#ELMR 5.5
library(faraway)
library(nnet)
data<-faraway::debt
ccID<-data$ccarduse
levels(ccID)<-c("Never","Occassional","Regularly")
summary(ccID)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.000 1.000 1.677 2.000 3.000 34
table(ccID)
## ccID
## 1 2 3
## 232 105 93
lmod<-lm(ccID~.,debt)
summary(lmod)
##
## Call:
## lm(formula = ccID ~ ., data = debt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.212e-15 -2.570e-17 -6.000e-20 3.269e-17 5.339e-16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.758e-16 8.735e-17 -2.013e+00 0.04508 *
## incomegp -9.831e-17 7.491e-18 -1.312e+01 < 2e-16 ***
## house -1.792e-17 1.492e-17 -1.201e+00 0.23061
## children 1.293e-17 8.617e-18 1.501e+00 0.13453
## singpar -1.165e-16 3.834e-17 -3.039e+00 0.00259 **
## agegp -2.945e-17 1.059e-17 -2.780e+00 0.00579 **
## bankacc -1.617e-16 2.677e-17 -6.042e+00 4.67e-09 ***
## bsocacc -4.879e-17 1.847e-17 -2.641e+00 0.00872 **
## manage -1.524e-17 1.019e-17 -1.496e+00 0.13583
## ccarduse 1.000e+00 1.186e-17 8.433e+16 < 2e-16 ***
## cigbuy -2.329e-17 1.938e-17 -1.202e+00 0.23035
## xmasbuy 2.745e-18 2.646e-17 1.040e-01 0.91744
## locintrn 5.821e-18 9.814e-18 5.930e-01 0.55352
## prodebt -8.800e-18 1.294e-17 -6.800e-01 0.49693
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.448e-16 on 290 degrees of freedom
## (160 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 7.472e+32 on 13 and 290 DF, p-value: < 2.2e-16
mmod<-multinom(ccID ~ incomegp + bankacc + prodebt, debt)
## # weights: 15 (8 variable)
## initial value 396.599036
## iter 10 value 332.156027
## final value 331.999894
## converged
summary(mmod)
## Call:
## multinom(formula = ccID ~ incomegp + bankacc + prodebt, data = debt)
##
## Coefficients:
## (Intercept) incomegp bankacc prodebt
## 2 -3.984421 0.2841986 1.620668 0.3239365
## 3 -7.006777 0.5041507 2.677107 0.6606552
##
## Std. Errors:
## (Intercept) incomegp bankacc prodebt
## 2 0.8107364 0.1050903 0.5549301 0.1946700
## 3 1.2528654 0.1154704 1.0423807 0.2069253
##
## Residual Deviance: 663.9998
## AIC: 679.9998
par(mfrow = c(1,3))
matplot(prop.table(table(data$prodebt,ccID),1),type="l",xlab="ProDebt",ylab="Proportion",lty=c(1,2,3))
matplot(prop.table(table(data$incomegp,ccID),1),type="l",xlab="Income Gapt",ylab="Proportion",lty=c(1,2,3))
matplot(prop.table(table(data$bankacc,ccID),1),type="l",xlab="Bank Act",ylab="Proportion",lty=c(1,2,3))
