rm(list=ls()) cells<-read.table("C:/Users/Chris/Documents/Stochastik III/R/cells.dat",header=T) oring<-read.table("C:/Users/Chris/Documents/Stochastik III/R/oring.dat",header=T) ####Aufgabe 2##### ###Logit### oring.logit<-glm(defekt~temp,data=oring,family=binomial(link=logit)) summary(oring.logit) index<-order(oring$temp) plot(oring$temp[index],oring.logit$fitted[index],type="l", xlim=c(45,81),ylim=c(0,1), xlab="Temperatur",ylab="Ausfallwahrscheinlichkeit") #add prediction pred<-predict(oring.logit,newdata=data.frame(temp=seq(45,53)),type="response") lines(seq(45,53),pred,col="red") ###Probit### oring.probit<-glm(defekt~temp,data=oring,family=binomial(link=probit)) summary(oring.probit) index<-order(oring$temp) plot(oring$temp[index],oring.probit$fitted[index],type="l", xlim=c(45,81),ylim=c(0,1), xlab="Temperatur",ylab="Ausfallwahrscheinlichkeit") #add prediction pred<-predict(oring.probit,newdata=data.frame(temp=seq(45,53)),type="response") lines(seq(45,53),pred,col="red") ###Teil b)### #von Hand theta<-oring.logit$coef[1]+oring.logit$coef[2]*45 phat<-exp(theta)/(exp(theta)+1) phat #direkt predict(oring.logit,newdata=data.frame(temp=45),type="response") ###NICHT verlangt, nur zum Vergleich... predict(oring.probit,newdata=data.frame(temp=45),type="response") ####Aufgabe 3##### ###Logit### cells.logit<-glm(death~dose+protected,data=cells,family=binomial(link=logit)) summary(cells.logit) quantil<-qchisq(.9,df=1) ###intercept (-3.46451/0.19318)^2 ### dose (0.11869/0.00534)^2 ### protected (-1.47558/0.15142)^2 ###Teil c### #behandelte Zelle predict(cells.logit,newdata=data.frame(dose=30,protected=1),type="response") #unbehandelte Zelle predict(cells.logit,newdata=data.frame(dose=30,protected=0),type="response")