Data mtcars berisi tentang suatu mobil dengan beberap faktor atau avriabel. dalam hal ini saya disclaimer mungkin penentuan variabel bebas dan tidak bebasnya begitu berbeda, tetapi setidaknya saya memberikan cara untuk membuat suatau regresi logistik. mudah-mudahan berguna.
reglogmtcar.R
HP
2024-08-19
#Regresi Logistikmtcars
#Menyiapkan data MTcars
#Regresi mtcars
reglogmtcars<-glm(vs~disp+wt,data=mtcars,family="binomial")
summary(reglogmtcars)
##
## Call:
## glm(formula = vs ~ disp + wt, family = "binomial", data = mtcars)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.60859 2.43903 0.660 0.510
## disp -0.03443 0.01536 -2.241 0.025 *
## wt 1.62635 1.49068 1.091 0.275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 43.86 on 31 degrees of freedom
## Residual deviance: 21.40 on 29 degrees of freedom
## AIC: 27.4
##
## Number of Fisher Scoring iterations: 6
#KIta mencoba model lain mengganti wt dengan hp
reglogmtcars2<-glm(vs~disp+hp,data=mtcars,family="binomial")
summary(reglogmtcars2)
##
## Call:
## glm(formula = vs ~ disp + hp, family = "binomial", data = mtcars)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.158348 3.238656 2.519 0.0118 *
## disp -0.003515 0.011765 -0.299 0.7651
## hp -0.061257 0.035496 -1.726 0.0844 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 43.86 on 31 degrees of freedom
## Residual deviance: 16.75 on 29 degrees of freedom
## AIC: 22.75
##
## Number of Fisher Scoring iterations: 7
#KIta mencoba model lain mengganti hp dengan drat
reglogmtcars3<-glm(vs~disp+drat,data=mtcars,family="binomial")
summary(reglogmtcars3)
##
## Call:
## glm(formula = vs ~ disp + drat, family = "binomial", data = mtcars)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 20.78542 11.12266 1.869 0.0617 .
## disp -0.04371 0.01867 -2.341 0.0192 *
## drat -3.43990 2.16431 -1.589 0.1120
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 43.860 on 31 degrees of freedom
## Residual deviance: 19.468 on 29 degrees of freedom
## AIC: 25.468
##
## Number of Fisher Scoring iterations: 7
#KIta mencoba model lain mengganti menambah wt
reglogmtcars4<-glm(vs~disp+drat+wt,data=mtcars,family="binomial")
summary(reglogmtcars4)
##
## Call:
## glm(formula = vs ~ disp + drat + wt, family = "binomial", data = mtcars)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 16.95724 11.15757 1.520 0.1286
## disp -0.05287 0.02243 -2.357 0.0184 *
## drat -3.06950 2.08161 -1.475 0.1403
## wt 1.36072 1.61400 0.843 0.3992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 43.860 on 31 degrees of freedom
## Residual deviance: 18.726 on 28 degrees of freedom
## AIC: 26.726
##
## Number of Fisher Scoring iterations: 7
#KIta mencoba model semua variabel
reglogmtcars5<-glm(vs~disp+gear,data=mtcars,family="binomial")
#model yang paling mendektai signifikan
summary(reglogmtcars5)
##
## Call:
## glm(formula = vs ~ disp + gear, family = "binomial", data = mtcars)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 41.46032 20.55385 2.017 0.0437 *
## disp -0.08533 0.04000 -2.133 0.0329 *
## gear -6.64466 3.46953 -1.915 0.0555 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 43.860 on 31 degrees of freedom
## Residual deviance: 12.496 on 29 degrees of freedom
## AIC: 18.496
##
## Number of Fisher Scoring iterations: 8
reglogmtcars6<-glm(vs~disp+mpg,data=mtcars,family="binomial")
summary(reglogmtcars6)
##
## Call:
## glm(formula = vs ~ disp + mpg, family = "binomial", data = mtcars)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.38791 6.01361 0.065 0.949
## disp -0.01611 0.01051 -1.532 0.125
## mpg 0.13232 0.21448 0.617 0.537
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 43.860 on 31 degrees of freedom
## Residual deviance: 22.283 on 29 degrees of freedom
## AIC: 28.283
##
## Number of Fisher Scoring iterations: 6
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