library(readxl)
datos_tesis1 <- read_excel("C:/Users/Isabella/Desktop/datos tesis1.xlsx",
col_types = c("numeric", "text", "text",
"text", "numeric", "text", "text",
"numeric", "text", "text", "numeric",
"text", "text", "numeric", "text",
"text", "text", "text", "text", "text",
"numeric", "text", "text", "text",
"text", "text", "text"))
## New names:
## * `101. ¿Obtuvo el crédito solicitado?` -> `101. ¿Obtuvo el crédito solicitado?...10`
## * `101. ¿Obtuvo el crédito solicitado?` -> `101. ¿Obtuvo el crédito solicitado?...11`
modelofinal=glm(formula= Y~EDAD+EMPLEADOS+CC+RUT+X1+DEUDAASUMIDA+M1+M2+SOLVENCIA+MORA, family="binomial", data = datos_tesis1)
summary(modelofinal)
##
## Call:
## glm(formula = Y ~ EDAD + EMPLEADOS + CC + RUT + X1 + DEUDAASUMIDA +
## M1 + M2 + SOLVENCIA + MORA, family = "binomial", data = datos_tesis1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1107 -0.9077 0.1635 0.8967 1.7956
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.05778 0.18250 -5.796 6.79e-09 ***
## EDAD 0.05571 0.01331 4.184 2.86e-05 ***
## EMPLEADOS 0.03415 0.07388 0.462 0.6439
## CC1 0.06281 0.25441 0.247 0.8050
## RUT1 0.47338 0.21516 2.200 0.0278 *
## X11 3.72502 0.66018 5.642 1.68e-08 ***
## DEUDAASUMIDA1 1.67195 0.94386 1.771 0.0765 .
## M11 -1.03870 0.82233 -1.263 0.2065
## M21 -0.38911 0.70032 -0.556 0.5785
## SOLVENCIA1 -1.20189 0.71806 -1.674 0.0942 .
## MORA1 0.78793 0.98640 0.799 0.4244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 947.87 on 715 degrees of freedom
## Residual deviance: 664.78 on 705 degrees of freedom
## AIC: 686.78
##
## Number of Fisher Scoring iterations: 6
exp(modelofinal$coefficients)
## (Intercept) EDAD EMPLEADOS CC1 RUT1
## 0.3472250 1.0572864 1.0347414 1.0648282 1.6054144
## X11 DEUDAASUMIDA1 M11 M21 SOLVENCIA1
## 41.4719118 5.3225294 0.3539158 0.6776591 0.3006247
## MORA1
## 2.1988318
mod=predict(modelofinal,type = "response")
library(pROC)
## Warning: package 'pROC' was built under R version 4.0.5
## Type 'citation("pROC")' for a citation.
##
## Attaching package: 'pROC'
## The following objects are masked from 'package:stats':
##
## cov, smooth, var
objroc <- roc(mod, datos_tesis1$Y,auc=T,ci=T)
## Warning in roc.default(mod, datos_tesis1$Y, auc = T, ci = T): 'response'
## has more than two levels. Consider setting 'levels' explicitly or using
## 'multiclass.roc' instead
## Setting levels: control = 0.197594379756048, case = 0.199482282252067
## Setting direction: controls < cases
## Warning in ci.auc.roc(roc, ...): ci.auc() of a ROC curve with AUC == 1 is always
## 1-1 and can be misleading.
plot.roc(objroc,print.auc=T,print.thres = "best",col="blue",xlab="1-ESpecificidad",ylab="Sensibilidad")
