tree+GSWM+RF+(1|family),family=binomial,data=dat)
mltv<-rbind(mltv,fxf(mm))
prdct<-merge(prdct,prd(mm),all=TRUE)
mm<lmer(endo~climber+herb+tree+RF+sdwt01+sdwt100+(1|family),family=binomial,data=dat)
mltv<-rbind(mltv,fxf(mm))
prdct<-merge(prdct,prd(mm),all=TRUE)
mm<-lmer(exo~GSWM+(1|family),family=binomial,data=dat)
mltv<-rbind(mltv,fxf(mm))
prdct<-merge(prdct,prd(mm),all=TRUE)
mm<-lmer(vert~climber+tree+RF+sdwt01+sdwt100+(1|family),family=binomial,data=dat)
mltv<-rbind(mltv,fxf(mm))
prdct<-merge(prdct,prd(mm),all=TRUE)
mm<-lmer(none~herb+tree+GSWM+sdwt01+(1|family),family=binomial,data=dat)
mltv<-rbind(mltv,fxf(mm))
prdct<-merge(prdct,prd(mm),all=TRUE)
mltv<-rbind(c("response","variable","deviance","coef","se","pvalue"),mltv)
# The ouputs of the fixed effects in the multivariate analysis
write.table(mltv,file="dispersal4mltvar.csv",sep=",",row.names=FALSE,col.names=TRUE)
# The outputs of the prediction
write.table(prdct,file="eu4predict.csv",sep=",",row.names=FALSE,col.names=TRUE)
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