#Logistic 回归
install.packages("AER")
data(Affairs,package="AER")
summary(Affairs)

affairs gender age yearsmarried children
Min. : 0.000 female:315 Min. :17.50 Min. : 0.125 no :171
1st Qu.: 0.000 male :286 1st Qu.:27.00 1st Qu.: 4.000 yes:430
Median : 0.000 Median :32.00 Median : 7.000
Mean : 1.456 Mean :32.49 Mean : 8.178
3rd Qu.: 0.000 3rd Qu.:37.00 3rd Qu.:15.000
Max. :12.000 Max. :57.00 Max. :15.000

religiousness education occupation rating
Min. :1.000 Min. : 9.00 Min. :1.000 Min. :1.000
1st Qu.:2.000 1st Qu.:14.00 1st Qu.:3.000 1st Qu.:3.000
Median :3.000 Median :16.00 Median :5.000 Median :4.000
Mean :3.116 Mean :16.17 Mean :4.195 Mean :3.932
3rd Qu.:4.000 3rd Qu.:18.00 3rd Qu.:6.000 3rd Qu.:5.000
Max. :5.000 Max. :20.00 Max. :7.000 Max. :5.000

table(Affairs$affairs)

0   1   2 3   7 12
451 34 17 19 42 38

#构建因子

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)

No Yes
451 150

#拟合Logistic回归
fit.full<-glm(ynaffair~gender+age+yearsmarried+children+religiousness+education+occupation+rating,data=Affairs,family=binomial())
summary(fit.full)

Call:
glm(formula = ynaffair ~ gender + age + yearsmarried + children +
religiousness + education + occupation + rating, family = binomial(),
data = Affairs)

Deviance Residuals:
Min 1Q Median 3Q Max
-1.5713 -0.7499 -0.5690 -0.2539 2.5191

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.37726 0.88776 1.551 0.120807
gendermale 0.28029 0.23909 1.172 0.241083
age -0.04426 0.01825 -2.425 0.015301 *
yearsmarried 0.09477 0.03221 2.942 0.003262 **
childrenyes 0.39767 0.29151 1.364 0.172508
religiousness -0.32472 0.08975 -3.618 0.000297 ***
education 0.02105 0.05051 0.417 0.676851
occupation 0.03092 0.07178 0.431 0.666630
rating -0.46845 0.09091 -5.153 2.56e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 675.38 on 600 degrees of freedom
Residual deviance: 609.51 on 592 degrees of freedom
AIC: 627.51

Number of Fisher Scoring iterations: 4

去除不显著的变量，重新拟合

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

Call:
glm(formula = ynaffair ~ age + yearsmarried + religiousness +
rating, family = binomial(), data = Affairs)

Deviance Residuals:
Min 1Q Median 3Q Max
-1.6278 -0.7550 -0.5701 -0.2624 2.3998

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.93083 0.61032 3.164 0.001558 **
age -0.03527 0.01736 -2.032 0.042127 *
yearsmarried 0.10062 0.02921 3.445 0.000571 ***
religiousness -0.32902 0.08945 -3.678 0.000235 ***
rating -0.46136 0.08884 -5.193 2.06e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 675.38 on 600 degrees of freedom
Residual deviance: 615.36 on 596 degrees of freedom
AIC: 625.36

Number of Fisher Scoring iterations: 4

#检验fit.reduced模型，检验新full和reduce模型的效果是否一致
anova(fit.reduced,fit.full,test="Chisq")

Analysis of Deviance Table

Model 1: ynaffair ~ age + yearsmarried + religiousness + rating
Model 2: ynaffair ~ gender + age + yearsmarried + children + religiousness +
education + occupation + rating
Resid. Df Resid. Dev Df Deviance Pr(>Chi)
1 596 615.36
2 592 609.51 4 5.8474 0.2108   #结果不显著，说明这两个模型不独立，效果类似

#模型参数
coef(fit.reduced)  #返回结果基于对数

(Intercept) age yearsmarried religiousness rating
1.93083017 -0.03527112 0.10062274 -0.32902386 -0.46136144

exp(coef(fit.reduced))

(Intercept) age       yearsmarried       religiousness    rating 
6.8952321 0.9653437   1.1058594          0.7196258        0.6304248  #大于0是正向影响，小于0是负向影响

#评价预测变量对概率的影响概率
testdata<-data.frame(rating=c(1,2,3,4,5),age=mean(Affairs\$age),yearsmarried=mean(Affairs\$yearsmarried),religiousness=mean(Affairs$religiousness))  
testdata

rating age yearsmarried religiousness   #针对rating进行预测
1 1 32.48752 8.177696 3.116473
2 2 32.48752 8.177696 3.116473
3 3 32.48752 8.177696 3.116473
4 4 32.48752 8.177696 3.116473
5 5 32.48752 8.177696 3.116473

testdata$prob<-predict(fit.reduced,newdata=testdata,type="response")
testdata

rating age yearsmarried religiousness prob  #rating从1变为5时，婚外情发生的可能性则从0.5逐渐减小到0.15
1 1 32.48752 8.177696 3.116473 0.5302296
2 2 32.48752 8.177696 3.116473 0.4157377
3 3 32.48752 8.177696 3.116473 0.3096712
4 4 32.48752 8.177696 3.116473 0.2204547
5 5 32.48752 8.177696 3.116473 0.1513079

#过度离势（是指所欲估计参数的实际变异数大于原先期望的变异数，这使我们进行统计推论时出现谬误。）

deviance(fit.reduced)/df.residual(fit.reduced) #检测是否有离群点

[1] 1.03248  #结果为1左右，没有过度离势

#过度离势检测
fit<-glm(ynaffair~age+yearsmarried+religiousness+rating,family=binomial(),data=Affairs)
fit.od<-glm(ynaffair~age+yearsmarried+religiousness+rating,family=quasibinomial(),data=Affairs)
pchisq(summary(fit.od)$dispersion*

fit$df.residual

,fit$df.residual,lower=F)

[1] 0.340122  （P>0.05) #结果为：不显著，说明模型没有过度离势的问题

#泊松回归
install.packages("robust")
data(breslow.dat,package="robust")
names(breslow.dat)
summary(breslow.dat[c(6,7,8,10)])

Base Age Trt sumY
Min. : 6.00 Min. :18.00 placebo :28 Min. : 0.00
1st Qu.: 12.00 1st Qu.:23.00 progabide:31 1st Qu.: 11.50
Median : 22.00 Median :28.00 Median : 16.00
Mean : 31.22 Mean :28.34 Mean : 33.05
3rd Qu.: 41.00 3rd Qu.:32.00 3rd Qu.: 36.00
Max. :151.00 Max. :42.00 Max. :302.00

opar<-par(no.readonly=TRUE)
par(mfrow=c(1,2))
#attach(breslow.dat)
hist(sumY,breaks=20,xlab="Seizure Count",main="Distribution of Seizures")
boxplot(sumY~Trt,xlab="Treatment",main="Group Comparisons")
par(opar)

#拟合泊松回归
fit<-glm(sumY~Base+Age+Trt,data=breslow.dat,family = poisson())
summary(fit)

Call:
glm(formula = sumY ~ Base + Age + Trt, family = poisson(), data = breslow.dat)

Deviance Residuals:
Min 1Q Median 3Q Max
-6.0569 -2.0433 -0.9397 0.7929 11.0061

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.9488259 0.1356191 14.370 < 2e-16 ***
Base 0.0226517 0.0005093 44.476 < 2e-16 ***
Age 0.0227401 0.0040240 5.651 1.59e-08 ***
Trtprogabide -0.1527009 0.0478051 -3.194 0.0014 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for poisson family taken to be 1)

Null deviance: 2122.73 on 58 degrees of freedom
Residual deviance: 559.44 on 55 degrees of freedom
AIC: 850.71

Number of Fisher Scoring iterations: 5

#解释模型参数
coef(fit)

(Intercept) Base Age Trtprogabide
1.94882593 0.02265174 0.02274013 -0.15270095

exp(coef(fit))

(Intercept) Base Age Trtprogabide
7.0204403 1.0229102 1.0230007 0.8583864

#过度离势
deviance(fit)/df.residual(fit)

[1] 10.1717  #大于1，存在过度离势现象

Overdispersion test Obs.Var/Theor.Var Statistic p-value
       poisson data 62.87013          3646.468  0  #结果小于0.05，显著，说明存在过度离势现象

#存在过度离势，重新拟合泊松回归
fit.od<-glm(sumY~Base+Age+Trt,data=breslow.dat,family=quasipoisson())
summary(fit.od)

Call:
glm(formula = sumY ~ Base + Age + Trt, family = quasipoisson(),
data = breslow.dat)

Deviance Residuals:
Min 1Q Median 3Q Max
-6.0569 -2.0433 -0.9397 0.7929 11.0061

Coefficients:
Estimate Std. Error t value Pr(>|t|)    #重新拟合后，估计参数没有变化，但是标准误差变大了不少
(Intercept) 1.948826 0.465091 4.190 0.000102 ***
Base 0.022652 0.001747 12.969 < 2e-16 ***
Age 0.022740 0.013800 1.648 0.105085
Trtprogabide -0.152701 0.163943 -0.931 0.355702
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for quasipoisson family taken to be 11.76075)

Null deviance: 2122.73 on 58 degrees of freedom
Residual deviance: 559.44 on 55 degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 5