Model Tobit

Model Tobit

Langkah Langkah Penyelesaian Model Tobit

Langkah 1: Menginstal dan Membaca Packages ggplot2, GGally, dan VGAM

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.0.5

library(GGally)

## Warning: package 'GGally' was built under R version 4.0.5

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

library(VGAM)

## Warning: package 'VGAM' was built under R version 4.0.5

## Loading required package: stats4

## Loading required package: splines

Langkah 2: Mengimpor data yang telah ditabulasi di Microsoft Excel, setelah itu membaca data yang tersimpan di rektori kerja kita

library(readxl)
Profesi <- read_excel("EKONOMETRIKA/Profesi.xlsx")
View(Profesi)

Langkah 3: Menghitung Statistik Deskriptif dengan menggunakan fungsi summary ()

summary(Profesi)

##       Edu            Gender         Kat_Prof1        Kat_Prof2   
##  Min.   : 8.00   Min.   :0.0000   Min.   :0.0000   Min.   :0.00  
##  1st Qu.:12.00   1st Qu.:0.0000   1st Qu.:1.0000   1st Qu.:0.00  
##  Median :13.00   Median :0.0000   Median :1.0000   Median :0.00  
##  Mean   :13.98   Mean   :0.2267   Mean   :0.7689   Mean   :0.06  
##  3rd Qu.:16.00   3rd Qu.:0.0000   3rd Qu.:1.0000   3rd Qu.:0.00  
##  Max.   :22.00   Max.   :1.0000   Max.   :1.0000   Max.   :1.00  
##    Kat_Prof3         Profesi     
##  Min.   :0.0000   Min.   :1.000  
##  1st Qu.:0.0000   1st Qu.:1.000  
##  Median :0.0000   Median :1.000  
##  Mean   :0.1711   Mean   :1.402  
##  3rd Qu.:0.0000   3rd Qu.:1.000  
##  Max.   :1.0000   Max.   :3.000

Langkah 4: Membuat Model Tobit

summary(m<-vglm(Profesi~Edu+Gender,tobit(Upper = 450), data = Profesi))

## 
## Call:
## vglm(formula = Profesi ~ Edu + Gender, family = tobit(Upper = 450), 
##     data = Profesi)
## 
## Coefficients: 
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept):1 -0.354786   0.145510  -2.438   0.0148 *  
## (Intercept):2 -0.437861   0.034173 -12.813   <2e-16 ***
## Edu            0.128138   0.009966  12.858   <2e-16 ***
## Gender        -0.151611   0.073365  -2.067   0.0388 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Names of linear predictors: mu, loglink(sd)
## 
## Log-likelihood: -441.4852 on 896 degrees of freedom
## 
## Number of Fisher scoring iterations: 5 
## 
## No Hauck-Donner effect found in any of the estimates

Langkah 5: Menghitung nilai p untuk msing-masing koefisien model tobit

ctable<-coef(summary(m))
pvals<-2*pt(abs(ctable[,"z value"]),df.residual(m), lower.tail = FALSE)
cbind(ctable,pvals)

##                 Estimate  Std. Error    z value     Pr(>|z|)        pvals
## (Intercept):1 -0.3547857 0.145509853  -2.438225 1.475959e-02 1.495296e-02
## (Intercept):2 -0.4378606 0.034172756 -12.813148 1.384027e-37 1.237696e-34
## Edu            0.1281383 0.009965851  12.857736 7.782273e-38 7.610497e-35
## Gender        -0.1516111 0.073364540  -2.066544 3.877712e-02 3.906396e-02

Langkah 6: Menguji Signifikansi secara keseluruhan

m2<-vglm(Profesi~Edu+Gender,tobit(Upper = 450),data = Profesi)
(p<-pchisq(2*logLik(m)-logLik(m2), df=2, lower.tail = FALSE))

## [1] 1

Langkah 7: Menghitung tingkat Kepercayaan

b<-coef(m)
se<-sqrt(diag(vcov(m)))

cbind(LL=b-qnorm(0.975)*se,UL=b+qnorm(0.975)*se)

##                       LL           UL
## (Intercept):1 -0.6399798 -0.069591676
## (Intercept):2 -0.5048380 -0.370883221
## Edu            0.1086056  0.147670988
## Gender        -0.2954029 -0.007819207

Langkah 8: Menguji Kelayakan Model

Profesi$yhat<-fitted(m)[,1]
Profesi$rr<-resid(m, type = "response")
Profesi$rp<-resid(m, type = "pearson")[,1]

par(mfcol = c(2,3))

with(Profesi, {plot(yhat, rr, main = "Fitted vs Residual") 
  qqnorm(rr)
  plot(yhat, rp, main = "Fitted vs Person Residuals")
  qqnorm(rp)
  plot(Profesi, rp, main = "Actual vs Person residuals")
  plot(Profesi, yhat, main = "Actual vs Fitted")})