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BirdRangeChangePredictors.SEK_clim.r
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BirdRangeChangePredictors.SEK_clim.r
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## CJA Bradshaw
## British birds - range change ecological correlates (Cagan Sekercioglu's data with contagion stats from Miguel)
## 25/01/2012; Mataelpino, Spain; updated 03/04/2012
## updated December 2012; February 2013
## Remove everything
rm(list = ls())
{ ## Load libraries
library(boot)
#library(qpcR)
library(lme4)
#library(spdep)
library(sampling)
library(vegan)
#library(FactoMineR)
}
# { ## Functions
#
# scale <- function(x) {
# x <- (x-min(x,na.rm=T)) / (max(x,na.rm=T)-min(x,na.rm=T))
# x
# }
#
# meancent = function(x,y){
# out = {}
# for (i in unique(y)){
# out[y==i] = x[y==i]-mean(x[y==i])
# }
# out
# }
# }
## functions
delta.IC <- function(x) x - min(x) ## where x is a vector of an IC
weight.IC <- function(x) (exp(-0.5*x))/sum(exp(-0.5*x)) ## Where x is a vector of dIC
# Set functions
AICc <- function(...) {
models <- list(...)
num.mod <- length(models)
AICcs <- numeric(num.mod)
ns <- numeric(num.mod)
ks <- numeric(num.mod)
AICc.vec <- rep(0,num.mod)
for (i in 1:num.mod) {
if (length(models[[i]]$df.residual) == 0) n <- models[[i]]$dims$N else n <- length(models[[i]]$residuals)
if (length(models[[i]]$df.residual) == 0) k <- sum(models[[i]]$dims$ncol) else k <- (length(models[[i]]$coeff))+1
AICcs[i] <- (-2*logLik(models[[i]])) + ((2*k*n)/(n-k-1))
ns[i] <- n
ks[i] <- k
AICc.vec[i] <- AICcs[i]
}
return(AICc.vec)
}
linreg.ER <- function(x,y) { # where x and y are vectors of the same length; calls AICc, delta.AIC, weight.AIC functions
fit.full <- lm(y ~ x); fit.null <- lm(y ~ 1)
AIC.vec <- c(AICc(fit.full),AICc(fit.null))
dAIC.vec <- delta.IC(AIC.vec); wAIC.vec <- weight.IC(dAIC.vec)
ER <- wAIC.vec[1]/wAIC.vec[2]
r.sq.adj <- as.numeric(summary(fit.full)[9])
return(c(ER,r.sq.adj))
}
## source
#source("C:/Program Files/R/new_lmer_AIC_tables3.R")
source("/Applications/RStudio.app/Contents/Resources/R/new_lmer_AIC_tables3.R")
## Import data
#setwd("C:\\Users\\Public\\Documents\\Papers\\Extinctions\\Bird Range Change\\")
setwd("~/Documents/Papers/Birds/UK Bird Range Change/data/")
datwk.orig <- read.table("birdcagan.csv", header=T, sep=",")
len.dat <- dim(datwk.orig)[1]
prc <- 100*(datwk.orig$t2 - datwk.orig$t1)/datwk.orig$t1
prc.tr <- prc + 101
lprc <- log10(abs(prc))
lprc.tr <- log10(prc.tr)
t1sq <- datwk.orig$t1^2
diff <- datwk.orig$t1 - datwk.orig$t2
diff.tr <- diff + abs(range(diff)[1]) + 1
frc <- 100*(datwk.orig$PredFutRangeSize3 - datwk.orig$PredPresRangeSize3)/datwk.orig$PredPresRangeSize3
frc.tr <- frc + 45
frdiff <- datwk.orig$PredFutRangeSize3 - datwk.orig$PredPresRangeSize3
lMass <- (as.vector(scale(datwk.orig$Avg..Mass,center=F,scale=T)))
Mass <- (datwk.orig$Avg..Mass)
lClutch <- as.vector(scale((datwk.orig$Min+datwk.orig$Max)/2,center=T,scale=T))
Clutch <- ((datwk.orig$Min+datwk.orig$Max)/2)
lLong <- log10(as.vector(scale(datwk.orig$NatL,center=F,scale=T)))
Long <- (datwk.orig$NatL)
Mat.Avg <- 12*((datwk.orig$MatMin + datwk.orig$MatMax)/2)
MinAge <- datwk.orig$MinAge
lMAB <- log10(ifelse((is.na(Mat.Avg) == T), MinAge, Mat.Avg))
MAB <- (ifelse((is.na(Mat.Avg) == T), MinAge, Mat.Avg))
lcont <- as.vector(scale(datwk.orig$cont.avg,center=F,scale=T))
lclump <- as.vector(scale(datwk.orig$clump,center=F,scale=T))
## compare contagion & clumpiness
plot(lcont,lclump, pch=19)
linreg.ER(lcont,lclump)
tFb <- scale(datwk.orig$feb_tmn_Mean,center=T,scale=T)
tJl <- scale(datwk.orig$jul_tmn_Mean,center=T,scale=T)
pcp <- scale(datwk.orig$an_sum_pre_Mean,center=T,scale=T)
threat <- datwk.orig$Threat
UKthreat <- datwk.orig$Ukthreat
drct <- as.factor(ifelse(prc > 1, 1, 0)) # direction of change (contraction = 0; expansion = 1)
natDisp <- datwk.orig$natDisp
lnatDisp <- log10(natDisp)
# hab <- ifelse((datwk.orig$Habitat == "forest" | datwk.orig$Habitat == "shrub" | datwk.orig$Habitat == "woodland"), "forest", datwk.orig$Habitat)
# hab <- ifelse((hab == "artificial" | hab == "grassland"), "farm", hab)
hab <- datwk.orig$Habitat
datwk <- data.frame(datwk.orig,t1sq,prc,prc.tr,diff,diff.tr,lMass,lClutch,lLong,lMAB,lcont,lclump,tFb,tJl,pcp,frc,frc.tr,frdiff,Clutch,MAB,Long,Mass,lprc,lprc.tr,threat,UKthreat,drct,natDisp,lnatDisp,hab)
dat.ext <- subset(datwk, prc < 0)
dat.ext <- dat.ext[-5, ] ## remove outlier
dat.exp <- subset(datwk, prc >= 0)
datwk.nw <- subset(datwk, (Order == "Passeriformes") | (Order == "Accipitriformes") | (Order == "Columbiformes") | (Order == "Galliformes") | (Order == "Strigiformes"))
datwk.nw$Order <- factor(datwk.nw$Order)
dat.ext.nw <- subset(dat.ext, (Order == "Passeriformes") | (Order == "Accipitriformes") | (Order == "Columbiformes") | (Order == "Galliformes") | (Order == "Strigiformes"))
dat.ext.nw$Order <- factor(dat.ext.nw$Order)
#write.csv(dat.ext.nw,file="truncators.csv")
dat.exp.nw <- subset(dat.exp, (Order == "Passeriformes") | (Order == "Accipitriformes") | (Order == "Columbiformes") | (Order == "Galliformes") | (Order == "Strigiformes"))
dat.exp.nw$Order <- factor(dat.exp.nw$Order)
#write.csv(dat.exp.nw,file="expanders.csv")
datwk.nw <- subset(datwk, (Order == "Passeriformes") | (Order == "Accipitriformes") | (Order == "Columbiformes") | (Order == "Galliformes") | (Order == "Strigiformes"))
datwk.nw$Order <- factor(datwk.nw$Order)
plot(datwk.nw$frc,datwk.nw$prc,pch=19,xlim=c(-100,100),ylim=c(-100,100))
abline(h=0,lty=2)
abline(v=0,lty=2)
plot(datwk.nw$frc,datwk.nw$prc,pch=19)
abline(h=0,lty=2)
abline(v=0,lty=2)
sub.anom1 <- which(datwk.nw$prc < 0 & datwk.nw$frc > 0)
datwk.nw$Common[sub.anom1]
datwk.nw.pinc.decr <- datwk.nw[sub.anom1,]
bias <- datwk.nw.pinc.decr$frc+abs(datwk.nw.pinc.decr$prc)
plot(datwk.nw.pinc.decr$t1,bias,pch=19,xlab="range t1",ylab="prop bias to agreement")
sub.anom1 <- which(datwk.nw$prc > 0 & datwk.nw$frc < 0)
datwk.nw$Common[sub.anom1]
datwk.nw.pinc.decr <- datwk.nw[sub.anom1,]
bias <- abs(datwk.nw.pinc.decr$frc)+datwk.nw.pinc.decr$prc
plot(datwk.nw.pinc.decr$t1,bias,pch=19,xlab="range t1",ylab="prop bias to agreement")
plot(datwk.nw$frdiff,datwk.nw$diff,pch=19)
abline(h=0,lty=2)
abline(v=0,lty=2)
sub.anom1 <- which(datwk.nw$diff < 0 & datwk.nw$frdiff > 0)
datwk.nw$Common[sub.anom1]
datwk.nw.pinc.decr <- datwk.nw[sub.anom1,]
bias <- abs(datwk.nw.pinc.decr$diff)+datwk.nw.pinc.decr$frdiff
plot(datwk.nw.pinc.decr$t1,bias,pch=19,xlab="range t1",ylab="bias to agreement")
plot(datwk.nw$PredPresRangeSize3,datwk.nw$t1,pch=19)
fit1 <- lm(datwk.nw$t1 ~ datwk.nw$PredPresRangeSize3)
abline(fit1)
summary(fit1)
## 'contingency' analysis for UK threat
## model set
mod1 <- "lprc~UKthreat+drct+UKthreat*drct"
mod2 <- "lprc~UKthreat+drct"
mod3 <- "lprc~UKthreat"
mod4 <- "lprc~drct"
mod5 <- "lprc~1"
## Make model vector
mod.vec <- c(mod1,mod2,mod3,mod4,mod5)
## Define n.mod
n.mod <- length(mod.vec)
# Model fitting and logLik output loop
Modnum <- length(mod.vec)
LL.vec <- SaveCount <- AICc.vec <- BIC.vec <- pc.dev.vec <- k.vec <- rep(0,Modnum)
mod.list <- list()
mod.num <- seq(1,Modnum,1)
for(i in 1:Modnum) {
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=datwk.nw, na.action=na.omit)
assign(paste("fit",i,sep=""), fit)
mod.list[[i]] <- fit
print(i)
}
sumtable <- aicW(mod.list, finite = TRUE, null.model = NULL, order = F)
row.names(sumtable) <- mod.vec
summary.table <- sumtable[order(sumtable[,4],decreasing=F),1:9]
summary.table
## saturated residual diagnostic
i <- 1
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=datwk.nw, na.action=na.omit)
plot(fit)
## Natal dispersal check
plot((dat.ext.nw$lnatDisp),dat.ext.nw$lprc,pch=19,main="contractors",xlab="mean natal dispersal (km)",ylab="delta R")
fit.ext <- lm(dat.ext.nw$lprc ~ dat.ext.nw$lnatDisp)
abline(fit.ext,lty=2)
linreg.ER(dat.ext.nw$lnatDisp,dat.ext.nw$lprc)
plot(fit.ext)
hist(log10(dat.ext.nw$natDisp))
plot((dat.exp.nw$lnatDisp),dat.exp.nw$lprc,pch=19,main="expanders",xlab="mean natal dispersal (km)",ylab="delta R")
fit.exp <- lm(dat.exp.nw$lprc ~ dat.exp.nw$lnatDisp)
abline(fit.exp,lty=2)
linreg.ER(dat.exp.nw$lnatDisp,dat.exp.nw$lprc)
plot(fit.ext)
hist(log10(dat.exp.nw$natDisp))
## correlation matrix for GLM input data
attach(datwk)
clump <- datwk.orig$clump
dat.cor.traits <- data.frame(lMass,lClutch,lLong,lnatDisp,tFb,tJl,pcp,lcont,lclump,t1)
dat.cor.ext <- data.frame(t1,lcont,lclump,tFb,tJl,pcp)
detach(datwk)
cor.mat.traits <- cor(dat.cor.traits,y=NULL,use="complete.obs",method="spearman")
lvar <- dim(dat.cor.traits)[2]
for (c in 1:lvar) {
cor.mat.traits[c,c:lvar] <- NA}
cor.mat.traits[,-lvar]
cor.mat.ext <- cor(dat.cor.ext,y=NULL,use="complete.obs",method="spearman")
for (c in 1:6) {
cor.mat.ext[c,c:6] <- NA}
cor.mat.ext[,-6]
hist(diff,br=30,main="",xlab="delta cells (t2-t1)",ylab="frequency",col="grey",border="black")
hist.out <- hist(diff,br=30,main="",xlab="delta cells (t2-t1)",ylab="frequency",col="grey",border="black")
hist.out.dat <- data.frame(hist.out$mids,hist.out$counts)
colnames(hist.out.dat) <- c("mids","freq")
write.csv(hist.out.dat,file="histout.csv")
####################################################################################
## GLM
## Traits
## all species
## response = transformed % range change (prc)
## model set
mod1 <- "lprc~lMass+lClutch+lLong"
mod2 <- "lprc~lMass+lClutch"
mod3 <- "lprc~lMass+lLong"
mod4 <- "lprc~lClutch+lLong"
mod5 <- "lprc~lMass"
mod6 <- "lprc~lClutch"
mod7 <- "lprc~lLong"
mod8 <- "lprc~1"
# mod1 <- "prc.tr~lMass+lClutch+lLong"
# mod2 <- "prc.tr~lMass+lClutch"
# mod3 <- "prc.tr~lMass+lLong"
# mod4 <- "prc.tr~lClutch+lLong"
# mod5 <- "prc.tr~lMass"
# mod6 <- "prc.tr~lClutch"
# mod7 <- "prc.tr~lLong"
# mod8 <- "prc.tr~1"
#
#
# mod1 <- "lprc.tr~lMass+lClutch+lLong"
# mod2 <- "lprc.tr~lMass+lClutch"
# mod3 <- "lprc.tr~lMass+lLong"
# mod4 <- "lprc.tr~lClutch+lLong"
# mod5 <- "lprc.tr~lMass"
# mod6 <- "lprc.tr~lClutch"
# mod7 <- "lprc.tr~lLong"
# mod8 <- "lprc.tr~1"
## Make model vector
#mod.vec <- c(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8,mod9,mod10,mod11,mod12,mod13,mod14,mod15,mod16)
mod.vec <- c(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8)
mod.vec <- c(mod2,mod3,mod4,mod5,mod6,mod7,mod8)
## Define n.mod
n.mod <- length(mod.vec)
# Model fitting and logLik output loop
Modnum <- length(mod.vec)
LL.vec <- SaveCount <- AICc.vec <- BIC.vec <- pc.dev.vec <- k.vec <- rep(0,Modnum)
mod.list <- list()
mod.num <- seq(1,Modnum,1)
for(i in 1:Modnum) {
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=datwk.nw, na.action=na.omit)
assign(paste("fit",i,sep=""), fit)
mod.list[[i]] <- fit
print(i)
}
sumtable <- aicW(mod.list, finite = TRUE, null.model = NULL, order = F)
row.names(sumtable) <- mod.vec
summary.table <- sumtable[order(sumtable[,4],decreasing=F),1:9]
summary.table
## saturated residual diagnostic
i <- 1
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="log"), data=datwk.nw, na.action=na.omit)
plot(fit)
## Partial residual plot
## select saturated model
par(mfrow=c(2,2),pty="s")
topfit1 <- glm(as.formula(mod.vec[1]),family=gaussian(link="identity"), data=datwk.nw, na.action=na.omit)
plot1 <- termplot(topfit1,terms="lMass",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Mass",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot2 <- termplot(topfit1,terms="lClutch",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Clutch Size",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
#plot3 <- termplot(topfit1,terms="lMAB",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Age Primiparity",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot4 <- termplot(topfit1,terms="lLong",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Longevity",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
par(mfrow=c(1,1))
n.sample <- topfit1$df.null+1
n.sample
###############################################
## response = t2 (t1 as control variable)
## model set
mod1 <- "t2~t1+lMass+lClutch+lLong"
mod2 <- "t2~t1+lMass+lClutch"
mod3 <- "t2~t1+lMass+lLong"
mod4 <- "t2~t1+lClutch+lLong"
mod5 <- "t2~t1+lMass"
mod6 <- "t2~t1+lClutch"
mod7 <- "t2~t1+lLong"
mod8 <- "t2~t1"
mod9 <- "t2~1"
## Make model vector
mod.vec <- c(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8,mod9)
## Define n.mod
n.mod <- length(mod.vec)
# Model fitting and logLik output loop
Modnum <- length(mod.vec)
LL.vec <- SaveCount <- AICc.vec <- BIC.vec <- pc.dev.vec <- k.vec <- rep(0,Modnum)
mod.list <- list()
mod.num <- seq(1,Modnum,1)
for(i in 1:Modnum) {
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="log"), data=datwk.nw, na.action=na.omit)
assign(paste("fit",i,sep=""), fit)
mod.list[[i]] <- fit
print(i)
}
sumtable <- aicW(mod.list, finite = TRUE, null.model = NULL, order = F)
row.names(sumtable) <- mod.vec
summary.table <- sumtable[order(sumtable[,4],decreasing=F),1:9]
summary.table
## saturated residual diagnostic
i <- 1
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="log"), data=datwk.nw, na.action=na.omit)
plot(fit)
## Partial residual plot
## select saturated model
par(mfrow=c(2,2),pty="s")
topfit1 <- glm(as.formula(mod.vec[1]),family=gaussian(link="sqrt"), data=datwk.nw, na.action=na.omit)
plot1 <- termplot(topfit1,terms="t1",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="R1",ylab="R2 partial resid",cex.axis=1.5,cex.lab=1.5)
plot2 <- termplot(topfit1,terms="lMass",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Mass",ylab="R2 partial resid",cex.axis=1.5,cex.lab=1.5)
plot3 <- termplot(topfit1,terms="lClutch",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Clutch Size",ylab="R2 partial resid",cex.axis=1.5,cex.lab=1.5)
plot4 <- termplot(topfit1,terms="lLong",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Longevity",ylab="R2 partial resid",cex.axis=1.5,cex.lab=1.5)
par(mfrow=c(1,1))
n.sample <- topfit1$df.null+1
n.sample
# export partial residulals
out1 <- data.frame(topfit1$model[,2],as.vector(residuals(topfit1,type="partial")[,1]),topfit1$model[,3],as.vector(residuals(topfit1,type="partial")[,3]),topfit1$model[,4],as.vector(residuals(topfit1,type="partial")[,3]))
colnames(out1) <- c("lMass","prR2M","lClutch","prR2C","lLong","prR2L")
write.table(out1,file="ext.lh.out.csv",sep=",",dec = ".", row.names = F,col.names = TRUE)
####################################################################################
## GLM
## Traits
## decliners only
## response = transformed % range change (prc)
## model set
# mod1 <- "abs(prc)~lMass+lClutch+lMAB+lLong"
# mod2 <- "abs(prc)~lMass+lClutch+lMAB"
# mod3 <- "abs(prc)~lMass+lClutch+lLong"
# mod4 <- "abs(prc)~lMass+lMAB+lLong"
# mod5 <- "abs(prc)~lClutch+lMAB+lLong"
# mod6 <- "abs(prc)~lMass+lClutch"
# mod7 <- "abs(prc)~lMass+lMAB"
# mod8 <- "abs(prc)~lMass+lLong"
# mod9 <- "abs(prc)~lClutch+lMAB"
# mod10 <- "abs(prc)~lClutch+lLong"
# mod11 <- "abs(prc)~lMAB+lLong"
# mod12 <- "abs(prc)~lMass"
# mod13 <- "abs(prc)~lClutch"
# mod14 <- "abs(prc)~lMAB"
# mod15 <- "abs(prc)~lLong"
# mod16 <- "abs(prc)~1"
# mod1 <- "abs(prc)~Mass+Clutch+MAB+Long"
# mod2 <- "abs(prc)~Mass+Clutch+MAB"
# mod3 <- "abs(prc)~Mass+Clutch+Long"
# mod4 <- "abs(prc)~Mass+MAB+Long"
# mod5 <- "abs(prc)~Clutch+MAB+Long"
# mod6 <- "abs(prc)~Mass+Clutch"
# mod7 <- "abs(prc)~Mass+MAB"
# mod8 <- "abs(prc)~Mass+Long"
# mod9 <- "abs(prc)~Clutch+MAB"
# mod10 <- "abs(prc)~Clutch+Long"
# mod11 <- "abs(prc)~MAB+Long"
# mod12 <- "abs(prc)~Mass"
# mod13 <- "abs(prc)~Clutch"
# mod14 <- "abs(prc)~MAB"
# mod15 <- "abs(prc)~Long"
# mod16 <- "abs(prc)~1"
# mod1 <- "abs(prc)~lMass+lClutch+lLong"
# mod2 <- "abs(prc)~lMass+lClutch"
# mod3 <- "abs(prc)~lMass+lLong"
# mod4 <- "abs(prc)~lClutch+lLong"
# mod5 <- "abs(prc)~lMass"
# mod6 <- "abs(prc)~lClutch"
# mod7 <- "abs(prc)~lLong"
# mod8 <- "abs(prc)~1"
mod1 <- "lprc~lMass+lClutch+lLong"
mod2 <- "lprc~lMass+lClutch"
mod3 <- "lprc~lMass+lLong"
mod4 <- "lprc~lClutch+lLong"
mod5 <- "lprc~lMass"
mod6 <- "lprc~lClutch"
mod7 <- "lprc~lLong"
mod8 <- "lprc~1"
## Make model vector
#mod.vec <- c(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8,mod9,mod10,mod11,mod12,mod13,mod14,mod15,mod16)
mod.vec <- c(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8)
mod.vec <- c(mod2,mod3,mod4,mod5,mod6,mod7,mod8)
## remove outlier
#dat.ext.nw2 <- dat.ext.nw[-c(32,61),]
dat.ext.nw2 <- dat.ext.nw[-c(32),]
## Define n.mod
n.mod <- length(mod.vec)
# Model fitting and logLik output loop
Modnum <- length(mod.vec)
LL.vec <- SaveCount <- AICc.vec <- BIC.vec <- pc.dev.vec <- k.vec <- rep(0,Modnum)
mod.list <- list()
mod.num <- seq(1,Modnum,1)
for(i in 1:Modnum) {
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=dat.ext.nw2, na.action=na.omit)
assign(paste("fit",i,sep=""), fit)
mod.list[[i]] <- fit
print(i)
}
sumtable <- aicW(mod.list, finite = TRUE, null.model = NULL, order = F)
row.names(sumtable) <- mod.vec
summary.table <- sumtable[order(sumtable[,4],decreasing=F),1:9]
summary.table
## saturated residual diagnostic
i <- 1
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=dat.ext.nw2, na.action=na.omit)
plot(fit)
## Partial residual plot
## select saturated model
par(mfrow=c(2,2),pty="s")
topfit1 <- glm(as.formula(mod.vec[1]),family=gaussian(link="identity"), data=dat.ext.nw2, na.action=na.omit)
plot1 <- termplot(topfit1,terms="lMass",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Mass",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot2 <- termplot(topfit1,terms="lClutch",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Clutch Size",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
#plot3 <- termplot(topfit1,terms="lMAB",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Age Primiparity",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot4 <- termplot(topfit1,terms="lLong",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Longevity",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
par(mfrow=c(1,1))
n.sample <- topfit1$df.null+1
n.sample
# export partial residulals
out1 <- data.frame(topfit1$model[,2],as.vector(residuals(topfit1,type="partial")[,1]),topfit1$model[,3],as.vector(residuals(topfit1,type="partial")[,3]),topfit1$model[,4],as.vector(residuals(topfit1,type="partial")[,3]))
colnames(out1) <- c("lMass","prlprcM","lClutch","prlprcC","lLong","prlprcL")
write.table(out1,file="ext.lh.out.csv",sep=",",dec = ".", row.names = F,col.names = TRUE)
###############################################
## response = t2 (t1 as control variable)
## model set
mod1 <- "t2~t1+lMass+lClutch+lLong"
mod2 <- "t2~t1+lMass+lClutch"
mod3 <- "t2~t1+lMass+lLong"
mod4 <- "t2~t1+lClutch+lLong"
mod5 <- "t2~t1+lMass"
mod6 <- "t2~t1+lClutch"
mod7 <- "t2~t1+lLong"
mod8 <- "t2~t1"
mod9 <- "t2~1"
## Make model vector
mod.vec <- c(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8,mod9)
## Define n.mod
n.mod <- length(mod.vec)
# Model fitting and logLik output loop
Modnum <- length(mod.vec)
LL.vec <- SaveCount <- AICc.vec <- BIC.vec <- pc.dev.vec <- k.vec <- rep(0,Modnum)
mod.list <- list()
mod.num <- seq(1,Modnum,1)
for(i in 1:Modnum) {
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="sqrt"), data=dat.ext.nw, na.action=na.omit)
assign(paste("fit",i,sep=""), fit)
mod.list[[i]] <- fit
print(i)
}
sumtable <- aicW(mod.list, finite = TRUE, null.model = NULL, order = F)
row.names(sumtable) <- mod.vec
summary.table <- sumtable[order(sumtable[,4],decreasing=F),1:9]
summary.table
## saturated residual diagnostic
i <- 1
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="sqrt"), data=dat.ext.nw, na.action=na.omit)
plot(fit)
## Partial residual plot
## select saturated model
par(mfrow=c(2,2),pty="s")
topfit1 <- glm(as.formula(mod.vec[1]),family=gaussian(link="sqrt"), data=dat.ext.nw, na.action=na.omit)
plot1 <- termplot(topfit1,terms="t1",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="R1",ylab="R2 partial resid",cex.axis=1.5,cex.lab=1.5)
plot2 <- termplot(topfit1,terms="lMass",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Mass",ylab="R2 partial resid",cex.axis=1.5,cex.lab=1.5)
plot3 <- termplot(topfit1,terms="lClutch",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Clutch Size",ylab="R2 partial resid",cex.axis=1.5,cex.lab=1.5)
plot4 <- termplot(topfit1,terms="lLong",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Longevity",ylab="R2 partial resid",cex.axis=1.5,cex.lab=1.5)
par(mfrow=c(1,1))
n.sample <- topfit1$df.null+1
n.sample
# export partial residulals
out1 <- data.frame(topfit1$model[,2],as.vector(residuals(topfit1,type="partial")[,1]),topfit1$model[,3],as.vector(residuals(topfit1,type="partial")[,3]),topfit1$model[,4],as.vector(residuals(topfit1,type="partial")[,3]))
colnames(out1) <- c("lMass","prR2M","lClutch","prR2C","lLong","prR2L")
write.table(out1,file="ext.lh.out.csv",sep=",",dec = ".", row.names = F,col.names = TRUE)
###############################################
## contractors only, including natal dispersal
## model set
mod1 <- "lprc~lMass+lClutch+lLong+lnatDisp"
mod2 <- "lprc~lMass+lClutch"
mod3 <- "lprc~lMass+lLong"
mod4 <- "lprc~lClutch+lLong"
mod5 <- "lprc~lMass"
mod6 <- "lprc~lClutch"
mod7 <- "lprc~lLong"
mod8 <- "lprc~lMass+lClutch+lLong"
mod9 <- "lprc~lMass+lClutch+lnatDisp"
mod10 <- "lprc~lMass+lLong+lnatDisp"
mod11 <- "lprc~lClutch+lLong+lnatDisp"
mod12 <- "lprc~lMass+lnatDisp"
mod13 <- "lprc~lClutch+lnatDisp"
mod14 <- "lprc~lLong+lnatDisp"
mod15 <- "lprc~lnatDisp"
mod16 <- "lprc~1"
## Make model vector
mod.vec <- c(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8,mod9,mod10,mod11,mod12,mod13,mod14,mod15,mod16)
## remove outlier
#dat.ext.nw2 <- dat.ext.nw[-c(32,61),]
dat.ext.nw2 <- dat.ext.nw[-c(32),]
## Define n.mod
n.mod <- length(mod.vec)
# Model fitting and logLik output loop
Modnum <- length(mod.vec)
LL.vec <- SaveCount <- AICc.vec <- BIC.vec <- pc.dev.vec <- k.vec <- rep(0,Modnum)
mod.list <- list()
mod.num <- seq(1,Modnum,1)
for(i in 1:Modnum) {
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=dat.ext.nw2, na.action=na.omit)
assign(paste("fit",i,sep=""), fit)
mod.list[[i]] <- fit
print(i)
}
sumtable <- aicW(mod.list, finite = TRUE, null.model = NULL, order = F)
row.names(sumtable) <- mod.vec
summary.table <- sumtable[order(sumtable[,4],decreasing=F),1:9]
summary.table
## saturated residual diagnostic
i <- 1
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=dat.ext.nw2, na.action=na.omit)
plot(fit)
## Partial residual plot
## select saturated model
par(mfrow=c(1,4),pty="s")
topfit1 <- glm(as.formula(mod.vec[1]),family=gaussian(link="identity"), data=dat.ext.nw2, na.action=na.omit)
plot1 <- termplot(topfit1,terms="lMass",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Mass",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot2 <- termplot(topfit1,terms="lClutch",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Clutch Size",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
#plot3 <- termplot(topfit1,terms="lMAB",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Age Primiparity",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot4 <- termplot(topfit1,terms="lLong",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Longevity",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot5 <- termplot(topfit1,terms="lnatDisp",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Natal Dispersal",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
par(mfrow=c(1,1))
n.sample <- topfit1$df.null+1
n.sample
# export partial residulals
out1 <- data.frame(topfit1$model[,2],as.vector(residuals(topfit1,type="partial")[,1]),topfit1$model[,3],as.vector(residuals(topfit1,type="partial")[,3]),topfit1$model[,4],as.vector(residuals(topfit1,type="partial")[,3]))
colnames(out1) <- c("lMass","prlprcM","lClutch","prlprcC","lLong","prlprcL")
write.table(out1,file="ext.lh.out.csv",sep=",",dec = ".", row.names = F,col.names = TRUE)
####################################################################################
## GLM
## Traits
## expanders only
## response = transformed % range change (prc)
## model set
# mod1 <- "abs(prc)~lMass+lClutch+lMAB+lLong"
# mod2 <- "abs(prc)~lMass+lClutch+lMAB"
# mod3 <- "abs(prc)~lMass+lClutch+lLong"
# mod4 <- "abs(prc)~lMass+lMAB+lLong"
# mod5 <- "abs(prc)~lClutch+lMAB+lLong"
# mod6 <- "abs(prc)~lMass+lClutch"
# mod7 <- "abs(prc)~lMass+lMAB"
# mod8 <- "abs(prc)~lMass+lLong"
# mod9 <- "abs(prc)~lClutch+lMAB"
# mod10 <- "abs(prc)~lClutch+lLong"
# mod11 <- "abs(prc)~lMAB+lLong"
# mod12 <- "abs(prc)~lMass"
# mod13 <- "abs(prc)~lClutch"
# mod14 <- "abs(prc)~lMAB"
# mod15 <- "abs(prc)~lLong"
# mod16 <- "abs(prc)~1"
# mod1 <- "abs(prc)~Mass+Clutch+MAB+Long"
# mod2 <- "abs(prc)~Mass+Clutch+MAB"
# mod3 <- "abs(prc)~Mass+Clutch+Long"
# mod4 <- "abs(prc)~Mass+MAB+Long"
# mod5 <- "abs(prc)~Clutch+MAB+Long"
# mod6 <- "abs(prc)~Mass+Clutch"
# mod7 <- "abs(prc)~Mass+MAB"
# mod8 <- "abs(prc)~Mass+Long"
# mod9 <- "abs(prc)~Clutch+MAB"
# mod10 <- "abs(prc)~Clutch+Long"
# mod11 <- "abs(prc)~MAB+Long"
# mod12 <- "abs(prc)~Mass"
# mod13 <- "abs(prc)~Clutch"
# mod14 <- "abs(prc)~MAB"
# mod15 <- "abs(prc)~Long"
# mod16 <- "abs(prc)~1"
# mod1 <- "abs(prc)~lMass+lClutch+lLong"
# mod2 <- "abs(prc)~lMass+lClutch"
# mod3 <- "abs(prc)~lMass+lLong"
# mod4 <- "abs(prc)~lClutch+lLong"
# mod5 <- "abs(prc)~lMass"
# mod6 <- "abs(prc)~lClutch"
# mod7 <- "abs(prc)~lLong"
# mod8 <- "abs(prc)~1"
mod1 <- "lprc~lMass+lClutch+lLong"
mod2 <- "lprc~lMass+lClutch"
mod3 <- "lprc~lMass+lLong"
mod4 <- "lprc~lClutch+lLong"
mod5 <- "lprc~lMass"
mod6 <- "lprc~lClutch"
mod7 <- "lprc~lLong"
mod8 <- "lprc~1"
## Make model vector
#mod.vec <- c(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8,mod9,mod10,mod11,mod12,mod13,mod14,mod15,mod16)
mod.vec <- c(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8)
mod.vec <- c(mod2,mod3,mod4,mod5,mod6,mod7,mod8)
## remove outlier
dat.exp.nw2 <- dat.exp.nw[-5,]
## Define n.mod
n.mod <- length(mod.vec)
# Model fitting and logLik output loop
Modnum <- length(mod.vec)
LL.vec <- SaveCount <- AICc.vec <- BIC.vec <- pc.dev.vec <- k.vec <- rep(0,Modnum)
mod.list <- list()
mod.num <- seq(1,Modnum,1)
for(i in 1:Modnum) {
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=dat.exp.nw, na.action=na.omit)
assign(paste("fit",i,sep=""), fit)
mod.list[[i]] <- fit
print(i)
}
sumtable <- aicW(mod.list, finite = TRUE, null.model = NULL, order = F)
row.names(sumtable) <- mod.vec
summary.table <- sumtable[order(sumtable[,4],decreasing=F),1:9]
summary.table
## saturated residual diagnostic
i <- 1
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=dat.exp.nw, na.action=na.omit)
plot(fit)
## Partial residual plot
## select saturated model
par(mfrow=c(2,2),pty="s")
topfit1 <- glm(as.formula(mod.vec[1]),family=gaussian(link="identity"), data=dat.exp.nw, na.action=na.omit)
plot1 <- termplot(topfit1,terms="lMass",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Mass",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot2 <- termplot(topfit1,terms="lClutch",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Clutch Size",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
#plot3 <- termplot(topfit1,terms="lMAB",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Age Primiparity",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot4 <- termplot(topfit1,terms="lLong",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Longevity",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
par(mfrow=c(1,1))
n.sample <- topfit1$df.null+1
n.sample
# export partial residulals
out2 <- data.frame(topfit1$model[,2],as.vector(residuals(topfit1,type="partial")[,1]),topfit1$model[,3],as.vector(residuals(topfit1,type="partial")[,3]),topfit1$model[,4],as.vector(residuals(topfit1,type="partial")[,3]))
colnames(out2) <- c("lMass","prlprcM","lClutch","prlprcC","lLong","prlprcL")
write.table(out2,file="exp.lh.out.csv",sep=",",dec = ".", row.names = F,col.names = TRUE)
##########################################
## response = t2 (t1 as control variable)
## model set
mod1 <- "t2~t1+lMass+lClutch+lLong"
mod2 <- "t2~t1+lMass+lClutch"
mod3 <- "t2~t1+lMass+lLong"
mod4 <- "t2~t1+lClutch+lLong"
mod5 <- "t2~t1+lMass"
mod6 <- "t2~t1+lClutch"
mod7 <- "t2~t1+lLong"
mod8 <- "t2~t1"
mod9 <- "t2~1"
## Make model vector
mod.vec <- c(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8,mod9)
## Define n.mod
n.mod <- length(mod.vec)
# Model fitting and logLik output loop
Modnum <- length(mod.vec)
LL.vec <- SaveCount <- AICc.vec <- BIC.vec <- pc.dev.vec <- k.vec <- rep(0,Modnum)
mod.list <- list()
mod.num <- seq(1,Modnum,1)
for(i in 1:Modnum) {
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="sqrt"), data=dat.exp.nw, na.action=na.omit)
assign(paste("fit",i,sep=""), fit)
mod.list[[i]] <- fit
print(i)
}
sumtable <- aicW(mod.list, finite = TRUE, null.model = NULL, order = F)
row.names(sumtable) <- mod.vec
summary.table <- sumtable[order(sumtable[,4],decreasing=F),1:9]
summary.table
## saturated residual diagnostic
i <- 1
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="sqrt"), data=dat.exp.nw, na.action=na.omit)
plot(fit)
## Partial residual plot
## select saturated model
par(mfrow=c(2,2),pty="s")
topfit1 <- glm(as.formula(mod.vec[1]),family=gaussian(link="sqrt"), data=dat.exp.nw, na.action=na.omit)
plot1 <- termplot(topfit1,terms="t1",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="R1",ylab="R2 partial resid",cex.axis=1.5,cex.lab=1.5)
plot2 <- termplot(topfit1,terms="lMass",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Mass",ylab="R2 partial resid",cex.axis=1.5,cex.lab=1.5)
plot3 <- termplot(topfit1,terms="lClutch",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Clutch Size",ylab="R2 partial resid",cex.axis=1.5,cex.lab=1.5)
plot4 <- termplot(topfit1,terms="lLong",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Longevity",ylab="R2 partial resid",cex.axis=1.5,cex.lab=1.5)
par(mfrow=c(1,1))
n.sample <- topfit1$df.null+1
n.sample
# export partial residuals
out1 <- data.frame(topfit1$model[,2],as.vector(residuals(topfit1,type="partial")[,1]),topfit1$model[,3],as.vector(residuals(topfit1,type="partial")[,3]),topfit1$model[,4],as.vector(residuals(topfit1,type="partial")[,3]))
colnames(out1) <- c("lMass","prR2M","lClutch","prR2C","lLong","prR2L")
write.table(out1,file="ext.lh.out.csv",sep=",",dec = ".", row.names = F,col.names = TRUE)
###############################################
## expanders only, including natal dispersal
## model set
mod1 <- "lprc~lMass+lClutch+lLong+lnatDisp"
mod2 <- "lprc~lMass+lClutch"
mod3 <- "lprc~lMass+lLong"
mod4 <- "lprc~lClutch+lLong"
mod5 <- "lprc~lMass"
mod6 <- "lprc~lClutch"
mod7 <- "lprc~lLong"
mod8 <- "lprc~lMass+lClutch+lLong"
mod9 <- "lprc~lMass+lClutch+lnatDisp"
mod10 <- "lprc~lMass+lLong+lnatDisp"
mod11 <- "lprc~lClutch+lLong+lnatDisp"
mod12 <- "lprc~lMass+lnatDisp"
mod13 <- "lprc~lClutch+lnatDisp"
mod14 <- "lprc~lLong+lnatDisp"
mod15 <- "lprc~lnatDisp"
mod16 <- "lprc~1"
## Make model vector
mod.vec <- c(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8,mod9,mod10,mod11,mod12,mod13,mod14,mod15,mod16)
## Define n.mod
n.mod <- length(mod.vec)
# Model fitting and logLik output loop
Modnum <- length(mod.vec)
LL.vec <- SaveCount <- AICc.vec <- BIC.vec <- pc.dev.vec <- k.vec <- rep(0,Modnum)
mod.list <- list()
mod.num <- seq(1,Modnum,1)
for(i in 1:Modnum) {
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=dat.exp.nw, na.action=na.omit)
assign(paste("fit",i,sep=""), fit)
mod.list[[i]] <- fit
print(i)
}
sumtable <- aicW(mod.list, finite = TRUE, null.model = NULL, order = F)
row.names(sumtable) <- mod.vec
summary.table <- sumtable[order(sumtable[,4],decreasing=F),1:9]
summary.table
## saturated residual diagnostic
i <- 1
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=dat.exp.nw, na.action=na.omit)
plot(fit)
## Partial residual plot
## select saturated model
par(mfrow=c(1,4),pty="s")
topfit1 <- glm(as.formula(mod.vec[1]),family=gaussian(link="identity"), data=dat.exp.nw, na.action=na.omit)
plot1 <- termplot(topfit1,terms="lMass",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Mass",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot2 <- termplot(topfit1,terms="lClutch",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Clutch Size",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
#plot3 <- termplot(topfit1,terms="lMAB",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Age Primiparity",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot4 <- termplot(topfit1,terms="lLong",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Longevity",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot5 <- termplot(topfit1,terms="lnatDisp",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Natal Dispersal",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
par(mfrow=c(1,1))
n.sample <- topfit1$df.null+1
n.sample
## climate
## all species
## response = transformed % range change (prc)
## model set
mod1 <- "lprc~tFb+tJl+pcp"
mod2 <- "lprc~tFb+tJl"
mod3 <- "lprc~tJl+pcp"
mod4 <- "lprc~tFb+pcp"
mod5 <- "lprc~tFb"
mod6 <- "lprc~tJl"
mod7 <- "lprc~pcp"
mod8 <- "lprc~1"
## Make model vector
mod.vec <- c(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8)
mod.vec <- c(mod2,mod3,mod4,mod5,mod6,mod7,mod8)
## Define n.mod
n.mod <- length(mod.vec)
# Model fitting and logLik output loop
Modnum <- length(mod.vec)
LL.vec <- SaveCount <- AICc.vec <- BIC.vec <- pc.dev.vec <- k.vec <- rep(0,Modnum)
mod.list <- list()
mod.num <- seq(1,Modnum,1)
for(i in 1:Modnum) {
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=datwk.nw, na.action=na.omit)
assign(paste("fit",i,sep=""), fit)
mod.list[[i]] <- fit
print(i)
}
sumtable <- aicW(mod.list, finite = TRUE, null.model = NULL, order = F)
row.names(sumtable) <- mod.vec
summary.table <- sumtable[order(sumtable[,4],decreasing=F),1:9]
summary.table
## saturated residual diagnostic
i <- 1
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=datwk.nw, na.action=na.omit)
plot(fit)
## Partial residual plot
## select saturated model
par(mfrow=c(2,2),pty="s")
topfit1 <- glm(as.formula(mod.vec[1]),family=gaussian(link="identity"), data=datwk.nw, na.action=na.omit)
plot1 <- termplot(topfit1,terms="tFb",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Feb Temp",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot2 <- termplot(topfit1,terms="tJl",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Jul Temp",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot3 <- termplot(topfit1,terms="pcp",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Ann pcp",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
par(mfrow=c(1,1))
n.sample <- topfit1$df.null+1
n.sample
## decliners only
## response = transformed % range change (prc)
## model set
# mod1 <- "abs(prc)~tFb+tJl+pcp"
# mod2 <- "abs(prc)~tFb+tJl"
# mod3 <- "abs(prc)~tJl+pcp"
# mod4 <- "abs(prc)~tFb+pcp"
# mod5 <- "abs(prc)~tFb"
# mod6 <- "abs(prc)~tJl"
# mod7 <- "abs(prc)~pcp"
# mod8 <- "abs(prc)~1"
mod1 <- "lprc~tFb+tJl+pcp"
mod2 <- "lprc~tFb+tJl"
mod3 <- "lprc~tJl+pcp"
mod4 <- "lprc~tFb+pcp"
mod5 <- "lprc~tFb"
mod6 <- "lprc~tJl"
mod7 <- "lprc~pcp"
mod8 <- "lprc~1"
## Make model vector
mod.vec <- c(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8)
mod.vec <- c(mod2,mod3,mod4,mod5,mod6,mod7,mod8)
## Define n.mod
n.mod <- length(mod.vec)
# Model fitting and logLik output loop
Modnum <- length(mod.vec)
LL.vec <- SaveCount <- AICc.vec <- BIC.vec <- pc.dev.vec <- k.vec <- rep(0,Modnum)
mod.list <- list()
mod.num <- seq(1,Modnum,1)
for(i in 1:Modnum) {
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=dat.ext.nw, na.action=na.omit)
assign(paste("fit",i,sep=""), fit)
mod.list[[i]] <- fit
print(i)
}
sumtable <- aicW(mod.list, finite = TRUE, null.model = NULL, order = F)
row.names(sumtable) <- mod.vec
summary.table <- sumtable[order(sumtable[,4],decreasing=F),1:9]
summary.table
## saturated residual diagnostic
i <- 1
fit <- glm(as.formula(mod.vec[i]),family=gaussian(link="identity"), data=dat.ext.nw, na.action=na.omit)
plot(fit)
## Partial residual plot
## select saturated model
par(mfrow=c(2,2),pty="s")
topfit1 <- glm(as.formula(mod.vec[1]),family=gaussian(link="identity"), data=dat.ext.nw, na.action=na.omit)
plot1 <- termplot(topfit1,terms="tFb",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Feb Temp",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot2 <- termplot(topfit1,terms="tJl",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Jul Temp",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)
plot3 <- termplot(topfit1,terms="pcp",partial.resid=T,rug=T,se=T,col.term="black",col.res="black",col.se="black",pch=19,xlab="Ann pcp",ylab="prc partial resid",cex.axis=1.5,cex.lab=1.5)