#Modelle fŸr Jahr

#Jahr stetig
m11<-lm(ZK[,37]~FyearE)
summary(m11)

#Jahr als Faktor
FyearE<-as.factor(yearE)
m4<-lm(ZK[,37]~FyearE)
summary(m4)
cm4<-confint(m4)
cm4
Vbetahat<-vcov(m4)

#Summe=0 Nebenbedingung
m4s<-lm(ZK[,37]~FyearE,options(contrasts = c("contr.sum", "contr.poly")))
summary(m4s)
cm4s<-confint(m4s)
cm4s
Vbetahats<-vcov(m4s)


#CIs fŸr geschŠtzten Koefficienten
K <- diag(length(coef(m4)))[-1,]
rownames(K) <- names(coef(m4))[-1]
gm4ci<-glht(m4, linfct =K)
summary(gm4ci)
JavaGD()
plot(gm4ci)

Ks <- diag(length(coef(m4s)))[-1,]
rownames(Ks) <- names(coef(m4s))[-1]
gm4sci<-glht(m4s, linfct =Ks)
summary(gm4sci)
JavaGD()
plot(gm4sci)

#Multiple Vergleiche: Post-hoc plots
JavaGD()
gm4<-glht(m4, linfct = mcp(FyearE = "Tukey"))
gm4
plot(gm4)
gm4s<-glht(m4s, linfct = mcp(FyearE = "Tukey"))
#wird dasselbe geben



#Quantile und GlŠttungen nach Jahr
aq<-tapply(ZK[,34],FyearE,function(x) quantile(x,0.95))
aq
ay<-seq(1985,2006)
plot(ay,aq)
amq<-lm(aq~ay)
summary(amq)
lines(lm$x,lm$fitted)

lmq<-loess(aq~ay)
lmq
lmq<-loess(aq~ay,span=0.3)
plot(ay,aq)
lines(lmq$x,lmq$fitted)

#Korrelationen
cc<-cor(ZK[,32:42])
mm<-matrix(cc,121,1)
a1<-rep(rownames(cc),11)
a1
a2<-rep(rownames(cc),each=11)
a2
pp<-sign(mm)
table(pp)
am<-abs(mm)
kk<-cbind(a1,a2,mm,am,pp)
colnames(kk)
colnames(kk)<-c("a1","a2","corr","abs","sign")
write.table(kk,file="kk.txt",quote=FALSE,sep="\t",row.names=FALSE)

#Modelle
m1<-lm(ZK[,37]~ZK[,32]+ZK[,33]+ZK[,34]+ZK[,35]+ZK[,36])
summary(m1)
par(mfrow=c(2,2))
plot(m1)
anova(m1)

m2<-lm(ZK[,37]~ZK[,32])
summary(m2)
plot(ZK[,32],ZK[,37])


m1<-lm(ZK[,38]~ZK[,32]+ZK[,33]+ZK[,34]+ZK[,35]+ZK[,36])
summary(m1)


JavaGD()
plot(ZK[,34],ZK[,39])
m3<-lm(ZK[,39]~ZK[,34]*FyearE)
summary(m3)
anova(m3)

#Parallel Coordinate Plots and Quantiles
q100<-quantile(ZK[,32],probs=seq(0,1,0.01))
qlj<-quantile(ZK[,33],probs=seq(0,1,0.01))
qsp<-quantile(ZK[,34],probs=seq(0,1,0.01))
qhj<-quantile(ZK[,35],probs=seq(0,1,0.01))
q400<-quantile(ZK[,36],probs=seq(0,1,0.01))
JavaGD()
ipcp(q100,qlj,qsp,qhj,q400)

aq100<-tapply(ZK[,32],FyearE,function(x) quantile(x,probs=seq(0.9,1,0.01)))
qx<-matrix(0,22,11)
for (i in 1:22) qx[i,]<-aq100[[i]]
tt<-table(FyearE)
dimnames(tt)
qt<-t(qx)
colnames(qt)<-dimnames(tt)$FyearE
ipcp(as.data.frame(qt))