> ###############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
1 > #逻辑回归不需要检测扰动项，但需要检测共线性 2 > library(car) 3 > vif(glss) 4 fico_score bankruptcy_ind age_oldest_tr tot_derog veh_mileage 5 1.271283 1.144846 1.075603 1.423850 1.003616