#package
library(ggplot2)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(gmodels)
library(ggmosaic)
library(corrplot)## corrplot 0.92 loadedlibrary(caret)## Loading required package: lattice
##
## Attaching package: 'caret'
##
## The following object is masked from 'package:purrr':
##
## liftlibrary(rpart)
library(rpart.plot)
#library(c50)
library(tidyr)
library(dplyr)
#library(knitr)
library(rsample)
library(readxl)
library(MASS)##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select#input
data_pendapatan<-read_xlsx("C:/Users/user/Downloads/Data Latihan.xlsx", sheet="Pendapatan")
head(data_pendapatan) ## # A tibble: 6 × 7
## Pendidikan Klasifikasi.Daerah Jenis.Kelamin Status.KRT Usia Status.Perkawinan
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 0 1 1 49 1
## 2 0 0 1 1 47 1
## 3 0 0 1 1 61 1
## 4 1 0 0 0 23 0
## 5 0 0 1 1 27 1
## 6 0 0 1 1 50 1
## # ℹ 1 more variable: Pendapatan <dbl>#merubah variabel ke faktor
data_pendapatan$Pendidikan<- as.factor(data_pendapatan$Pendidikan)
data_pendapatan$Klasifikasi.Daerah<- as.factor(data_pendapatan$Klasifikasi.Daerah)
data_pendapatan$Jenis.Kelamin<-as.factor(data_pendapatan$Jenis.Kelamin)
data_pendapatan$Status.KRT<-as.factor(data_pendapatan$Status.KRT)
data_pendapatan$Status.Perkawinan<-as.factor(data_pendapatan$Status.Perkawinan)
data_pendapatan$Pendapatan<-as.factor(data_pendapatan$Pendapatan)
str(data_pendapatan$Pendapatan)## Factor w/ 4 levels "1","2","3","4": 2 2 1 2 2 2 4 1 1 1 ...library(arsenal)##
## Attaching package: 'arsenal'## The following object is masked from 'package:lubridate':
##
## is.Datetab<-tableby(Pendapatan~ .,data=data_pendapatan)
summary(tab,text=TRUE)##
##
## | | 1 (N=279) | 2 (N=568) | 3 (N=133) | 4 (N=20) | Total (N=1000) | p value|
## |:------------------|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|-------:|
## |Pendidikan | | | | | | < 0.001|
## |- 0 | 268 (96.1%) | 524 (92.3%) | 74 (55.6%) | 8 (40.0%) | 874 (87.4%) | |
## |- 1 | 11 (3.9%) | 44 (7.7%) | 59 (44.4%) | 12 (60.0%) | 126 (12.6%) | |
## |Klasifikasi.Daerah | | | | | | < 0.001|
## |- 0 | 66 (23.7%) | 128 (22.5%) | 2 (1.5%) | 0 (0.0%) | 196 (19.6%) | |
## |- 1 | 213 (76.3%) | 440 (77.5%) | 131 (98.5%) | 20 (100.0%) | 804 (80.4%) | |
## |Jenis.Kelamin | | | | | | < 0.001|
## |- 0 | 132 (47.3%) | 128 (22.5%) | 45 (33.8%) | 4 (20.0%) | 309 (30.9%) | |
## |- 1 | 147 (52.7%) | 440 (77.5%) | 88 (66.2%) | 16 (80.0%) | 691 (69.1%) | |
## |Status.KRT | | | | | | < 0.001|
## |- 0 | 141 (50.5%) | 208 (36.6%) | 54 (40.6%) | 4 (20.0%) | 407 (40.7%) | |
## |- 1 | 138 (49.5%) | 360 (63.4%) | 79 (59.4%) | 16 (80.0%) | 593 (59.3%) | |
## |Usia | | | | | | < 0.001|
## |- Mean (SD) | 42.957 (14.304) | 38.165 (12.304) | 39.910 (10.340) | 46.000 (5.554) | 39.891 (12.753) | |
## |- Range | 15.000 - 80.000 | 14.000 - 78.000 | 19.000 - 66.000 | 37.000 - 56.000 | 14.000 - 80.000 | |
## |Status.Perkawinan | | | | | | 0.007|
## |- 0 | 91 (32.6%) | 138 (24.3%) | 32 (24.1%) | 1 (5.0%) | 262 (26.2%) | |
## |- 1 | 188 (67.4%) | 430 (75.7%) | 101 (75.9%) | 19 (95.0%) | 738 (73.8%) | |#pembetukan model
#Partisi data
set.seed(123)
acak<-createDataPartition(data_pendapatan$Pendapatan,p=0.8, list=FALSE)
data_train<-data_pendapatan[acak, ]
data_test<-data_pendapatan[-acak,]summary(data_train)## Pendidikan Klasifikasi.Daerah Jenis.Kelamin Status.KRT Usia
## 0:700 0:164 0:243 0:329 Min. :15.00
## 1:102 1:638 1:559 1:473 1st Qu.:30.00
## Median :39.00
## Mean :39.57
## 3rd Qu.:48.00
## Max. :80.00
## Status.Perkawinan Pendapatan
## 0:219 1:224
## 1:583 2:455
## 3:107
## 4: 16
##
## summary(data_test)## Pendidikan Klasifikasi.Daerah Jenis.Kelamin Status.KRT Usia
## 0:174 0: 32 0: 66 0: 78 Min. :14.00
## 1: 24 1:166 1:132 1:120 1st Qu.:32.00
## Median :40.00
## Mean :41.18
## 3rd Qu.:49.75
## Max. :78.00
## Status.Perkawinan Pendapatan
## 0: 43 1: 55
## 1:155 2:113
## 3: 26
## 4: 4
##
## #model
model_pendapatan <- polr(Pendapatan~., method="logistic", data=data_train, Hess=T)
summary(model_pendapatan)## Call:
## polr(formula = Pendapatan ~ ., data = data_train, Hess = T, method = "logistic")
##
## Coefficients:
## Value Std. Error t value
## Pendidikan1 2.52138 0.237066 10.636
## Klasifikasi.Daerah1 0.56414 0.175636 3.212
## Jenis.Kelamin1 0.54822 0.199061 2.754
## Status.KRT1 0.64226 0.212620 3.021
## Usia -0.03221 0.006865 -4.692
## Status.Perkawinan1 0.53006 0.180398 2.938
##
## Intercepts:
## Value Std. Error t value
## 1|2 -0.5217 0.3040 -1.7161
## 2|3 2.6805 0.3220 8.3249
## 3|4 5.1927 0.4173 12.4437
##
## Residual Deviance: 1454.495
## AIC: 1472.495#uji multicol
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## somevif(model_pendapatan)## Pendidikan Klasifikasi.Daerah Jenis.Kelamin Status.KRT
## 1.045494 1.026166 1.637565 2.146962
## Usia Status.Perkawinan
## 1.467146 1.279745#uji gof
library(generalhoslem)## Loading required package: reshape##
## Attaching package: 'reshape'## The following object is masked from 'package:lubridate':
##
## stamp## The following object is masked from 'package:dplyr':
##
## rename## The following objects are masked from 'package:tidyr':
##
## expand, smithslipsitz.test(model_pendapatan)##
## Lipsitz goodness of fit test for ordinal response models
##
## data: formula: Pendapatan ~ Pendidikan + Klasifikasi.Daerah + Jenis.Kelamin + formula: Status.KRT + Usia + Status.Perkawinan
## LR statistic = 25.252, df = 9, p-value = 0.002705#partial test
coefmodel <- c(-model_pendapatan$coefficients)
koefisien<-coef(summary(model_pendapatan))
#menghitung p-value
p<-pnorm(abs(koefisien[,"t value"]),lower.tail = FALSE)*2
(ctabel<-cbind <-cbind(round(koefisien,4),"p-value"=round(p,4)))## Value Std. Error t value p-value
## Pendidikan1 2.5214 0.2371 10.6358 0.0000
## Klasifikasi.Daerah1 0.5641 0.1756 3.2120 0.0013
## Jenis.Kelamin1 0.5482 0.1991 2.7540 0.0059
## Status.KRT1 0.6423 0.2126 3.0207 0.0025
## Usia -0.0322 0.0069 -4.6924 0.0000
## Status.Perkawinan1 0.5301 0.1804 2.9383 0.0033
## 1|2 -0.5217 0.3040 -1.7161 0.0862
## 2|3 2.6805 0.3220 8.3249 0.0000
## 3|4 5.1927 0.4173 12.4437 0.0000#prediksi pada data test
predict_prob=predict(model_pendapatan, data_test, type="prob")
predict_prob## 1 2 3 4
## 1 0.33989193 0.5869007 0.06684284 0.0063644817
## 2 0.09094504 0.6200168 0.25712374 0.0319143966
## 3 0.34715626 0.5817922 0.06488758 0.0061639731
## 4 0.22654704 0.6515250 0.11079339 0.0111345959
## 5 0.45852474 0.4956463 0.04194932 0.0038796221
## 6 0.27736764 0.6268207 0.08729227 0.0085193724
## 7 0.45053826 0.5022034 0.04325224 0.0040061223
## 8 0.42708974 0.5211741 0.04733153 0.0044046568
## 9 0.16596916 0.6643290 0.15339845 0.0163033801
## 10 0.47871059 0.4788780 0.03883282 0.0035785796
## 11 0.41888599 0.5277037 0.04885576 0.0045545422
## 12 0.17047630 0.6643126 0.14941636 0.0157947308
## 13 0.19957107 0.6601811 0.12719275 0.0130550895
## 14 0.23224118 0.6492378 0.10773556 0.0107854394
## 15 0.49064332 0.4688438 0.03710068 0.0034121918
## 16 0.16155797 0.6641529 0.15746103 0.0168281297
## 17 0.17978138 0.6637024 0.14169240 0.0148238350
## 18 0.06370555 0.5621674 0.32789533 0.0462316915
## 19 0.45053826 0.5022034 0.04325224 0.0040061223
## 20 0.02126057 0.3268849 0.52003914 0.1318153519
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## 25 0.22619708 0.6516608 0.11098546 0.0111566195
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## 35 0.04556406 0.4944027 0.39541220 0.0646210140
## 36 0.04418351 0.4877719 0.40144897 0.0665956573
## 37 0.03509392 0.4369870 0.44477770 0.0831413986
## 38 0.31855620 0.6014032 0.07303489 0.0070057152
## 39 0.44665532 0.5053745 0.04390095 0.0040692477
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## 193 0.17978138 0.6637024 0.14169240 0.0148238350
## 194 0.15724192 0.6637843 0.16160429 0.0173694711
## 195 0.37303323 0.5629819 0.05847229 0.0055126196
## 196 0.20476669 0.6588248 0.12376200 0.0126464808
## 197 0.40362728 0.5396848 0.05183851 0.0048493980
## 198 0.30807312 0.6082240 0.07634991 0.0073529734#confussion matrix
prediksi.test <- predict(model_pendapatan, data_test, type="class")
data_test$Pendapatan<-as.factor(data_test$Pendapatan)
confusionMatrix(as.factor(prediksi.test), data_test$Pendapatan, positive="1")## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4
## 1 11 2 0 0
## 2 43 107 23 2
## 3 1 4 3 2
## 4 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.6111
## 95% CI : (0.5394, 0.6794)
## No Information Rate : 0.5707
## P-Value [Acc > NIR] : 0.1406
##
## Kappa : 0.1738
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4
## Sensitivity 0.20000 0.9469 0.11538 0.0000
## Specificity 0.98601 0.2000 0.95930 1.0000
## Pos Pred Value 0.84615 0.6114 0.30000 NaN
## Neg Pred Value 0.76216 0.7391 0.87766 0.9798
## Prevalence 0.27778 0.5707 0.13131 0.0202
## Detection Rate 0.05556 0.5404 0.01515 0.0000
## Detection Prevalence 0.06566 0.8838 0.05051 0.0000
## Balanced Accuracy 0.59301 0.5735 0.53734 0.5000#odds ratio
data.frame(coef(model_pendapatan), exp(coef(model_pendapatan)))## coef.model_pendapatan. exp.coef.model_pendapatan..
## Pendidikan1 2.52137511 12.4456991
## Klasifikasi.Daerah1 0.56413644 1.7579291
## Jenis.Kelamin1 0.54821922 1.7301692
## Status.KRT1 0.64226197 1.9007755
## Usia -0.03221301 0.9683003
## Status.Perkawinan1 0.53005786 1.6990306