Budworm- Data

# Dateneingabe
dose <- rep(c(1,2,4,8,16,32),2)
ldose <- rep(0:5, 2)
bud.weights <- rep(20,12)
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex <- factor(rep(c("M", "F"), c(6, 6)))
SF <- cbind(numdead, numalive=20-numdead)

# erste Modelle mit Interaktion
budworm.lg.2 <- glm(SF ~ sex*dose, family = binomial)
summary(budworm.lg.2)

budworm.lg.i <- glm(SF ~ sex*ldose, family=binomial)
summary(budworm.lg.i)

budworm.lg.A <- glm(SF ~ sex*I(ldose-3), family=binomial)
summary(budworm.lg.A)


plot(budworm.lg.i$linear.predictor, budworm.lg.i$residuals)

# Graphiken der vorhergesagten Werte gegen den linearen PrŠdiktor
par(mfrow=c(1,1))
ld<-seq(0,5,0.1)
plot(c(1,32), c(0,1), type ="n", xlab = "dose", ylab = "prob", log="x")
text(2^ldose, numdead/20, as.character(sex))
lines(2^ld, predict(budworm.lg.2, data.frame(dose=2^ld, 
sex=factor(rep("M", length(ld)),levels=levels(sex))), type 
="response"))
lines(2^ld, predict(budworm.lg.i, data.frame(ldose=ld, 
sex=factor(rep("M", length(ld)),levels=levels(sex))), type 
="response"),col=2)
lines(2^ld, predict(budworm.lg.A, data.frame(ldose=ld, 
sex=factor(rep("M", length(ld)),levels=levels(sex))), type 
="response"),col=3)
lines(2^ld, predict(budworm.lg.2, data.frame(dose=2^ld, 
sex=factor(rep("F", length(ld)),levels=levels(sex))), type 
="response"))
lines(2^ld, predict(budworm.lg.i, data.frame(ldose=ld, 
sex=factor(rep("F", length(ld)),levels=levels(sex))), type 
="response"),col=2)
lines(2^ld, predict(budworm.lg.A, data.frame(ldose=ld, 
sex=factor(rep("F", length(ld)),levels=levels(sex))), type 
="response"),col=3)

par(mfrow=c(1,3))
ld<-seq(0,5,0.1)
plot(c(1,32), c(0,1), type ="n", xlab = "dose", ylab = "prob", log="x")
text(2^ldose, numdead/20, as.character(sex))
lines(2^ld, predict(budworm.lg.2, data.frame(dose=2^ld, 
sex=factor(rep("M", length(ld)),levels=levels(sex))), type 
="response"))
lines(2^ld, predict(budworm.lg.2, data.frame(dose=2^ld, 
sex=factor(rep("F", length(ld)),levels=levels(sex))), type 
="response"))

plot(c(1,32), c(0,1), type ="n", xlab = "dose", ylab = "prob", log="x")
text(2^ldose, numdead/20, as.character(sex))
lines(2^ld, predict(budworm.lg.i, data.frame(ldose=ld, 
sex=factor(rep("M", length(ld)),levels=levels(sex))), type 
="response"),col=2)
lines(2^ld, predict(budworm.lg.i, data.frame(ldose=ld, 
sex=factor(rep("F", length(ld)),levels=levels(sex))), type 
="response"),col=2)

plot(c(1,32), c(0,1), type ="n", xlab = "dose", ylab = "prob", log="x")
text(2^ldose, numdead/20, as.character(sex))
lines(2^ld, predict(budworm.lg.A, data.frame(ldose=ld, 
sex=factor(rep("M", length(ld)),levels=levels(sex))), type 
="response"),col=3)
lines(2^ld, predict(budworm.lg.A, data.frame(ldose=ld, 
sex=factor(rep("F", length(ld)),levels=levels(sex))), type 
="response"),col=3)

# Logistische Regression mit Zielvariable als relative H"aufigkeiten
budworm.lgw.i <- glm(numdead/bud.weights ~ sex*ldose, 
weights=bud.weights,family=binomial)
summary(budworm.lgw.i)

budworm.lgw.0 <- glm(numdead/bud.weights ~ 1, 
weights=bud.weights,family=binomial)
summary(budworm.lgw.0)

# Probit-Modell f"ur Budworm-Daten
budworm.probit.i <- glm(SF ~ sex*ldose, 
family=binomial(link=probit))
summary(budworm.probit.i)