逻辑回归
> ###############逻辑回归
> setwd("/Users/yaozhilin/Downloads/R_edu/data")
> accepts<-read.csv("accepts.csv")
> names(accepts)
[1] "application_id" "account_number" "bad_ind" "vehicle_year" "vehicle_make"
[6] "bankruptcy_ind" "tot_derog" "tot_tr" "age_oldest_tr" "tot_open_tr"
[11] "tot_rev_tr" "tot_rev_debt" "tot_rev_line" "rev_util" "fico_score"
[16] "purch_price" "msrp" "down_pyt" "loan_term" "loan_amt"
[21] "ltv" "tot_income" "veh_mileage" "used_ind"
> accepts<-accepts[complete.cases(accepts),]
> select<-sample(1:nrow(accepts),length(accepts$application_id)*0.7)
> train<-accepts[select,]###70%用于建模
> test<-accepts[-select,]###30%用于检测
> attach(train)
> ###用glm(y~x,family=binomial(link="logit"))
> gl<-glm(bad_ind~fico_score,family=binomial(link = "logit"))
> summary(gl)
Call:
glm(formula = bad_ind ~ fico_score, family = binomial(link = "logit"))
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0794 -0.6790 -0.4937 -0.3073 2.6028
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 9.049667 0.629120 14.38 <2e-16 ***
fico_score -0.015407 0.000938 -16.43 <2e-16 ***
---
Signif. codes: 0 ‘***' 0.001 ‘**' 0.01 ‘*' 0.05 ‘.' 0.1 ‘ ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2989.2 on 3046 degrees of freedom
Residual deviance: 2665.9 on 3045 degrees of freedom
AIC: 2669.9
Number of Fisher Scoring iterations: 5
多元逻辑回归
> ###多元逻辑回归
> gls<-glm(bad_ind~fico_score+bankruptcy_ind+age_oldest_tr+
+ tot_derog+rev_util+veh_mileage,family = binomial(link = "logit"))
> summary(gls)
Call:
glm(formula = bad_ind ~ fico_score + bankruptcy_ind + age_oldest_tr +
tot_derog + rev_util + veh_mileage, family = binomial(link = "logit"))
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2646 -0.6743 -0.4647 -0.2630 2.8177
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 8.205e+00 7.433e-01 11.039 < 2e-16 ***
fico_score -1.338e-02 1.092e-03 -12.260 < 2e-16 ***
bankruptcy_indY -3.771e-01 1.855e-01 -2.033 0.0421 *
age_oldest_tr -4.458e-03 6.375e-04 -6.994 2.68e-12 ***
tot_derog 3.012e-02 1.552e-02 1.941 0.0523 .
rev_util 3.763e-04 5.252e-04 0.717 0.4737
veh_mileage 2.466e-06 1.381e-06 1.786 0.0741 .
---
Signif. codes: 0 ‘***' 0.001 ‘**' 0.01 ‘*' 0.05 ‘.' 0.1 ‘ ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2989.2 on 3046 degrees of freedom
Residual deviance: 2601.4 on 3040 degrees of freedom
AIC: 2615.4
Number of Fisher Scoring iterations: 5
> glss<-step(gls,direction = "both")
Start: AIC=2615.35
bad_ind ~ fico_score + bankruptcy_ind + age_oldest_tr + tot_derog +
rev_util + veh_mileage
Df Deviance AIC
- rev_util 1 2601.9 2613.9
<none> 2601.3 2615.3
- veh_mileage 1 2604.4 2616.4
- tot_derog 1 2605.1 2617.1
- bankruptcy_ind 1 2605.7 2617.7
- age_oldest_tr 1 2655.9 2667.9
- fico_score 1 2763.8 2775.8
Step: AIC=2613.88
bad_ind ~ fico_score + bankruptcy_ind + age_oldest_tr + tot_derog +
veh_mileage
Df Deviance AIC
<none> 2601.9 2613.9
- veh_mileage 1 2604.9 2614.9
+ rev_util 1 2601.3 2615.3
- tot_derog 1 2605.7 2615.7
- bankruptcy_ind 1 2606.1 2616.1
- age_oldest_tr 1 2656.9 2666.9
- fico_score 1 2773.2 2783.2
> #出来的数据是logit，我们需要转换 > train$pre<-predict(glss,train) > #出来的数据是logit，我们需要转换 > train$pre<-predict(glss,train) > summary(train$pre) Min. 1st Qu. Median Mean 3rd Qu. Max. -4.868 -2.421 -1.671 -1.713 -1.011 2.497 > train$pre_p<-1/(1+exp(-1*train$pre)) > summary(train$pre_p) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00763 0.08157 0.15823 0.19298 0.26677 0.92395
#逻辑回归不需要检测扰动项，但需要检测共线性 > library(car) > vif(glss) > fico_score bankruptcy_ind age_oldest_tr tot_derog veh_mileage >1.271283 1.144846 1.075603 1.423850 1.003616
