【数据分析入门】R语言之广义线性回归与logistics回归

广义线性回归?glmdata(breslow.dat, package="robust") names(breslow.dat)summary(breslow.dat[c(6，7，8，10)])attach(breslow.dat)> fit <-glm(sumY ~ Base + Age + Trt,data=breslow.dat, family=poisson(link="log

如果我是温帅帅

685人浏览 · 2022-04-11 11:50:15

如果我是温帅帅 · 2022-04-11 11:50:15 发布

广义线性回归

?glm
data(breslow.dat, package="robust") names(breslow.dat)
summary(breslow.dat[c(6，7，8，10)])
attach(breslow.dat)

在这里插入图片描述

> fit <-glm(sumY ~ Base + Age + Trt,data=breslow.dat, family=poisson(link="log")) 
> summary(fit)

在这里插入图片描述

coef(fit)
exp(coef(fit))

在这里插入图片描述

logistics回归

data(Affairs, package="AER") summary(Affairs)
table(Affairs$affairs)
prop.table(table(Affairs$affairs)) prop.table(table(Affairs$gender))

Affairs$ynaffair[Affairs$affairs > 0] <-1 Affairs$ynaffair[Affairs$affairs==0] <-0 Affairs$ynaffair <-factor(Affairs$ynaffair,                     levels=c(0,1),labels=c("NO","Yes"))
table(Affairs$ynaffair)

在这里插入图片描述

attach(Affairs)
fit<-glm(ynaffair ~ gender + age + yearsmarried + children + 
                   religiousness+ education+occupation +rating, data=Affairs,family=binomial())
summary(fit)

在这里插入图片描述

fit1<-glm(ynaffair ~  age + yearsmarried + 
                   religiousness+  +rating, data=Affairs,family=binomial())
summary(fit)

在这里插入图片描述

anova(fit,fit1,test="Chisq")

在这里插入图片描述

data.frame(rating =c(1,2,3,4，5)
age = mean(Affairs$age),
yearsmarried = mean(Affairs$yearsmarried), rcliaiousness = mean(Affairs$religiousness)
testdata$prob <- predict(fitl, newdata=testdata, type="response")
testdata <-data.frame(rating = mean(Affairs$rating),
age = seq(17,57,10),
yearsmarried = mean(Affairs$yearsmarried), reliaiousness= mean(Affairs$religiousness)