CTFSworkshop(data=TRUE)
args(modelfit.Bayes)
fit=modelfit.Bayes(start=c(-2,2,.5),xcol='logdbh',ycol='logagb',steps=100,showstep=10,burnin=50)
fit=modelfit.Bayes(start=c(-2,2,.5),xcol='logdbh',ycol='logagb',data=treemass,steps=100,showstep=10,burnin=50)
fit=modelfit.Bayes(start=c(-2,2,.5),xcol='logdbh',ycol='logagb',data=treemass,model=linear.model,steps=100,showstep=10,burnin=50)
str(treemass.split)
names(treemass.split)
options(width=70)
is.list(treemass.split)
head(treemass)
head(treemass.split[[1]])
names(treemass.split)
options(width=60)
names(treemass.split)
table(treemass$Locality)
treemass.split$BraRond
subset(treemass,Locality=='BraRond')
treemass.split$BraRond
CTFSworkshop()
args(growthModelHier)
args(metrop1step)
args(CI)
growthModelHier(start=c(0,0,1),data=treemass.split,xcol='logdbh',gcol='logagb')
CTFSworkshop()
growthModelHier(start=c(0,0,1),data=treemass.split,xcol='logdbh',gcol='logagb')
growthModelHier(start=c(0,0,1),data=treemass.split,xcol='logdbh',gcol='logagb',steps=1000,burnin=500,showstep=100)
fit=growthModelHier(start=c(0,0,1),data=treemass.split,xcol='logdbh',gcol='logagb',steps=1000,burnin=500,showstep=100)
debug(growthModelHier)
fit=growthModelHier(start=c(0,0,1),data=treemass.split,xcol='logdbh',gcol='logagb',steps=1000,burnin=500,showstep=100)
Q
debug(growthModelHier)
fit=growthModelHier(start=c(0,0,1),data=treemass.split,xcol='logdbh',gcol='logagb',steps=1000,burnin=500,showstep=100)
Q
source('~/meetings_workshops/Rmodeling/cenpat/growthModelHier.r')
source('~/meetings_workshops/Rmodeling/cenpat/growthModelHierBayes.r')
source('~/meetings_workshops/Rmodeling/cenpat/growthModel.HierBayes.r')
source('~/meetings_workshops/Rmodeling/Cenpat/growthModel.HierBayes.r')
CTFSworkshop()
fit=growthModelHier(start=c(0,0,1),data=treemass.split,xcol='logdbh',gcol='logagb',steps=1000,burnin=500,showstep=100)
n
dim(param)
head(param[1,,])
n
x
plot(x,y)
allspp[j]
n
metropInter
oneparam
head(param[1,,])
oneparam
head(param[1,,])
head(param[1,,])
Q
fit=growthModelHier(start=c(0,0,1),data=treemass.split,xcol='logdbh',gcol='logagb',steps=1000,burnin=500,showstep=100,debug=TRUE)
n
c
m
n
head(param[1,,])
head(param[2,,])
head(param[3,,])
param[,i,1]
n
hyperMuInter
hyperSDInter
c
c
n
c
i
head(param[3,,])
head(param[2,,])
param[,i,1]
param[,i,2]
Q
CTFSworkshop()
fit=growthModelHier(start=c(0,0,1),data=treemass.split,xcol='logdbh',gcol='logagb',steps=1000,burnin=500,showstep=100)
Q
CTFSworkshop()
fit=growthModelHier(start=c(0,0,1),data=treemass.split,xcol='logdbh',gcol='logagb',steps=1000,burnin=500,showstep=100)
fit=growthModelHier(start=c(0,0,1),data=treemass.split,xcol='logdbh',gcol='logagb',steps=2000,burnin=1000,showstep=100)
names(fit)
dim(fit$full)
dim(fullhyper)
dim(fit$fullhyper)
fit$best
dim(fit$fullhyper)
plot(fit$fullhyper[,1])
plot(fit$fullhyper[,2])
plot(fit$fullhyper[,3])
plot(fit$fullhyper[,4])
plot(fit$full[1,,1])
plot(fit$full[2,,1])
plot(fit$fullhyper[,4])
plot(fit$full[2,,1])
fit=growthModelHier(start=c(0,0,1),data=treemass.split,xcol='logdbh',gcol='logagb',steps=4000,burnin=1000,showstep=3000)
fit$besthyper
fit$CIhyper
plot(fit$fullhyper[,4])
plot(fit$fullhyper[,1])
fit=growthModelHier(start=c(-2,2,1),data=treemass.split,xcol='logdbh',gcol='logagb',steps=4000,burnin=1000,showstep=3000)
plot(fit$fullhyper[,1])
plot(fit$fullhyper[,2])
plot(fit$full[2,,1])
plot(fit$full[2,,2])
plot(fit$full[2,,1])
fit$best
lm(logagb~logdbh,data=treemass.split$BraRond)$coef
abline(fit$best[1,1:2])
plot(logagb~logdbh,data=treemass)
plot(logagb~logdbh,data=treemass[[1]])
plot(logagb~logdbh,data=treemass.split[[1]])
for(i in 2:28) points(logagb~logdbh,data=treemass.split[[i]],col=i)
names(treemass.split)
head(fit$best)
str(fit$best)
abline(fit$best[1,1:2])
abline(as.matrix(fit$best[1,1:2]))
fit$best=as.matrix(fit$best)
cf=as.matrix(fit$best)
cf
abline(cf[3,1:2])
cf=as.matrix(fit$best[,1:2])
abline(cf[3,])
abline(cf[5,])
plot(logagb~logdbh,data=treemass)
plot(logagb~logdbh,data=treemass,cex=.2)
for(i in 1:28) abline(cf[i,])
for(i in 1:28) abline(cf[i,],col=i)
fit$besthyper
for(i in 1:28) { plot(logagb~logdbh,data=treemass[[i]]);          abline(cf[i,]) }
for(i in 1:28) { plot(logagb~logdbh,data=treemass[[i]]);          abline(cf[i,]) }
cf[1,]
for(i in 1:28) { plot(logagb~logdbh,data=treemass.split[[i]]);          abline(cf[i,]) }
for(i in 1:28) { plot(logagb~logdbh,data=treemass.split[[i]]);          abline(cf[i,]) }
for(i in 1:28) { plot(logagb~logdbh,data=treemass.split[[i]]);          abline(cf[i,]) }
par(ask=TRUE)
for(i in 1:28) { plot(logagb~logdbh,data=treemass.split[[i]]);          abline(cf[i,]) }
par(ask=FALSE)
par(mfcol=c(4,3))
for(i in 1:12) { plot(logagb~logdbh,data=treemass.split[[i]]);          abline(cf[i,]) }
for(i in 1:12) { plot(logagb~logdbh,data=treemass.split[[i]]);          abline(cf[i,]) }
par(mfcol=c(4,3))
for(i in 1:12) { plot(logagb~logdbh,data=treemass.split[[i]]);          abline(cf[i,]) }
graphics.off()
par(mfcol=c(4,3))
for(i in 1:12) { plot(logagb~logdbh,data=treemass.split[[i]]);          abline(cf[i,]) }
par(mfcol=c(7,4))
for(i in 1:28) { plot(logagb~logdbh,data=treemass.split[[i]]);          abline(cf[i,]) }
graphics.off()
par(mfcol=c(7,4))
for(i in 1:28) { plot(logagb~logdbh,data=treemass.split[[i]]);          abline(cf[i,]) }
fit$bes
fit$best
mod=lmer(logagb~1+logdbh+(1+logdbh|Locality),data=treemass)
head(coef(mod)$Local)
head(fit$best)
head(coef(mod)$Local,8)
head(fit$best,8)
plot(logagb~logdbh,data=treemass.split$BraRond)
graphics.off()
plot(logagb~logdbh,data=treemass.split$BraRond)
plot(logagb~logdbh,data=treemass,cex=.2)
points(logagb~logdbh,data=treemass.split$BraRond,col='red',pch=16)
abline(cf['BraRond',],col='red',lwd=2)
cf=as.matrix(fit$best[,1:2])
cf['BraRond',]
abline(lm(logagb~logdbh,data=treemass.split$BraRond,col='green',lwd=2))
abline(lm(logagb~logdbh,data=treemass.split$BraRond),col='green',lwd=2)
site='BraRond'
site
plot(logagb~logdbh,data=treemass,cex=.2)
abline(cf[site,],col='red',lwd=2)
points(logagb~logdbh,data=treemass.split[[site]],col='red',pch=16)
abline(lm(logagb~logdbh,data=treemass.split[[site]]),col='green',lwd=2)
site='Australia'
head(fit$best)
plot(logagb~logdbh,data=treemass,cex=.2)
points(logagb~logdbh,data=treemass.split[[site]],col='red',pch=16)
abline(lm(logagb~logdbh,data=treemass.split[[site]]),col='green',lwd=2)
abline(cf[site,],col='red',lwd=2)
fit$best
site='IndiaKarna'
plot(logagb~logdbh,data=treemass,cex=.2)
points(logagb~logdbh,data=treemass.split[[site]],col='red',pch=16)
abline(cf[site,],col='red',lwd=2)
abline(lm(logagb~logdbh,data=treemass.split[[site]]),col='green',lwd=2)
lm(logagb~logdbh,data=treemass.split[[site]])$coef
fit$besthyper
display(mod)
names(fit)
savehistory('~/meetings_workshops/Rmodeling/Cenpat/history13Oct.txt')