Integrasi Data
Maulia Savana Putri
3/28/2022
#Tujuan 1
Memprediksi nilai variabel dari unit sampel Sakernas Agustus 2019 yang tidak diobservasi di Sakernas Februari 2020 menggunakan Mass Imputation.
##1. Import Library
library(survey)
library(readxl)
library(caret)
library(dplyr)
library(stringr)##2. Load Data
sakernas2019_diy <- read_excel("sak0819(edit B5_R17A).xlsx")
sakernas2020_diy <- read_excel("sak0220(edit B5_R17A).xlsx")##3. Membuat Variabel PSU, SSU, Strata di Sakernas 2019 dan Sakernas 2020
sakernas2020_diy['KODE_KAB']=str_pad(sakernas2020_diy$KODE_KAB, width = 2, side = 'left', pad = '0')
sakernas2020_diy['NO_DSRT']=str_pad(sakernas2020_diy$NO_DSRT, width = 2, side = 'left', pad = '0')
sakernas2019_diy['KODE_KAB']=str_pad(sakernas2019_diy$KODE_KAB, width = 2, side = 'left', pad = '0')
sakernas2019_diy['NO_DSRT']=str_pad(sakernas2019_diy$NO_DSRT, width = 2, side = 'left', pad = '0')
sakernas2020_diy["psu"]=paste(sakernas2020_diy$KODE_PROV, sakernas2020_diy$KODE_KAB, sakernas2020_diy$nks_ok, sep = "")
sakernas2019_diy["psu"]=paste(sakernas2019_diy$KODE_PROV, sakernas2019_diy$KODE_KAB, sakernas2019_diy$nks_ok, sep = "")
sakernas2020_diy["ssu"]=paste(sakernas2020_diy$KODE_PROV, sakernas2020_diy$KODE_KAB, sakernas2020_diy$nks_ok, sakernas2020_diy$NO_DSRT, sep = "")
sakernas2019_diy["ssu"]=paste(sakernas2019_diy$KODE_PROV, sakernas2019_diy$KODE_KAB, sakernas2019_diy$nks_ok, sakernas2019_diy$NO_DSRT, sep = "")
sakernas2020_diy["strata"]=paste(sakernas2020_diy$KODE_PROV, sakernas2020_diy$KODE_KAB, sakernas2020_diy$KLASIFIKAS, sep = "")
sakernas2019_diy["strata"]=paste(sakernas2019_diy$KODE_PROV, sakernas2019_diy$KODE_KAB, sakernas2019_diy$KLASIFIKAS, sep = "")##4. Mengubah Tipe Data Variabel Sakernas 2019
sakernas2019_diy$B4_K6=as.factor(sakernas2019_diy$B4_K6)
sakernas2019_diy$B4_K9=as.factor(sakernas2019_diy$B4_K9)
sakernas2019_diy$B4_K10=as.factor(sakernas2019_diy$B4_K10)
sakernas2019_diy$B5_R1A=as.factor(sakernas2019_diy$B5_R1A)
sakernas2019_diy$B5_R4A=as.factor(sakernas2019_diy$B5_R4A)
sakernas2019_diy$B5_R4B=as.factor(sakernas2019_diy$B5_R4B)
sakernas2019_diy$B5_R4C=as.factor(sakernas2019_diy$B5_R4C)
sakernas2019_diy$B5_R4D=as.factor(sakernas2019_diy$B5_R4D)
sakernas2019_diy$B5_R4E=as.factor(sakernas2019_diy$B5_R4E)
sakernas2019_diy$B5_R4F=as.factor(sakernas2019_diy$B5_R4F)
sakernas2019_diy$B5_R5A1=as.factor(sakernas2019_diy$B5_R5A1)
sakernas2019_diy$B5_R5A2=as.factor(sakernas2019_diy$B5_R5A2)
sakernas2019_diy$B5_R5A3=as.factor(sakernas2019_diy$B5_R5A3)
sakernas2019_diy$B5_R5A4=as.factor(sakernas2019_diy$B5_R5A4)
sakernas2019_diy$B5_R5B=as.factor(sakernas2019_diy$B5_R5B)
sakernas2019_diy$B5_R6=as.factor(sakernas2019_diy$B5_R6)
sakernas2019_diy$B5_R20_KAT=as.factor(sakernas2019_diy$B5_R20_KAT)
sakernas2019_diy$B5_R24A=as.factor(sakernas2019_diy$B5_R24A)
sakernas2019_diy$B5_R30A=as.factor(sakernas2019_diy$B5_R30A)
sakernas2019_diy$B5_R1F=as.factor(sakernas2019_diy$B5_R1F)
sakernas2019_diy$B5_R6=as.factor(sakernas2019_diy$B5_R6)
sakernas2019_diy$B5_R7A=as.factor(sakernas2019_diy$B5_R7A)
sakernas2019_diy$B5_R7B=as.factor(sakernas2019_diy$B5_R7B)
sakernas2019_diy$B5_R9=as.factor(sakernas2019_diy$B5_R9)
sakernas2019_diy$B5_R10=as.factor(sakernas2019_diy$B5_R10)
sakernas2019_diy$B5_R12A=as.factor(sakernas2019_diy$B5_R12A)
sakernas2019_diy$B5_R12B=as.factor(sakernas2019_diy$B5_R12B)
sakernas2019_diy$B5_R13A=as.factor(sakernas2019_diy$B5_R13A)
sakernas2019_diy$B5_R13B=as.factor(sakernas2019_diy$B5_R13B)
sakernas2019_diy$B5_R17A=as.factor(sakernas2019_diy$B5_R17A)
sakernas2019_diy$B5_R17B=as.factor(sakernas2019_diy$B5_R17B)
sakernas2019_diy$B5_R21_KJI=as.factor(sakernas2019_diy$B5_R21_KJI)
sakernas2019_diy$B5_R21_KBJ=as.factor(sakernas2019_diy$B5_R21_KBJ)
sakernas2019_diy$B5_R22A=as.factor(sakernas2019_diy$B5_R22A)
sakernas2019_diy$B5_R24B=as.factor(sakernas2019_diy$B5_R24B)
sakernas2019_diy$B5_R24C1=as.factor(sakernas2019_diy$B5_R24C1)
sakernas2019_diy$B5_R24C2=as.factor(sakernas2019_diy$B5_R24C2)
sakernas2019_diy$B5_R24D=as.factor(sakernas2019_diy$B5_R24D)
sakernas2019_diy$B5_R25A1=as.factor(sakernas2019_diy$B5_R25A1)
sakernas2019_diy$B5_R25A2=as.factor(sakernas2019_diy$B5_R25A2)
sakernas2019_diy$B5_R25A3=as.factor(sakernas2019_diy$B5_R25A3)
sakernas2019_diy$B5_R25B=as.factor(sakernas2019_diy$B5_R25B)
sakernas2019_diy$B5_R25C1=as.factor(sakernas2019_diy$B5_R25C1)
sakernas2019_diy$B5_R25C2=as.factor(sakernas2019_diy$B5_R25C2)
sakernas2019_diy$B5_R25C3=as.factor(sakernas2019_diy$B5_R25C3)
sakernas2019_diy$B5_R25C4=as.factor(sakernas2019_diy$B5_R25C4)
sakernas2019_diy$B5_R25C5=as.factor(sakernas2019_diy$B5_R25C5)
sakernas2019_diy$B5_R26=as.factor(sakernas2019_diy$B5_R26)
sakernas2019_diy$B5_R27=as.factor(sakernas2019_diy$B5_R27)
sakernas2019_diy$B5_R29=as.factor(sakernas2019_diy$B5_R29)
sakernas2019_diy$B5_R30A=as.factor(sakernas2019_diy$B5_R30A)
sakernas2019_diy$B5_R30B=as.factor(sakernas2019_diy$B5_R30B)
sakernas2019_diy$B5_R30C=as.factor(sakernas2019_diy$B5_R30C)
sakernas2019_diy$B5_R30D=as.factor(sakernas2019_diy$B5_R30D)
sakernas2019_diy$B5_R30E=as.factor(sakernas2019_diy$B5_R30E)
sakernas2019_diy$B5_R30F=as.factor(sakernas2019_diy$B5_R30F)
sakernas2019_diy$B5_R31=as.factor(sakernas2019_diy$B5_R31)
sakernas2019_diy$B5_R34=as.factor(sakernas2019_diy$B5_R34)
sakernas2019_diy$B5_R36E=as.factor(sakernas2019_diy$B5_R36E)
sakernas2019_diy$B5_R47=as.factor(sakernas2019_diy$B5_R47)
sakernas2019_diy$B5_R48=as.factor(sakernas2019_diy$B5_R48)##5. Mengubah Tipe Data Sakernas 2020
sakernas2020_diy$B4_K6=as.factor(sakernas2020_diy$B4_K6)
sakernas2020_diy$B4_K9=as.factor(sakernas2020_diy$B4_K9)
sakernas2020_diy$B4_K10=as.factor(sakernas2020_diy$B4_K10)
sakernas2020_diy$B5_R1A=as.factor(sakernas2020_diy$B5_R1A)
sakernas2020_diy$B5_R4A=as.factor(sakernas2020_diy$B5_R4A)
sakernas2020_diy$B5_R4B=as.factor(sakernas2020_diy$B5_R4B)
sakernas2020_diy$B5_R4C=as.factor(sakernas2020_diy$B5_R4C)
sakernas2020_diy$B5_R4D=as.factor(sakernas2020_diy$B5_R4D)
sakernas2020_diy$B5_R4E=as.factor(sakernas2020_diy$B5_R4E)
sakernas2020_diy$B5_R4F=as.factor(sakernas2020_diy$B5_R4F)
sakernas2020_diy$B5_R5A1=as.factor(sakernas2020_diy$B5_R5A1)
sakernas2020_diy$B5_R5A2=as.factor(sakernas2020_diy$B5_R5A2)
sakernas2020_diy$B5_R5A3=as.factor(sakernas2020_diy$B5_R5A3)
sakernas2020_diy$B5_R5A4=as.factor(sakernas2020_diy$B5_R5A4)
sakernas2020_diy$B5_R5B=as.factor(sakernas2020_diy$B5_R5B)
sakernas2020_diy$B5_R6=as.factor(sakernas2020_diy$B5_R6)
sakernas2020_diy$B5_R20_KAT=as.factor(sakernas2020_diy$B5_R20_KAT)
sakernas2020_diy$B5_R24A=as.factor(sakernas2020_diy$B5_R24A)
sakernas2020_diy$B5_R30A=as.factor(sakernas2020_diy$B5_R30A)
sakernas2020_diy$B5_R1F=as.factor(sakernas2020_diy$B5_R1F)
sakernas2020_diy$B5_R6=as.factor(sakernas2020_diy$B5_R6)
sakernas2020_diy$B5_R7A=as.factor(sakernas2020_diy$B5_R7A)
sakernas2020_diy$B5_R7B=as.factor(sakernas2020_diy$B5_R7B)
sakernas2020_diy$B5_R9=as.factor(sakernas2020_diy$B5_R9)
sakernas2020_diy$B5_R10=as.factor(sakernas2020_diy$B5_R10)
sakernas2020_diy$B5_R12A=as.factor(sakernas2020_diy$B5_R12A)
sakernas2020_diy$B5_R12B=as.factor(sakernas2020_diy$B5_R12B)
sakernas2020_diy$B5_R13A=as.factor(sakernas2020_diy$B5_R13A)
sakernas2020_diy$B5_R13B=as.factor(sakernas2020_diy$B5_R13B)
sakernas2020_diy$B5_R17A=as.factor(sakernas2020_diy$B5_R17A)
sakernas2020_diy$B5_R17B=as.factor(sakernas2020_diy$B5_R17B)
sakernas2020_diy$B5_R21_KJI=as.factor(sakernas2020_diy$B5_R21_KJI)
sakernas2020_diy$B5_R21_KBJ=as.factor(sakernas2020_diy$B5_R21_KBJ)
sakernas2020_diy$B5_R22A=as.factor(sakernas2020_diy$B5_R22A)
sakernas2020_diy$B5_R24B=as.factor(sakernas2020_diy$B5_R24B)
sakernas2020_diy$B5_R24C1=as.factor(sakernas2020_diy$B5_R24C1)
sakernas2020_diy$B5_R24C2=as.factor(sakernas2020_diy$B5_R24C2)
sakernas2020_diy$B5_R24D=as.factor(sakernas2020_diy$B5_R24D)
sakernas2020_diy$B5_R25A1=as.factor(sakernas2020_diy$B5_R25A1)
sakernas2020_diy$B5_R25A2=as.factor(sakernas2020_diy$B5_R25A2)
sakernas2020_diy$B5_R25A3=as.factor(sakernas2020_diy$B5_R25A3)
sakernas2020_diy$B5_R25B=as.factor(sakernas2020_diy$B5_R25B)
sakernas2020_diy$B5_R25C1=as.factor(sakernas2020_diy$B5_R25C1)
sakernas2020_diy$B5_R25C2=as.factor(sakernas2020_diy$B5_R25C2)
sakernas2020_diy$B5_R25C3=as.factor(sakernas2020_diy$B5_R25C3)
sakernas2020_diy$B5_R25C4=as.factor(sakernas2020_diy$B5_R25C4)
sakernas2020_diy$B5_R25C5=as.factor(sakernas2020_diy$B5_R25C5)
sakernas2020_diy$B5_R26=as.factor(sakernas2020_diy$B5_R26)
sakernas2020_diy$B5_R27=as.factor(sakernas2020_diy$B5_R27)
sakernas2020_diy$B5_R29=as.factor(sakernas2020_diy$B5_R29)
sakernas2020_diy$B5_R30A=as.factor(sakernas2020_diy$B5_R30A)
sakernas2020_diy$B5_R30B=as.factor(sakernas2020_diy$B5_R30B)
sakernas2020_diy$B5_R30C=as.factor(sakernas2020_diy$B5_R30C)
sakernas2020_diy$B5_R30D=as.factor(sakernas2020_diy$B5_R30D)
sakernas2020_diy$B5_R30E=as.factor(sakernas2020_diy$B5_R30E)
sakernas2020_diy$B5_R30F=as.factor(sakernas2020_diy$B5_R30F)
sakernas2020_diy$B5_R31=as.factor(sakernas2020_diy$B5_R31)
sakernas2020_diy$B5_R34=as.factor(sakernas2020_diy$B5_R34)
sakernas2020_diy$B5_R36E=as.factor(sakernas2020_diy$B5_R36E)
sakernas2020_diy$B5_R47=as.factor(sakernas2020_diy$B5_R47)
sakernas2020_diy$B5_R48=as.factor(sakernas2020_diy$B5_R48)##6. Membuat Variabel Jenis Kegiatan Seminggu yang Lalu
sakernas2020_diy$jk=ifelse(sakernas2020_diy$B5_R5A1 == "1" | sakernas2020_diy$B5_R6=="1", 1, ifelse(sakernas2020_diy$B5_R5A1=="2" & sakernas2020_diy$B5_R6=="2" & sakernas2020_diy$B5_R12A=="1" |sakernas2020_diy$B5_R5A1=="2" & sakernas2020_diy$B5_R6=="2" & sakernas2020_diy$B5_R12A=="2" & sakernas2020_diy$B5_R12B=="1" | sakernas2020_diy$B5_R5A1=="2" & sakernas2020_diy$B5_R6=="2" & sakernas2020_diy$B5_R12A=="2" & sakernas2020_diy$B5_R12B=="2" & between(sakernas2020_diy$B5_R17A, 0, 4), 2, ifelse(sakernas2020_diy$B5_R5A1=="2" & sakernas2020_diy$B5_R5B=="2" & sakernas2020_diy$B5_R6=="2" & sakernas2020_diy$B5_R12A=="2" & sakernas2020_diy$B5_R12B=="2" & sakernas2020_diy$B5_R17A=="4", 4, ifelse(sakernas2020_diy$B5_R5A1=="2" & sakernas2020_diy$B5_R5B=="3" & sakernas2020_diy$B5_R6=="2" & sakernas2020_diy$B5_R12A=="2" & sakernas2020_diy$B5_R12B=="2" & sakernas2020_diy$B5_R17A=="4", 5, ifelse(sakernas2020_diy$B5_R5A1=="2" & sakernas2020_diy$B5_R5B=="4" & sakernas2020_diy$B5_R6=="2" & sakernas2020_diy$B5_R12A=="2" & sakernas2020_diy$B5_R12B=="2" & sakernas2020_diy$B5_R17A=="4", 6, 0)))))
sakernas2019_diy$jk=ifelse(sakernas2019_diy$B5_R5A1 == "1" | sakernas2019_diy$B5_R6=="1", 1, ifelse(sakernas2019_diy$B5_R5A1=="2" & sakernas2019_diy$B5_R6=="2" & sakernas2019_diy$B5_R12A=="1" |sakernas2019_diy$B5_R5A1=="2" & sakernas2019_diy$B5_R6=="2" & sakernas2019_diy$B5_R12A=="2" & sakernas2019_diy$B5_R12B=="1" | sakernas2019_diy$B5_R5A1=="2" & sakernas2019_diy$B5_R6=="2" & sakernas2019_diy$B5_R12A=="2" & sakernas2019_diy$B5_R12B=="2" & between(sakernas2019_diy$B5_R17A, 0, 4), 2, ifelse(sakernas2019_diy$B5_R5A1=="2" & sakernas2019_diy$B5_R5B=="2" & sakernas2019_diy$B5_R6=="2" & sakernas2019_diy$B5_R12A=="2" & sakernas2019_diy$B5_R12B=="2" & sakernas2019_diy$B5_R17A=="4", 4, ifelse(sakernas2019_diy$B5_R5A1=="2" & sakernas2019_diy$B5_R5B=="3" & sakernas2019_diy$B5_R6=="2" & sakernas2019_diy$B5_R12A=="2" & sakernas2019_diy$B5_R12B=="2" & sakernas2019_diy$B5_R17A=="4", 5, ifelse(sakernas2019_diy$B5_R5A1=="2" & sakernas2019_diy$B5_R5B=="4" & sakernas2019_diy$B5_R6=="2" & sakernas2019_diy$B5_R12A=="2" & sakernas2019_diy$B5_R12B=="2" & sakernas2019_diy$B5_R17A=="4", 6, 0)))))
table(sakernas2020_diy$jk)
table(sakernas2019_diy$jk)
sakernas2020_diy$jk=as.factor(sakernas2020_diy$jk)
sakernas2019_diy$jk=as.factor(sakernas2019_diy$jk)##
## 1 2 4 5 6
## 1628 51 258 347 93
##
## 1 2 4 5 6
## 6610 207 834 1434 329##7. Identifikasi Sampel yang Diobservasi di Sakernas 2020 dan Sakernas 2019
df_intersect2019 <- sakernas2019_diy[(sakernas2019_diy$id_unik %in% sakernas2020_diy$id_unik), ]
df_intersect2020 <- sakernas2020_diy[(sakernas2020_diy$id_unik %in% sakernas2019_diy$id_unik), ]
dim(df_intersect2020)## [1] 1501 106##8. Identifikasi Sampel yang Diobservasi di Sakernas 2020 saja dan Sakernas 2019 saja
df_predict <- sakernas2019_diy[!(sakernas2019_diy$id_unik %in% sakernas2020_diy$id_unik), ]
df_test <- sakernas2020_diy[!(sakernas2020_diy$id_unik %in% sakernas2019_diy$id_unik), ]
dim(df_predict) #Diobservasi di Sakernas 2019 saja
dim(df_test) #Diobservasi di Sakernas 2019 saja## [1] 7913 103
## [1] 876 106##9. Merge Sampel yang Diobservasi di Sakernas 2020 dan Sakernas 2019
sak = merge(x= df_intersect2019, y =df_intersect2020, by = "id_unik")
dim(sak)
set.seed(1)
Train <- createDataPartition(sak$B5_R12A.x, p=0.8, list=FALSE)
training <- sak[Train, ]
testing <- sak[-Train, ]## [1] 1501 208##10. Mengubah Nama Variabel Supaya dapat Diolah di Pemodelan
df_predict$B2_R1.x=df_predict$B2_R1
df_predict$B2_R2.x=df_predict$B2_R2
df_predict$B4_K3.x=df_predict$B4_K3
df_predict$B4_K6.x=df_predict$B4_K6
df_predict$B4_K8.x=df_predict$B4_K8
df_predict$B4_K9.x=df_predict$B4_K9
df_predict$B4_K10.x=df_predict$B4_K10
df_predict$B5_R4A.x=df_predict$B5_R4A
df_predict$B5_R4B.x=df_predict$B5_R4B
df_predict$B5_R4C.x=df_predict$B5_R4C
df_predict$B5_R4D.x=df_predict$B5_R4D
df_predict$B5_R4E.x=df_predict$B5_R4E
df_predict$B5_R5A1.x=df_predict$B5_R5A1
df_predict$B5_R5A2.x=df_predict$B5_R5A2
df_predict$B5_R5A3.x=df_predict$B5_R5A3
df_predict$B5_R5A4.x=df_predict$B5_R5A4
df_predict$B5_R5B.x=df_predict$B5_R5B
df_predict$B5_R6.x=df_predict$B5_R6
df_predict$B5_R12A.x=df_predict$B5_R12A
df_predict$B5_R12B.x=df_predict$B5_R12B
df_predict$B5_R17A.x=df_predict$B5_R17A
df_predict$B5_R20_KAT.x=df_predict$B5_R20_KAT
df_predict$jk.x=df_predict$jk
df_predict$jkeg.x=df_predict$jkeg
df_predict$B5_R24A.x=df_predict$B5_R24A
df_predict$B5_R28A1.x=df_predict$B5_R28A1
df_predict$B5_R28B1.x=df_predict$B5_R28B1
df_predict$B5_R28B2.x=df_predict$B5_R28B2
df_predict$B5_R28C1.x=df_predict$B5_R28C1
df_predict$B5_R28C2.x=df_predict$B5_R28C2
df_predict$FINAL_WEIG.x=df_predict$FINAL_WEIG##11. Setting Desain Survei
options(survey.lonely.psu = "adjust")
des<-svydesign(ids=~psu.y+ssu.y, strata=~strata.y, weights=~FINAL_WEIG.y, data = training)##12. Pemodelan Variabel Bekerja Minimal 1 Jam Tanpa Terputus Seminggu yang Lalu
fit.logit6 <- svyglm(B5_R5A1.y~B2_R1.x+B2_R2.x+B4_K3.x+B4_K6.x+B4_K8.x
+B4_K9.x+B4_K10.x+B5_R4A.x+B5_R4B.x+B5_R4C.x
+B5_R4D.x+B5_R4E.x+B5_R5A1.x+B5_R5A2.x+B5_R5A3.x
+B5_R5A4.x+B5_R5B.x+B5_R20_KAT.x,
design=des,family=binomial)
summary(fit.logit6)
predict = predict(fit.logit6, newdata = testing, type = "response")
predict <- cut(predict, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
log_conf <- confusionMatrix(predict, testing$B5_R5A1.y)
log_conf
B5_R5A1 = predict(fit.logit6, newdata = df_predict, type = "response")
B5_R5A1 = as.data.frame(B5_R5A1)
B5_R5A1 = B5_R5A1$response
df_predict['B5_R5A1.y'] <- cut(B5_R5A1, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
summary(df_predict$B5_R5A1.y)
summary(df_predict$B5_R5A1.x)##
## Call:
## svyglm(formula = B5_R5A1.y ~ B2_R1.x + B2_R2.x + B4_K3.x + B4_K6.x +
## B4_K8.x + B4_K9.x + B4_K10.x + B5_R4A.x + B5_R4B.x + B5_R4C.x +
## B5_R4D.x + B5_R4E.x + B5_R5A1.x + B5_R5A2.x + B5_R5A3.x +
## B5_R5A4.x + B5_R5B.x + B5_R20_KAT.x, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~psu.y + ssu.y, strata = ~strata.y, weights = ~FINAL_WEIG.y,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.576679724 1.566320151 1.007 0.326769
## B2_R1.x 0.115662007 0.203145120 0.569 0.575784
## B2_R2.x -0.301365195 0.182855079 -1.648 0.115768
## B4_K3.x 0.231261430 0.069235923 3.340 0.003439 **
## B4_K6.x2 0.289793431 0.221268792 1.310 0.205917
## B4_K8.x 0.012268954 0.008502926 1.443 0.165330
## B4_K9.x2 -0.081281964 1.101536208 -0.074 0.941949
## B4_K9.x3 -1.194954395 0.579298336 -2.063 0.053075 .
## B4_K10.x2 -0.036138486 0.442830691 -0.082 0.935812
## B4_K10.x3 -0.625789847 0.633214025 -0.988 0.335437
## B4_K10.x4 -0.589954903 0.547919066 -1.077 0.295088
## B5_R4A.x2 -0.818732173 0.497397817 -1.646 0.116199
## B5_R4A.x3 13.964839386 1.212612130 11.516 0.0000000005173919 ***
## B5_R4B.x5 0.000002265 0.710880344 0.000 0.999997
## B5_R4B.x6 2.265728577 1.364626406 1.660 0.113262
## B5_R4C.x2 1.631253609 0.674967984 2.417 0.025884 *
## B5_R4C.x3 15.739005707 1.241864974 12.674 0.0000000001024353 ***
## B5_R4D.x5 14.120168676 0.753867882 18.730 0.0000000000001046 ***
## B5_R4D.x6 0.667324555 1.492176269 0.447 0.659776
## B5_R4E.x2 1.167460001 0.752860882 1.551 0.137471
## B5_R4E.x3 -0.874596869 1.124801362 -0.778 0.446405
## B5_R5A1.x2 -0.993629606 0.641183145 -1.550 0.137715
## B5_R5A2.x4 -0.373478162 1.164985508 -0.321 0.752023
## B5_R5A3.x2 -0.492286449 0.416000034 -1.183 0.251254
## B5_R5A4.x4 -0.015694166 0.287585830 -0.055 0.957049
## B5_R5B.x2 2.244643929 0.836125491 2.685 0.014669 *
## B5_R5B.x3 1.734484610 0.293912726 5.901 0.0000110774392183 ***
## B5_R5B.x4 1.466172482 0.635133990 2.308 0.032387 *
## B5_R20_KAT.x1 -3.393921028 0.644077188 -5.269 0.0000437082271855 ***
## B5_R20_KAT.x2 -17.358608347 0.800006492 -21.698 0.0000000000000072 ***
## B5_R20_KAT.x3 -2.733042507 0.716317588 -3.815 0.001168 **
## B5_R20_KAT.x4 -18.128030929 1.218858054 -14.873 0.0000000000063917 ***
## B5_R20_KAT.x5 -17.281268426 1.221168485 -14.151 0.0000000000152611 ***
## B5_R20_KAT.x6 -2.653975279 0.798271296 -3.325 0.003562 **
## B5_R20_KAT.x7 -2.652145116 0.714836134 -3.710 0.001485 **
## B5_R20_KAT.x8 -2.133059124 0.756786262 -2.819 0.010970 *
## B5_R20_KAT.x9 -3.881251764 0.779526525 -4.979 0.0000833205769088 ***
## B5_R20_KAT.x10 -2.701630560 1.172336956 -2.304 0.032651 *
## B5_R20_KAT.x11 -2.611079591 0.908371361 -2.874 0.009708 **
## B5_R20_KAT.x12 0.239340573 1.259984548 0.190 0.851359
## B5_R20_KAT.x13 -18.821876263 1.070214211 -17.587 0.0000000000003251 ***
## B5_R20_KAT.x14 -4.217814555 1.233929857 -3.418 0.002883 **
## B5_R20_KAT.x15 -3.986642881 0.918101676 -4.342 0.000351 ***
## B5_R20_KAT.x16 -18.147612325 0.691822650 -26.232 < 0.0000000000000002 ***
## B5_R20_KAT.x17 -3.938405487 0.786775950 -5.006 0.0000784852775388 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.9582358)
##
## Number of Fisher Scoring iterations: 16
##
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2
## 1 198 23
## 2 14 64
##
## Accuracy : 0.8763
## 95% CI : (0.8335, 0.9114)
## No Information Rate : 0.709
## P-Value [Acc > NIR] : 0.000000000004647
##
## Kappa : 0.6907
##
## Mcnemar's Test P-Value : 0.1884
##
## Sensitivity : 0.9340
## Specificity : 0.7356
## Pos Pred Value : 0.8959
## Neg Pred Value : 0.8205
## Prevalence : 0.7090
## Detection Rate : 0.6622
## Detection Prevalence : 0.7391
## Balanced Accuracy : 0.8348
##
## 'Positive' Class : 1
##
## 1 2
## 5441 2472
## 1 2
## 5375 2538log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("2", "1"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##13. Pemodelan Variabel Kegiatan dengan Waktu Terbanyak Selama Seminggu yang Lalu
fit.logit7 <- svyglm(B5_R5B.y~B2_R1.x+B2_R2.x+B4_K3.x+B4_K6.x+B4_K8.x
+B4_K9.x+B4_K10.x+B5_R4A.x+B5_R4B.x+B5_R4C.x
+B5_R4D.x+B5_R4E.x+B5_R5A2.x+B5_R5A3.x
+B5_R5A4.x+B5_R5B.x+B5_R20_KAT.x+B5_R5A1.y+B5_R5A1.x,
design=des,family=binomial)
summary(fit.logit7)
predict = predict(fit.logit7, newdata = testing, type = "response")
predict <- cut(predict, breaks = c(0, 0.25, 0.5, 0.75, 1), labels = c(1, 2, 3, 4), right = TRUE)
log_conf <- confusionMatrix(predict, testing$B5_R5B.y)
log_conf
B5_R5B = predict(fit.logit7, newdata = df_predict, type = "response")
B5_R5B = as.data.frame(B5_R5B)
B5_R5B = B5_R5B$response
df_predict['B5_R5B.y'] <- cut(B5_R5B, breaks = c(0, 0.25, 0.5, 0.75, 1), labels = c(1, 2, 3, 4), right = TRUE)
summary(df_predict$B5_R5B.y)
summary(df_predict$B5_R5B.x)##
## Call:
## svyglm(formula = B5_R5B.y ~ B2_R1.x + B2_R2.x + B4_K3.x + B4_K6.x +
## B4_K8.x + B4_K9.x + B4_K10.x + B5_R4A.x + B5_R4B.x + B5_R4C.x +
## B5_R4D.x + B5_R4E.x + B5_R5A2.x + B5_R5A3.x + B5_R5A4.x +
## B5_R5B.x + B5_R20_KAT.x + B5_R5A1.y + B5_R5A1.x, design = des,
## family = binomial)
##
## Survey design:
## svydesign(ids = ~psu.y + ssu.y, strata = ~strata.y, weights = ~FINAL_WEIG.y,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.42932 2.20145 10.643 0.00000000339605962 ***
## B2_R1.x -0.36745 0.33192 -1.107 0.282857
## B2_R2.x 0.29967 0.35943 0.834 0.415352
## B4_K3.x 0.12348 0.12206 1.012 0.325153
## B4_K6.x2 0.30816 0.29392 1.048 0.308309
## B4_K8.x 0.01238 0.01680 0.736 0.470932
## B4_K9.x2 -23.38619 1.34541 -17.382 0.00000000000106891 ***
## B4_K9.x3 1.54299 1.00845 1.530 0.143387
## B4_K10.x2 0.91546 0.72842 1.257 0.224902
## B4_K10.x3 0.67594 1.28151 0.527 0.604317
## B4_K10.x4 1.09324 0.77020 1.419 0.172860
## B5_R4A.x2 0.46648 0.52404 0.890 0.385125
## B5_R4A.x3 -22.28674 2.24666 -9.920 0.00000001010557071 ***
## B5_R4B.x5 -16.16098 0.87755 -18.416 0.00000000000039825 ***
## B5_R4B.x6 -9.91775 2.41287 -4.110 0.000657 ***
## B5_R4C.x2 -12.89852 1.00766 -12.801 0.00000000017722028 ***
## B5_R4C.x3 -5.67939 2.49482 -2.276 0.035265 *
## B5_R4D.x5 2.83309 1.72630 1.641 0.118126
## B5_R4D.x6 -11.26317 2.88884 -3.899 0.001052 **
## B5_R4E.x2 -15.02835 0.89840 -16.728 0.00000000000205220 ***
## B5_R4E.x3 -19.09168 1.29191 -14.778 0.00000000001654144 ***
## B5_R5A2.x4 -29.18260 1.23976 -23.539 0.00000000000000569 ***
## B5_R5A3.x2 -0.24319 0.66255 -0.367 0.717855
## B5_R5A4.x4 0.78214 0.55366 1.413 0.174806
## B5_R5B.x2 0.66084 1.20273 0.549 0.589450
## B5_R5B.x3 2.63726 0.47953 5.500 0.00003189723722048 ***
## B5_R5B.x4 1.94533 0.58775 3.310 0.003896 **
## B5_R20_KAT.x1 -0.85936 1.22360 -0.702 0.491465
## B5_R20_KAT.x2 -19.47853 1.23231 -15.806 0.00000000000534988 ***
## B5_R20_KAT.x3 -0.17139 1.13704 -0.151 0.881865
## B5_R20_KAT.x4 -18.94247 1.74644 -10.846 0.00000000252244444 ***
## B5_R20_KAT.x5 -19.61910 1.53016 -12.822 0.00000000017253413 ***
## B5_R20_KAT.x6 -1.13800 1.27040 -0.896 0.382193
## B5_R20_KAT.x7 -1.03955 1.39246 -0.747 0.464973
## B5_R20_KAT.x8 -22.19883 0.92707 -23.945 0.00000000000000422 ***
## B5_R20_KAT.x9 -1.65414 1.13119 -1.462 0.160900
## B5_R20_KAT.x10 -22.42211 1.53813 -14.578 0.00000000002077011 ***
## B5_R20_KAT.x11 -20.31590 1.30562 -15.560 0.00000000000696761 ***
## B5_R20_KAT.x12 -31.30079 2.90617 -10.770 0.00000000281649184 ***
## B5_R20_KAT.x13 -21.77935 1.66680 -13.067 0.00000000012664187 ***
## B5_R20_KAT.x14 -0.47491 1.29680 -0.366 0.718472
## B5_R20_KAT.x15 -2.28098 1.42553 -1.600 0.126983
## B5_R20_KAT.x16 -19.91729 1.12673 -17.677 0.00000000000080228 ***
## B5_R20_KAT.x17 -0.36300 1.16595 -0.311 0.759121
## B5_R5A1.y2 62.00238 2.12713 29.148 < 0.0000000000000002 ***
## B5_R5A1.x2 -1.36789 1.10298 -1.240 0.230835
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.5695314)
##
## Number of Fisher Scoring iterations: 21
##
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4
## 1 188 0 5 0
## 2 12 1 3 0
## 3 2 0 1 0
## 4 0 28 38 21
##
## Overall Statistics
##
## Accuracy : 0.7057
## 95% CI : (0.6505, 0.7567)
## No Information Rate : 0.6756
## P-Value [Acc > NIR] : 0.1467
##
## Kappa : 0.4516
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4
## Sensitivity 0.9307 0.034483 0.021277 1.00000
## Specificity 0.9485 0.944444 0.992063 0.76259
## Pos Pred Value 0.9741 0.062500 0.333333 0.24138
## Neg Pred Value 0.8679 0.901060 0.844595 1.00000
## Prevalence 0.6756 0.096990 0.157191 0.07023
## Detection Rate 0.6288 0.003344 0.003344 0.07023
## Detection Prevalence 0.6455 0.053512 0.010033 0.29097
## Balanced Accuracy 0.9396 0.489464 0.506670 0.88129
## 1 2 3 4
## 5058 307 69 2479
## 1 2 3 4
## 4739 782 1985 407log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("4", "3", "2", "1"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##14. Pemodelan Variabel Bekerja Minimal 1 Jam Tanpa Terputus dalam Seminggu tetapi Saat Ini Sementara Tidak Bekerja
fit.logit5 <- svyglm(B5_R6.y~B2_R1.x+B2_R2.x+B4_K3.x+B4_K6.x+B4_K8.x
+B4_K9.x+B4_K10.x+B5_R4A.x+B5_R4B.x+B5_R4C.x
+B5_R4D.x+B5_R4E.x+B5_R5A2.x+B5_R5A3.x
+B5_R5A4.x+B5_R5B.x+B5_R20_KAT.x+B5_R6.x,
design=des,family=binomial)
summary(fit.logit5)
predict = predict(fit.logit5, newdata = testing, type = "response")
predict <- cut(predict, breaks = c(0, 0.3333, 0.6666, 1), labels = c(0, 1, 2), right = TRUE)
log_conf <- confusionMatrix(predict, testing$B5_R6.y)
log_conf
B5_R6 = predict(fit.logit5, newdata = df_predict, type = "response")
B5_R6 = as.data.frame(B5_R6)
B5_R6 = B5_R6$response
df_predict['B5_R6.y'] <- cut(B5_R6, breaks = c(0, 0.3333, 0.6666, 1), labels = c(0, 1, 2), right = TRUE)
summary(df_predict$B5_R6.y)
summary(df_predict$B5_R6.x)##
## Call:
## svyglm(formula = B5_R6.y ~ B2_R1.x + B2_R2.x + B4_K3.x + B4_K6.x +
## B4_K8.x + B4_K9.x + B4_K10.x + B5_R4A.x + B5_R4B.x + B5_R4C.x +
## B5_R4D.x + B5_R4E.x + B5_R5A2.x + B5_R5A3.x + B5_R5A4.x +
## B5_R5B.x + B5_R20_KAT.x + B5_R6.x, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~psu.y + ssu.y, strata = ~strata.y, weights = ~FINAL_WEIG.y,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.178187 1.682428 -0.106 0.91682
## B2_R1.x 0.110702 0.206580 0.536 0.59860
## B2_R2.x -0.291318 0.184488 -1.579 0.13173
## B4_K3.x 0.236857 0.072245 3.279 0.00417 **
## B4_K6.x2 0.287637 0.222127 1.295 0.21171
## B4_K8.x 0.014075 0.008542 1.648 0.11678
## B4_K9.x2 -0.030391 1.121471 -0.027 0.97868
## B4_K9.x3 -1.149767 0.596695 -1.927 0.06993 .
## B4_K10.x2 -0.106387 0.439060 -0.242 0.81128
## B4_K10.x3 -0.661164 0.620704 -1.065 0.30087
## B4_K10.x4 -0.619510 0.541902 -1.143 0.26793
## B5_R4A.x2 -0.832964 0.503951 -1.653 0.11569
## B5_R4A.x3 13.947686 1.224408 11.391 0.000000001161093 ***
## B5_R4B.x5 -0.049033 0.720191 -0.068 0.94647
## B5_R4B.x6 2.195894 1.374839 1.597 0.12763
## B5_R4C.x2 1.592814 0.681349 2.338 0.03115 *
## B5_R4C.x3 15.680841 1.243578 12.609 0.000000000226394 ***
## B5_R4D.x5 14.136699 0.760055 18.600 0.000000000000336 ***
## B5_R4D.x6 0.676075 1.489663 0.454 0.65537
## B5_R4E.x2 1.209949 0.747730 1.618 0.12302
## B5_R4E.x3 -0.977262 1.147564 -0.852 0.40563
## B5_R5A2.x4 -0.374244 1.180020 -0.317 0.75478
## B5_R5A3.x2 -0.481435 0.417146 -1.154 0.26355
## B5_R5A4.x4 0.006523 0.290883 0.022 0.98236
## B5_R5B.x2 2.179771 0.843134 2.585 0.01867 *
## B5_R5B.x3 1.739528 0.291498 5.968 0.000012045243506 ***
## B5_R5B.x4 1.467604 0.636057 2.307 0.03313 *
## B5_R20_KAT.x1 -1.780212 0.685954 -2.595 0.01828 *
## B5_R20_KAT.x2 -15.682806 0.848424 -18.485 0.000000000000374 ***
## B5_R20_KAT.x3 -1.062644 0.778606 -1.365 0.18913
## B5_R20_KAT.x4 -16.492967 1.236348 -13.340 0.000000000090162 ***
## B5_R20_KAT.x5 -15.621556 1.258897 -12.409 0.000000000293696 ***
## B5_R20_KAT.x6 -0.992555 0.775647 -1.280 0.21692
## B5_R20_KAT.x7 -1.042617 0.753271 -1.384 0.18324
## B5_R20_KAT.x8 -0.330004 0.897178 -0.368 0.71729
## B5_R20_KAT.x9 -2.110609 0.968865 -2.178 0.04291 *
## B5_R20_KAT.x10 -1.075833 1.212516 -0.887 0.38663
## B5_R20_KAT.x11 -0.952972 0.871833 -1.093 0.28878
## B5_R20_KAT.x12 1.871161 1.465843 1.277 0.21800
## B5_R20_KAT.x13 -17.112756 1.105256 -15.483 0.000000000007576 ***
## B5_R20_KAT.x14 -2.517442 1.284465 -1.960 0.06567 .
## B5_R20_KAT.x15 -2.462408 0.928195 -2.653 0.01619 *
## B5_R20_KAT.x16 -16.361735 0.828911 -19.739 0.000000000000121 ***
## B5_R20_KAT.x17 -2.216041 0.727662 -3.045 0.00696 **
## B5_R6.x1 -16.457042 0.462164 -35.609 < 0.0000000000000002 ***
## B5_R6.x2 0.659328 0.712363 0.926 0.36692
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.9405483)
##
## Number of Fisher Scoring iterations: 16
##
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1 2
## 0 189 4 15
## 1 11 2 7
## 2 12 1 58
##
## Overall Statistics
##
## Accuracy : 0.8328
## 95% CI : (0.7856, 0.8733)
## No Information Rate : 0.709
## P-Value [Acc > NIR] : 0.0000005053
##
## Kappa : 0.6214
##
## Mcnemar's Test P-Value : 0.04399
##
## Statistics by Class:
##
## Class: 0 Class: 1 Class: 2
## Sensitivity 0.8915 0.285714 0.7250
## Specificity 0.7816 0.938356 0.9406
## Pos Pred Value 0.9087 0.100000 0.8169
## Neg Pred Value 0.7473 0.982079 0.9035
## Prevalence 0.7090 0.023411 0.2676
## Detection Rate 0.6321 0.006689 0.1940
## Detection Prevalence 0.6957 0.066890 0.2375
## Balanced Accuracy 0.8366 0.612035 0.8328
## 0 1 2
## 5124 518 2271
## 0 1 2
## 5375 157 2381log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("2", "1", "0"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##15. Pemodelan Variabel Aktif Mencari Pekerjaan Seminggu yang Lalu
fit.logit1 <- svyglm(B5_R12A.y~B2_R1.x+B2_R2.x+B4_K3.x+B4_K6.x+B4_K8.x
+B4_K9.x+B4_K10.x+B5_R4A.x+B5_R4B.x+B5_R4C.x
+B5_R4D.x+B5_R4E.x+B5_R5A1.x+B5_R5A2.x+B5_R5A3.x
+B5_R5A4.x+B5_R5B.x+B5_R12A.x+B5_R20_KAT.x+B5_R5A1.y+B5_R5B.y,
design=des,family=binomial)
summary(fit.logit1)
predict = predict(fit.logit1, newdata = testing, type = "response")
predict <- factor(ifelse(predict > 0.5, "2","1"))
log_conf <- confusionMatrix(predict, testing$B5_R12A.y, positive = "2")
log_conf
B5_R12A = predict(fit.logit1, newdata = df_predict, type = "response")
B5_R12A = as.data.frame(B5_R12A)
B5_R12A = B5_R12A$response
df_predict['B5_R12A.y'] <- factor(ifelse(B5_R12A > 0.5, "2","1"))
summary(df_predict$B5_R12A.y)
summary(df_predict$B5_R12A.x)##
## Call:
## svyglm(formula = B5_R12A.y ~ B2_R1.x + B2_R2.x + B4_K3.x + B4_K6.x +
## B4_K8.x + B4_K9.x + B4_K10.x + B5_R4A.x + B5_R4B.x + B5_R4C.x +
## B5_R4D.x + B5_R4E.x + B5_R5A1.x + B5_R5A2.x + B5_R5A3.x +
## B5_R5A4.x + B5_R5B.x + B5_R12A.x + B5_R20_KAT.x + B5_R5A1.y +
## B5_R5B.y, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~psu.y + ssu.y, strata = ~strata.y, weights = ~FINAL_WEIG.y,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -18.71471 3.29673 -5.677 0.000057151461614 ***
## B2_R1.x -0.03415 0.59180 -0.058 0.95480
## B2_R2.x 0.19846 0.67186 0.295 0.77203
## B4_K3.x 0.23453 0.12806 1.831 0.08841 .
## B4_K6.x2 0.18631 0.66096 0.282 0.78217
## B4_K8.x 0.06968 0.02577 2.704 0.01711 *
## B4_K9.x2 1.55424 1.74242 0.892 0.38747
## B4_K9.x3 -19.01386 1.54526 -12.305 0.000000006775027 ***
## B4_K10.x2 2.11050 0.73683 2.864 0.01249 *
## B4_K10.x3 19.34853 1.11595 17.338 0.000000000073831 ***
## B4_K10.x4 18.11882 1.83792 9.858 0.000000111436339 ***
## B5_R4A.x2 -1.66850 0.73417 -2.273 0.03934 *
## B5_R4A.x3 2.59078 1.83360 1.413 0.17952
## B5_R4B.x5 15.88181 1.31856 12.045 0.000000008915781 ***
## B5_R4B.x6 -4.78051 2.14719 -2.226 0.04292 *
## B5_R4C.x2 17.27382 0.97605 17.698 0.000000000056043 ***
## B5_R4C.x3 18.29902 1.42101 12.877 0.000000003761404 ***
## B5_R4D.x5 0.11879 1.47121 0.081 0.93679
## B5_R4D.x6 0.17834 1.57985 0.113 0.91172
## B5_R4E.x2 15.35583 1.24065 12.377 0.000000006280237 ***
## B5_R4E.x3 18.38214 1.27606 14.405 0.000000000868069 ***
## B5_R5A1.x2 15.36659 1.51901 10.116 0.000000080947010 ***
## B5_R5A2.x4 19.93713 1.31802 15.127 0.000000000455385 ***
## B5_R5A3.x2 0.01391 0.72609 0.019 0.98498
## B5_R5A4.x4 0.21077 0.49618 0.425 0.67745
## B5_R5B.x2 3.73577 1.46038 2.558 0.02276 *
## B5_R5B.x3 1.78136 0.81658 2.181 0.04669 *
## B5_R5B.x4 0.04877 1.21460 0.040 0.96854
## B5_R12A.x2 0.13032 0.74477 0.175 0.86361
## B5_R20_KAT.x1 33.17442 1.37933 24.051 0.000000000000871 ***
## B5_R20_KAT.x2 33.41862 1.60398 20.835 0.000000000006173 ***
## B5_R20_KAT.x3 17.88054 1.44712 12.356 0.000000006421497 ***
## B5_R20_KAT.x4 -6.89634 1.68230 -4.099 0.00108 **
## B5_R20_KAT.x5 32.67077 1.66505 19.622 0.000000000013930 ***
## B5_R20_KAT.x6 15.76498 1.37649 11.453 0.000000016982758 ***
## B5_R20_KAT.x7 18.44226 1.66205 11.096 0.000000025386188 ***
## B5_R20_KAT.x8 16.25088 1.44716 11.230 0.000000021817713 ***
## B5_R20_KAT.x9 34.46976 1.33995 25.725 0.000000000000346 ***
## B5_R20_KAT.x10 16.97669 1.49067 11.389 0.000000018246951 ***
## B5_R20_KAT.x11 16.45471 1.72788 9.523 0.000000170526920 ***
## B5_R20_KAT.x12 32.29017 2.03719 15.850 0.000000000244857 ***
## B5_R20_KAT.x13 15.27022 2.10307 7.261 0.000004152935378 ***
## B5_R20_KAT.x14 15.37999 1.11828 13.753 0.000000001595192 ***
## B5_R20_KAT.x15 15.98219 1.71375 9.326 0.000000220173786 ***
## B5_R20_KAT.x16 16.61525 1.75576 9.463 0.000000184180801 ***
## B5_R20_KAT.x17 17.16553 1.48524 11.557 0.000000015128758 ***
## B5_R5A1.y2 0.87078 0.97316 0.895 0.38602
## B5_R5B.y2 -0.92220 1.18703 -0.777 0.45015
## B5_R5B.y3 -3.11938 0.84457 -3.693 0.00241 **
## B5_R5B.y4 -4.46382 1.34798 -3.311 0.00514 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.5262438)
##
## Number of Fisher Scoring iterations: 20
##
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2
## 1 1 1
## 2 6 291
##
## Accuracy : 0.9766
## 95% CI : (0.9524, 0.9905)
## No Information Rate : 0.9766
## P-Value [Acc > NIR] : 0.5987
##
## Kappa : 0.214
##
## Mcnemar's Test P-Value : 0.1306
##
## Sensitivity : 0.9966
## Specificity : 0.1429
## Pos Pred Value : 0.9798
## Neg Pred Value : 0.5000
## Prevalence : 0.9766
## Detection Rate : 0.9732
## Detection Prevalence : 0.9933
## Balanced Accuracy : 0.5697
##
## 'Positive' Class : 2
##
## 1 2
## 166 7747
## 1 2
## 235 7678log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("2", "1"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##16. Pemodelan Variabel Aktif Mempersiapkan Usaha Semingu yang Lalu
fit.logit2<-svyglm(B5_R12B.y~B2_R1.x+B2_R2.x+B4_K3.x+B4_K6.x+B4_K8.x
+B4_K9.x+B4_K10.x+B5_R4A.x+B5_R4B.x+B5_R4C.x
+B5_R4D.x+B5_R4E.x+B5_R5A1.x+B5_R5A2.x+B5_R5A3.x
+B5_R5A4.x+B5_R5B.x+B5_R20_KAT.x+B5_R12B.x+B5_R5A1.y+B5_R5B.y,
design=des,family=binomial)
summary(fit.logit2)
predict = predict(fit.logit2, newdata = testing, type = "response")
summary(predict)
predict <- factor(ifelse(predict > 0.5, "2","1"))
log_conf <- confusionMatrix(predict, testing$B5_R12B.y, positive = "2")
log_conf
B5_R12B = predict(fit.logit2, newdata = df_predict, type = "response")
B5_R12B = as.data.frame(B5_R12B)
B5_R12B = B5_R12B$response
df_predict['B5_R12B.y'] <- factor(ifelse(B5_R12B > 0.5, "2","1"))
summary(df_predict$B5_R12B.y)
summary(df_predict$B5_R12B.x)##
## Call:
## svyglm(formula = B5_R12B.y ~ B2_R1.x + B2_R2.x + B4_K3.x + B4_K6.x +
## B4_K8.x + B4_K9.x + B4_K10.x + B5_R4A.x + B5_R4B.x + B5_R4C.x +
## B5_R4D.x + B5_R4E.x + B5_R5A1.x + B5_R5A2.x + B5_R5A3.x +
## B5_R5A4.x + B5_R5B.x + B5_R20_KAT.x + B5_R12B.x + B5_R5A1.y +
## B5_R5B.y, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~psu.y + ssu.y, strata = ~strata.y, weights = ~FINAL_WEIG.y,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 169.62294 8.11966 20.890 0.00000000000595338 ***
## B2_R1.x -2.51915 1.03191 -2.441 0.028520 *
## B2_R2.x -0.09588 0.92840 -0.103 0.919211
## B4_K3.x 8.11481 2.54758 3.185 0.006611 **
## B4_K6.x2 -24.29313 2.81332 -8.635 0.00000055669830113 ***
## B4_K8.x 0.10410 0.05224 1.993 0.066150 .
## B4_K9.x2 -40.11529 5.86952 -6.835 0.00000813346023951 ***
## B4_K9.x3 -40.42488 3.34231 -12.095 0.00000000845340265 ***
## B4_K10.x2 -32.87033 1.92014 -17.119 0.00000000008758027 ***
## B4_K10.x3 -14.72036 2.08555 -7.058 0.00000569891232253 ***
## B4_K10.x4 8.25676 3.65209 2.261 0.040223 *
## B5_R4A.x2 12.79040 2.26916 5.637 0.00006135104655639 ***
## B5_R4A.x3 -76.76414 4.21434 -18.215 0.00000000003803728 ***
## B5_R4B.x5 17.99581 2.02063 8.906 0.00000038450166088 ***
## B5_R4B.x6 -91.29760 4.77092 -19.136 0.00000000001954723 ***
## B5_R4C.x2 15.40751 2.43970 6.315 0.00001905748828536 ***
## B5_R4C.x3 7.70741 4.01331 1.920 0.075405 .
## B5_R4D.x5 -22.18439 6.39505 -3.469 0.003760 **
## B5_R4D.x6 44.09515 4.28284 10.296 0.00000006502796010 ***
## B5_R4E.x2 13.26197 1.53441 8.643 0.00000055058881632 ***
## B5_R4E.x3 -17.36052 10.58926 -1.639 0.123389
## B5_R5A1.x2 -45.58190 1.96750 -23.167 0.00000000000145325 ***
## B5_R5A2.x4 -26.80521 2.84331 -9.427 0.00000019291431254 ***
## B5_R5A3.x2 -21.45654 2.86103 -7.500 0.00000288062820813 ***
## B5_R5A4.x4 20.30496 0.85303 23.803 0.00000000000100345 ***
## B5_R5B.x2 -10.31166 2.85953 -3.606 0.002864 **
## B5_R5B.x3 -1.99587 1.40638 -1.419 0.177735
## B5_R5B.x4 23.46446 1.95967 11.974 0.00000000962043546 ***
## B5_R20_KAT.x1 -30.89092 2.08802 -14.794 0.00000000061084089 ***
## B5_R20_KAT.x2 -69.74085 3.93649 -17.716 0.00000000005525202 ***
## B5_R20_KAT.x3 14.83223 1.64648 9.008 0.00000033501164430 ***
## B5_R20_KAT.x4 -143.64355 6.81143 -21.089 0.00000000000523611 ***
## B5_R20_KAT.x5 -89.16604 2.53361 -35.193 0.00000000000000459 ***
## B5_R20_KAT.x6 -29.64093 2.51730 -11.775 0.00000001192234368 ***
## B5_R20_KAT.x7 -27.27166 2.40817 -11.325 0.00000001960095768 ***
## B5_R20_KAT.x8 -53.59961 1.96581 -27.266 0.00000000000015566 ***
## B5_R20_KAT.x9 -30.82733 2.03964 -15.114 0.00000000046036666 ***
## B5_R20_KAT.x10 -96.01467 5.24787 -18.296 0.00000000003583198 ***
## B5_R20_KAT.x11 -73.42817 2.79827 -26.241 0.00000000000026366 ***
## B5_R20_KAT.x12 -72.74025 3.29529 -22.074 0.00000000000281155 ***
## B5_R20_KAT.x13 -74.09373 4.15793 -17.820 0.00000000005109493 ***
## B5_R20_KAT.x14 -54.06549 5.16252 -10.473 0.00000005256167559 ***
## B5_R20_KAT.x15 -40.29851 2.65491 -15.179 0.00000000043502741 ***
## B5_R20_KAT.x16 -50.61957 9.51046 -5.323 0.000108 ***
## B5_R20_KAT.x17 -42.88653 2.87335 -14.926 0.00000000054354029 ***
## B5_R12B.x2 48.94153 3.13478 15.612 0.00000000029939035 ***
## B5_R5A1.y2 -52.01227 3.21801 -16.163 0.00000000018878272 ***
## B5_R5B.y2 -12.60246 5.90887 -2.133 0.051127 .
## B5_R5B.y3 2.85165 4.12323 0.692 0.500494
## B5_R5B.y4 -21.41217 5.33215 -4.016 0.001276 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.01304022)
##
## Number of Fisher Scoring iterations: 25
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 1.000 0.982 1.000 1.000
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2
## 1 0 6
## 2 3 290
##
## Accuracy : 0.9699
## 95% CI : (0.9436, 0.9861)
## No Information Rate : 0.99
## P-Value [Acc > NIR] : 0.999
##
## Kappa : -0.0136
##
## Mcnemar's Test P-Value : 0.505
##
## Sensitivity : 0.9797
## Specificity : 0.0000
## Pos Pred Value : 0.9898
## Neg Pred Value : 0.0000
## Prevalence : 0.9900
## Detection Rate : 0.9699
## Detection Prevalence : 0.9799
## Balanced Accuracy : 0.4899
##
## 'Positive' Class : 2
##
## 1 2
## 597 7316
## 1 2
## 43 7870log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("2", "1"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##17. Pemodelan Variabel Alasan Tidak Aktif Mencari Pekerjaan dan Tidak Aktif Mempersiapkan Usaha Semingu yang Lalu
fit.logit3<-svyglm(B5_R17A.y~B2_R1.x+B2_R2.x+B4_K3.x+B4_K6.x+B4_K8.x
+B4_K9.x+B4_K10.x+B5_R4A.x+B5_R4B.x+B5_R4C.x
+B5_R4D.x+B5_R4E.x+B5_R5A1.x+B5_R5A2.x+B5_R5A3.x
+B5_R5A4.x+B5_R5B.x+B5_R20_KAT.x+B5_R17A.x
+B5_R12A.y+B5_R12B.y, design=des,family=binomial)
summary(fit.logit3)
predict = predict(fit.logit3, newdata = testing, type = "response")
summary(predict)
predict <- cut(predict, breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1), labels = c(0, 1, 2, 3, 4), right = TRUE)
log_conf <- confusionMatrix(predict, testing$B5_R17A.y)
log_conf
B5_R17A = predict(fit.logit3, newdata = df_predict, type = "response")
df_predict['B5_R17A.y'] <- cut(B5_R17A, breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1), labels = c(0, 1, 2, 3, 4), right = TRUE)
summary(df_predict$B5_R17A.y)
summary(df_predict$B5_R17A.x)##
## Call:
## svyglm(formula = B5_R17A.y ~ B2_R1.x + B2_R2.x + B4_K3.x + B4_K6.x +
## B4_K8.x + B4_K9.x + B4_K10.x + B5_R4A.x + B5_R4B.x + B5_R4C.x +
## B5_R4D.x + B5_R4E.x + B5_R5A1.x + B5_R5A2.x + B5_R5A3.x +
## B5_R5A4.x + B5_R5B.x + B5_R20_KAT.x + B5_R17A.x + B5_R12A.y +
## B5_R12B.y, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~psu.y + ssu.y, strata = ~strata.y, weights = ~FINAL_WEIG.y,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -78.592983 0.928638 -84.633 <0.0000000000000002 ***
## B2_R1.x -0.005396 0.087904 -0.061 0.952
## B2_R2.x -0.003431 0.091497 -0.038 0.971
## B4_K3.x 0.002755 0.018793 0.147 0.886
## B4_K6.x2 -0.049238 0.071840 -0.685 0.505
## B4_K8.x -0.001373 0.003414 -0.402 0.694
## B4_K9.x2 -0.057033 0.435933 -0.131 0.898
## B4_K9.x3 -0.019198 0.216053 -0.089 0.931
## B4_K10.x2 0.034907 0.149699 0.233 0.819
## B4_K10.x3 0.002171 0.250281 0.009 0.993
## B4_K10.x4 0.049367 0.229162 0.215 0.833
## B5_R4A.x2 -0.009845 0.225055 -0.044 0.966
## B5_R4A.x3 -0.011345 0.914176 -0.012 0.990
## B5_R4B.x5 0.007140 0.290512 0.025 0.981
## B5_R4B.x6 -0.075707 0.467693 -0.162 0.874
## B5_R4C.x2 0.006637 0.282636 0.023 0.982
## B5_R4C.x3 -0.058607 0.796807 -0.074 0.942
## B5_R4D.x5 0.030100 0.475954 0.063 0.951
## B5_R4D.x6 0.028851 1.157075 0.025 0.980
## B5_R4E.x2 0.025054 0.423022 0.059 0.954
## B5_R4E.x3 -0.131790 0.361014 -0.365 0.721
## B5_R5A1.x2 -0.073286 0.218514 -0.335 0.743
## B5_R5A2.x4 -0.030073 0.436523 -0.069 0.946
## B5_R5A3.x2 -0.027618 0.149512 -0.185 0.856
## B5_R5A4.x4 0.011620 0.198523 0.059 0.954
## B5_R5B.x2 -0.027897 0.317052 -0.088 0.931
## B5_R5B.x3 0.003230 0.180616 0.018 0.986
## B5_R5B.x4 0.008245 0.235928 0.035 0.973
## B5_R20_KAT.x1 -0.124928 0.209222 -0.597 0.561
## B5_R20_KAT.x2 -0.112191 0.540023 -0.208 0.839
## B5_R20_KAT.x3 -0.085838 0.236920 -0.362 0.723
## B5_R20_KAT.x4 -0.264746 1.012077 -0.262 0.798
## B5_R20_KAT.x5 -0.056371 0.972931 -0.058 0.955
## B5_R20_KAT.x6 -0.111736 0.219556 -0.509 0.619
## B5_R20_KAT.x7 -0.085962 0.207744 -0.414 0.686
## B5_R20_KAT.x8 -0.088303 0.272642 -0.324 0.751
## B5_R20_KAT.x9 -0.063123 0.246384 -0.256 0.802
## B5_R20_KAT.x10 -0.125410 0.396579 -0.316 0.757
## B5_R20_KAT.x11 -0.085488 0.354624 -0.241 0.813
## B5_R20_KAT.x12 -0.050996 0.786104 -0.065 0.949
## B5_R20_KAT.x13 -0.135043 0.391772 -0.345 0.736
## B5_R20_KAT.x14 -0.102958 0.228511 -0.451 0.660
## B5_R20_KAT.x15 -0.081037 0.237712 -0.341 0.739
## B5_R20_KAT.x16 -0.046225 0.389280 -0.119 0.907
## B5_R20_KAT.x17 -0.059695 0.247575 -0.241 0.813
## B5_R17A.x1 -0.061919 0.997907 -0.062 0.951
## B5_R17A.x2 -0.098091 0.656535 -0.149 0.884
## B5_R17A.x3 -0.020288 0.766225 -0.026 0.979
## B5_R17A.x4 -0.050574 0.208115 -0.243 0.812
## B5_R12A.y2 53.124422 0.216742 245.104 <0.0000000000000002 ***
## B5_R12B.y2 52.320100 0.568543 92.025 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.0000000000003942534)
##
## Number of Fisher Scoring iterations: 25
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 1.0000 1.0000 0.9666 1.0000 1.0000
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1 2 3 4
## 0 10 0 0 0 0
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 1 288
##
## Overall Statistics
##
## Accuracy : 0.9967
## 95% CI : (0.9815, 0.9999)
## No Information Rate : 0.9632
## P-Value [Acc > NIR] : 0.0001686
##
## Kappa : 0.9507
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 0 Class: 1 Class: 2 Class: 3 Class: 4
## Sensitivity 1.00000 NA NA 0.000000 1.0000
## Specificity 1.00000 1 1 1.000000 0.9091
## Pos Pred Value 1.00000 NA NA NaN 0.9965
## Neg Pred Value 1.00000 NA NA 0.996656 1.0000
## Prevalence 0.03344 0 0 0.003344 0.9632
## Detection Rate 0.03344 0 0 0.000000 0.9632
## Detection Prevalence 0.03344 0 0 0.000000 0.9666
## Balanced Accuracy 1.00000 NA NA 0.500000 0.9545
## 0 1 2 3 4
## 743 0 0 0 7170
## 0 1 2 3 4
## 263 6 4 17 7623log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("4", "3", "2", "1", "0"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##18. Pemodelan Variabel Jenis Kegiatan Seminggu yang Lalu
fit.logit4 <- svyglm(jk.y~B2_R1.x+B2_R2.x+B4_K3.x+B4_K6.x+B4_K8.x
+B4_K9.x+B4_K10.x+jk.x+B5_R12A.y+B5_R12B.y,
design=des,family=binomial)
summary(fit.logit4)
predict = predict(fit.logit4, newdata = testing, type = "response")
predict <- cut(predict, breaks = c(0, 0.20, 0.40, 0.60, 0.80, 1), labels = c(1, 2, 4, 5, 6), right = TRUE)
log_conf <- confusionMatrix(predict, testing$jk.y)
log_conf
jk = predict(fit.logit4, newdata = df_predict, type = "response")
jk = as.data.frame(jk)
jk = jk$response
df_predict['jk.y'] <- cut(jk, breaks = c(0, 0.20, 0.40, 0.60, 0.80, 1), labels = c(1, 2, 4, 5, 6), right = TRUE)
summary(df_predict$jk.y)
summary(df_predict$jk.x)##
## Call:
## svyglm(formula = jk.y ~ B2_R1.x + B2_R2.x + B4_K3.x + B4_K6.x +
## B4_K8.x + B4_K9.x + B4_K10.x + jk.x + B5_R12A.y + B5_R12B.y,
## design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~psu.y + ssu.y, strata = ~strata.y, weights = ~FINAL_WEIG.y,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.326883 1.483482 10.332 0.000000000000111 ***
## B2_R1.x 0.258119 0.257708 1.002 0.321669
## B2_R2.x -0.436769 0.243137 -1.796 0.078861 .
## B4_K3.x 0.176258 0.072793 2.421 0.019376 *
## B4_K6.x2 0.558374 0.236992 2.356 0.022690 *
## B4_K8.x 0.009507 0.009521 0.999 0.323138
## B4_K9.x2 0.378984 0.799873 0.474 0.637832
## B4_K9.x3 -1.327759 0.642481 -2.067 0.044305 *
## B4_K10.x2 -0.160093 0.421645 -0.380 0.705889
## B4_K10.x3 -0.923976 0.868838 -1.063 0.293006
## B4_K10.x4 -0.566032 0.602007 -0.940 0.351901
## jk.x2 1.812475 0.488692 3.709 0.000549 ***
## jk.x4 3.796383 0.640315 5.929 0.000000344750304 ***
## jk.x5 3.970057 0.261283 15.194 < 0.0000000000000002 ***
## jk.x6 4.482830 0.728994 6.149 0.000000159894893 ***
## B5_R12A.y2 -1.616982 0.530593 -3.048 0.003780 **
## B5_R12B.y2 -15.527451 0.944064 -16.447 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.036735)
##
## Number of Fisher Scoring iterations: 14
##
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 4 5 6
## 1 187 1 1 11 5
## 2 16 1 2 0 0
## 4 3 0 1 0 0
## 5 2 1 0 6 3
## 6 11 0 24 17 7
##
## Overall Statistics
##
## Accuracy : 0.6756
## 95% CI : (0.6193, 0.7283)
## No Information Rate : 0.7324
## P-Value [Acc > NIR] : 0.9877
##
## Kappa : 0.3262
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 4 Class: 5 Class: 6
## Sensitivity 0.8539 0.333333 0.035714 0.17647 0.46667
## Specificity 0.7750 0.939189 0.988930 0.97736 0.81690
## Pos Pred Value 0.9122 0.052632 0.250000 0.50000 0.11864
## Neg Pred Value 0.6596 0.992857 0.908475 0.90244 0.96667
## Prevalence 0.7324 0.010033 0.093645 0.11371 0.05017
## Detection Rate 0.6254 0.003344 0.003344 0.02007 0.02341
## Detection Prevalence 0.6856 0.063545 0.013378 0.04013 0.19732
## Balanced Accuracy 0.8144 0.636261 0.512322 0.57691 0.64178
## 1 2 4 5 6
## 5246 267 45 336 2019
## 1 2 4 5 6
## 5532 174 688 1225 294log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("6","5", "4", "2", "1"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##19. Filter Status Berusaha Sendiri/Pekerja Bebas dan Setting Desain Survei
sakernas2020_diy.28b=filter(df_intersect2020, B5_R24A %in% c("1", "5", "6"))
df_predict.28b=filter(df_predict, B5_R24A %in% c("1", "5", "6"))
df1=select(df_predict.28b, id_unik, FINAL_WEIG)
sak.28b = merge(x=df_intersect2019 , y =sakernas2020_diy.28b, by = "id_unik")
dim(sak.28b)## [1] 419 208##20. Pemodelan Variabel Hari Kerja Sebulan Status Berusaha Sendiri/Pekerja Bebas
set.seed(123)
ctrl <- trainControl(method = "repeatedcv", number = 84, savePredictions = TRUE)
mod_fit1 <- train(B5_R28A1.y~B2_R1.x+B2_R2.x+B4_K3.x+B4_K6.x+B4_K8.x
+B4_K9.x+B4_K10.x+B5_R4A.x+B5_R4B.x+B5_R4C.x
+B5_R4D.x+B5_R4E.x+B5_R5A1.x+B5_R5A2.x+B5_R5A3.x
+B5_R5A4.x+B5_R5B.x+B5_R20_KAT.x+B5_R28A1.x+B5_R24A.x,
data=sak.28b, method="knn", trControl = ctrl,
tuneLength = 10, weights =FINAL_WEIG.y)
View(mod_fit1$pred)
print(mod_fit1)
df_predict.28b['B5_R28A1.y'] = predict(mod_fit1, newdata = df_predict.28b)
summary(df_predict.28b$B5_R28A1.y)
summary(sakernas2020_diy.28b$B5_R28A1)## k-Nearest Neighbors
##
## 419 samples
## 20 predictor
##
## No pre-processing
## Resampling: Cross-Validated (84 fold, repeated 1 times)
## Summary of sample sizes: 414, 413, 413, 413, 414, 414, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 10.92512 0.3862427 9.075605
## 7 10.80849 0.3865846 9.220774
## 9 10.91681 0.3835839 9.397788
## 11 10.91877 0.3883956 9.499416
## 13 11.06436 0.3833920 9.722389
## 15 11.05829 0.3881514 9.732989
## 17 11.08920 0.3867155 9.824306
## 19 11.08754 0.4003846 9.832504
## 21 11.12917 0.3973609 9.858167
## 23 11.12137 0.3975984 9.833281
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 7.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.8571 15.4286 19.6250 18.8979 22.5714 30.0000
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 15.00 13.96 26.00 31.00##21. Pemodelan Variabel Pendapatan Uang Sebulan Status Berusaha Sendiri/Pekerja Bebas
set.seed(123)
ctrl <- trainControl(method = "repeatedcv", number = 84, savePredictions = TRUE)
mod_fit1 <- train(B5_R28B1.y~B2_R1.x+B2_R2.x+B4_K3.x+B4_K6.x+B4_K8.x
+B4_K9.x+B4_K10.x+B5_R4A.x+B5_R4B.x+B5_R4C.x
+B5_R4D.x+B5_R4E.x+B5_R5A1.x+B5_R5A2.x+B5_R5A3.x
+B5_R5A4.x+B5_R5B.x+B5_R20_KAT.x+B5_R28B1.x+B5_R24A.x
+B5_R28A1.y,data=sak.28b, method="knn",
trControl = ctrl, tuneLength = 10, weights =FINAL_WEIG.y)
View(mod_fit1$pred)
print(mod_fit1)
df_predict.28b['B5_R28B1.y'] = predict(mod_fit1, newdata = df_predict.28b)
summary(df_predict.28b$B5_R28B1.y)
summary(sakernas2020_diy.28b$B5_R28B1)## k-Nearest Neighbors
##
## 419 samples
## 21 predictor
##
## No pre-processing
## Resampling: Cross-Validated (84 fold, repeated 1 times)
## Summary of sample sizes: 414, 413, 414, 414, 415, 414, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 1017043.3 0.5577883 622246.3
## 7 946336.3 0.5736349 587217.7
## 9 914613.0 0.5644867 573839.6
## 11 907853.9 0.5708105 575816.8
## 13 889079.6 0.5799281 565002.1
## 15 854481.9 0.6064512 546121.3
## 17 849446.0 0.6068670 546391.6
## 19 848423.5 0.6176234 545731.1
## 21 840625.6 0.6279711 540012.8
## 23 836514.0 0.6251435 536773.9
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 23.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 771902 1308485 1379441 1811111 2840870
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 400000 859439 1200000 25000000##22. Pemodelan Variabel Pendapatan Barang Sebulan Status Berusaha Sendiri/Pekerja Bebas
set.seed(123)
ctrl <- trainControl(method = "repeatedcv", number = 84, savePredictions = TRUE)
mod_fit1 <- train(B5_R28B2.y~B2_R1.x+B2_R2.x+B4_K3.x+B4_K6.x+B4_K8.x
+B4_K9.x+B4_K10.x+B5_R4A.x+B5_R4B.x+B5_R4C.x
+B5_R4D.x+B5_R4E.x+B5_R5A1.x+B5_R5A2.x+B5_R5A3.x
+B5_R5A4.x+B5_R5B.x+B5_R20_KAT.x+B5_R28B2.x+B5_R24A.x
+B5_R28A1.y, data=sak.28b, method="knn",
trControl = ctrl, tuneLength = 1, weights =FINAL_WEIG.y)
View(mod_fit1$pred)
print(mod_fit1)
df_predict.28b['B5_R28B2.y'] = predict(mod_fit1, newdata = df_predict.28b)
summary(df_predict.28b$B5_R28B2.y)
summary(sakernas2020_diy.28b$B5_R28B2)## k-Nearest Neighbors
##
## 419 samples
## 21 predictor
##
## No pre-processing
## Resampling: Cross-Validated (84 fold, repeated 1 times)
## Summary of sample sizes: 414, 414, 414, 414, 414, 414, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 112087.8 0.6174902 60389.04
##
## Tuning parameter 'k' was held constant at a value of 5
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 40000 79268 85600 720000
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 40081 0 2900000##23. Filter Status Buruh/Karyawan/Pegawai dan Setting Desain Survei
sakernas2020_diy.28c=filter(df_intersect2020, B5_R24A %in% c("4"))
#sakernas2019_diy.28c=filter(df_intersect2019, B5_R24A %in% c("4"))
df_predict.28c=filter(df_predict, B5_R24A %in% c("4"))
df2=select(df_predict.28c, id_unik)
sak.28c = merge(x=df_intersect2019 , y =sakernas2020_diy.28c, by = "id_unik")
dim(sak.28c)## [1] 445 208##24. Pemodelan Variabel Upah Uang Sebulan Status Buruh/Karyawan/Pegawai
set.seed(123)
ctrl <- trainControl(method = "repeatedcv", number = 89, savePredictions = TRUE)
mod_fit1 <- train(B5_R28C1.y~B2_R1.x+B2_R2.x+B4_K3.x+B4_K6.x+B4_K8.x+B4_K9.x
+B4_K10.x+B5_R4A.x+B5_R4B.x+B5_R4C.x+B5_R4D.x+B5_R4E.x
+B5_R5A1.x+B5_R5A2.x+B5_R5A3.x+B5_R5A4.x+B5_R5B.x
+B5_R20_KAT.x+B5_R28C1.x+B5_R24A.x,
data=sak.28c, method="knn", trControl = ctrl,
tuneLength = 10, weights =FINAL_WEIG.y)
print(mod_fit1)
df_predict.28c['B5_R28C1.y'] = predict(mod_fit1, newdata = df_predict.28c)
summary(df_predict.28c$B5_R28C1.y)
summary(sakernas2020_diy.28c$B5_R28C1)## k-Nearest Neighbors
##
## 445 samples
## 20 predictor
##
## No pre-processing
## Resampling: Cross-Validated (89 fold, repeated 1 times)
## Summary of sample sizes: 439, 441, 440, 440, 441, 439, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 1016105.2 0.7486570 722924.6
## 7 1000495.5 0.7639502 708938.7
## 9 995508.3 0.7578837 705954.4
## 11 979361.0 0.7648500 694318.1
## 13 972938.0 0.7737228 691621.7
## 15 958229.1 0.7854831 680514.3
## 17 959570.7 0.7751031 683902.0
## 19 961759.3 0.7728342 684414.7
## 21 958359.3 0.7721862 683676.3
## 23 960118.2 0.7717976 682593.5
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 15.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 568125 1311333 1774200 2317760 2411529 8340367
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 1200000 1800000 2260857 2400000 20000000##25. Pemodelan Variabel Upah Barang Sebulan Status Buruh/Karyawan/Pegawai
set.seed(123)
ctrl <- trainControl(method = "repeatedcv", number = 89, savePredictions = TRUE)
mod_fit1 <- train(B5_R28C2.y~B2_R1.x+B2_R2.x+B4_K3.x+B4_K6.x+B4_K8.x
+B4_K9.x+B4_K10.x+B5_R4A.x+B5_R4B.x+B5_R4C.x+B5_R4D.x
+B5_R4E.x+B5_R5A1.x+B5_R5A2.x+B5_R5A3.x+B5_R5A4.x
+B5_R5B.x+B5_R20_KAT.x+B5_R28C2.x+B5_R24A.x,
data=sak.28c, method="knn", trControl = ctrl,
tuneLength = 10, weights =FINAL_WEIG.y)
print(mod_fit1)
df_predict.28c['B5_R28C2.y'] = predict(mod_fit1, newdata = df_predict.28c)
summary(df_predict.28c$B5_R28C2.y)
summary(sakernas2020_diy.28c$B5_R28C2)## k-Nearest Neighbors
##
## 445 samples
## 20 predictor
##
## No pre-processing
## Resampling: Cross-Validated (89 fold, repeated 1 times)
## Summary of sample sizes: 440, 440, 440, 440, 440, 440, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 148803.5 0.3424534 101049.67
## 7 145800.1 0.3774275 100560.07
## 9 144826.0 0.3451199 101497.34
## 11 141626.1 0.3498559 99569.18
## 13 141126.7 0.3581952 99632.56
## 15 141907.4 0.3572102 101389.93
## 17 141799.9 0.3463531 102370.27
## 19 140517.6 0.3717658 102322.84
## 21 139857.7 0.3462524 102042.52
## 23 139913.2 0.3485657 102476.49
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 21.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 34091 52083 65678 82083 293333
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 67369 0 1000000#Tujuan 2
Memprediksi nilai variabel yang tidak diobservasi di Susenas menggunakan nilai variabel yang diobservasi di Sakernas dan sebaliknya menggunakan Mass Imputation.
##1. Import Library
library(survey)
library(readxl)
library(caret)
library(dplyr)
library(stringr)##2. Load Data
sakernas_diy <- read_excel("sakernas_new2.xlsx")
sus1 <- read_excel("susenas 2020.xlsx", sheet = "RT")
sus2 <- read_excel("susenas 2020.xlsx", sheet = "Indo")
susenas_diy = merge(x = sus1, y = sus2, all.y = TRUE)##3. Membuat Variabel PSU, SSU, Strata
sakernas_diy['KODE_KAB']=str_pad(sakernas_diy$KODE_KAB, width = 2, side = 'left', pad = '0')
sakernas_diy['NO_DSRT']=str_pad(sakernas_diy$NO_DSRT, width = 2, side = 'left', pad = '0')
susenas_diy['R102']=str_pad(susenas_diy$R102, width = 2, side = 'left', pad = '0')
sakernas_diy["psu"]=paste(sakernas_diy$KODE_PROV, sakernas_diy$KODE_KAB, sakernas_diy$nks_ok, sep = "")
sakernas_diy["ssu"]=paste(sakernas_diy$KODE_PROV, sakernas_diy$KODE_KAB, sakernas_diy$nks_ok, sakernas_diy$NO_DSRT, sep = "")
sakernas_diy["strata"]=paste(sakernas_diy$KODE_PROV, sakernas_diy$KODE_KAB, sakernas_diy$KLASIFIKAS, sep = "")susenas_diy['KODE_KAB']=str_pad(susenas_diy$KODE_KAB, width = 2, side = 'left', pad = '0')##4. Mengubah Tipe Data Variabel Sakernas
sakernas_diy$B4_K3=as.factor(sakernas_diy$B4_K3)
sakernas_diy$B4_K6=as.factor(sakernas_diy$B4_K6)
sakernas_diy$B4_K9=as.factor(sakernas_diy$B4_K9)
sakernas_diy$B4_K10=as.factor(sakernas_diy$B4_K10)
sakernas_diy$B5_R1A=as.factor(sakernas_diy$B5_R1A)
sakernas_diy$B5_R4A=as.factor(sakernas_diy$B5_R4A)
sakernas_diy$B5_R4B=as.factor(sakernas_diy$B5_R4B)
sakernas_diy$B5_R4C=as.factor(sakernas_diy$B5_R4C)
sakernas_diy$B5_R4D=as.factor(sakernas_diy$B5_R4D)
sakernas_diy$B5_R4E=as.factor(sakernas_diy$B5_R4E)
sakernas_diy$B5_R5A1=as.factor(sakernas_diy$B5_R5A1)
sakernas_diy$B5_R5A2=as.factor(sakernas_diy$B5_R5A2)
sakernas_diy$B5_R5A3=as.factor(sakernas_diy$B5_R5A3)
sakernas_diy$B5_R5A4=as.factor(sakernas_diy$B5_R5A4)
sakernas_diy$B5_R5B=as.factor(sakernas_diy$B5_R5B)
sakernas_diy$B5_R20_KAT=as.factor(sakernas_diy$B5_R20_KAT)
sakernas_diy$B5_R24A=as.factor(sakernas_diy$B5_R24A)
sakernas_diy$B5_R6=as.factor(sakernas_diy$B5_R6)
sakernas_diy$B5_R12A=as.factor(sakernas_diy$B5_R12A)
sakernas_diy$B5_R12B=as.factor(sakernas_diy$B5_R12B)
sakernas_diy$B5_R17A=as.factor(sakernas_diy$B5_R17A)
sakernas_diy$KLASIFIKAS=as.factor(sakernas_diy$KLASIFIKAS)
sakernas_diy$KODE_KAB=as.factor(sakernas_diy$KODE_KAB)
sakernas_diy$jk=as.factor(sakernas_diy$jk)##5. Mengubah Tipe Data Variabel Susenas
susenas_diy$B2_R1=susenas_diy$R301
susenas_diy$B2_R2=susenas_diy$R303
susenas_diy$B4_K3=as.factor(susenas_diy$R403)
susenas_diy$B4_K6=as.factor(susenas_diy$R405)
susenas_diy$B4_K9=as.factor(susenas_diy$R612)
susenas_diy$B4_K8=susenas_diy$R407
susenas_diy$B4_K10=as.factor(susenas_diy$R404)
susenas_diy$B5_R1A=as.factor(susenas_diy$R615)
susenas_diy$B5_R4A=as.factor(susenas_diy$R1002)
susenas_diy$B5_R4B=as.factor(susenas_diy$R1003)
susenas_diy$B5_R4C=as.factor(susenas_diy$R1004)
susenas_diy$B5_R4D=as.factor(susenas_diy$R1005)
susenas_diy$B5_R4E=as.factor(susenas_diy$R1008)
susenas_diy$B5_R5A1=as.factor(susenas_diy$R702_A)
susenas_diy$B5_R5A2=as.factor(susenas_diy$R702_B)
susenas_diy$B5_R5A3=as.factor(susenas_diy$R702_C)
susenas_diy$B5_R5A4=as.factor(susenas_diy$R702_D)
susenas_diy$B5_R5B=as.factor(susenas_diy$R703)
susenas_diy$B5_R6=as.factor(susenas_diy$R704)
susenas_diy$B5_R24A=as.factor(susenas_diy$R706)
susenas_diy$B5_R20_KAT=as.factor(susenas_diy$R705)
susenas_diy$KLASIFIKAS=as.factor(susenas_diy$R105)
susenas_diy$KODE_KAB=as.factor(susenas_diy$R102)
susenas_diy$STATUS=as.factor(susenas_diy$STATUS)##6. Filter Sampel Susenas yang Termasuk Penduduk Usia Kerja
susenas_diy.15 <- subset(susenas_diy, R407>14)##7. Split Data dan Setting Desain Survei
Train <- createDataPartition(sakernas_diy$B5_R12A, p=0.8, list=FALSE)
training <- sakernas_diy[Train, ]
testing <- sakernas_diy[-Train, ]
options(survey.lonely.psu = "adjust")
des<-svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = training)##8. Pemodelan Variabel Aktif Mencari Pekerjaan Seminggu yang Lalu
fit.logit1 <- svyglm(B5_R12A~B2_R1+B2_R2+B4_K3+B4_K6+B4_K8+B4_K9
+B4_K10+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E
+B5_R5A1+B5_R5A2+B5_R5A3+B5_R5A4+B5_R5B+B5_R1A
+B5_R24A+KLASIFIKAS+KODE_KAB,design=des,family=binomial)
summary(fit.logit1)
predict = predict(fit.logit1, newdata = testing, type = "response")
predict <- factor(ifelse(predict > 0.5, "2","1"))
log_conf <- confusionMatrix(predict, testing$B5_R12A, positive = "2")
log_conf
B5_R12A.p = predict(fit.logit1, newdata = sakernas_diy, type = "response")
sakernas_diy['B5_R12A.p'] <- factor(ifelse(B5_R12A.p > 0.5, "2","1"))
B5_R12A = predict(fit.logit1, newdata = susenas_diy.15, type = "response")
susenas_diy.15['B5_R12A'] <- factor(ifelse(B5_R12A > 0.5, "2","1"))
summary(susenas_diy.15$B5_R12A)##
## Call:
## svyglm(formula = B5_R12A ~ B2_R1 + B2_R2 + B4_K3 + B4_K6 + B4_K8 +
## B4_K9 + B4_K10 + B5_R4A + B5_R4B + B5_R4C + B5_R4D + B5_R4E +
## B5_R5A1 + B5_R5A2 + B5_R5A3 + B5_R5A4 + B5_R5B + B5_R1A +
## B5_R24A + KLASIFIKAS + KODE_KAB, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~psu + ssu, strata = ~strata, weights = ~w.adj2_20,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.93289 1.66984 10.739 < 0.0000000000000002 ***
## B2_R1 -0.16055 0.27651 -0.581 0.561925
## B2_R2 0.26697 0.30959 0.862 0.389180
## B4_K32 1.31135 0.51352 2.554 0.011143 *
## B4_K33 -0.13509 0.42412 -0.319 0.750298
## B4_K34 -1.74049 0.69125 -2.518 0.012314 *
## B4_K35 0.21677 0.75548 0.287 0.774354
## B4_K36 -0.02615 0.90124 -0.029 0.976870
## B4_K37 14.23099 0.71960 19.776 < 0.0000000000000002 ***
## B4_K38 16.22786 0.65699 24.700 < 0.0000000000000002 ***
## B4_K39 0.76792 0.64733 1.186 0.236427
## B4_K62 0.46826 0.25065 1.868 0.062689 .
## B4_K8 0.04276 0.01125 3.802 0.000173 ***
## B4_K92 3.32376 1.04078 3.194 0.001551 **
## B4_K93 0.88947 0.34010 2.615 0.009354 **
## B4_K102 1.31395 0.41965 3.131 0.001909 **
## B4_K103 2.08722 1.07221 1.947 0.052488 .
## B4_K104 2.04831 1.37075 1.494 0.136125
## B5_R4A2 -1.58691 0.62081 -2.556 0.011064 *
## B5_R4A3 14.80955 0.80997 18.284 < 0.0000000000000002 ***
## B5_R4B5 15.04364 0.66140 22.745 < 0.0000000000000002 ***
## B5_R4B6 10.07199 1.25061 8.054 0.0000000000000179 ***
## B5_R4C2 -0.82691 1.77467 -0.466 0.641579
## B5_R4C3 15.84582 0.79699 19.882 < 0.0000000000000002 ***
## B5_R4D5 17.28543 1.64940 10.480 < 0.0000000000000002 ***
## B5_R4D6 15.28360 1.71175 8.929 < 0.0000000000000002 ***
## B5_R4E2 16.46550 0.66304 24.833 < 0.0000000000000002 ***
## B5_R4E3 15.99290 0.79134 20.210 < 0.0000000000000002 ***
## B5_R5A12 -0.56861 0.69083 -0.823 0.411094
## B5_R5A24 -0.72406 1.16341 -0.622 0.534165
## B5_R5A32 -0.63064 0.29297 -2.153 0.032130 *
## B5_R5A44 -0.10694 0.29061 -0.368 0.713130
## B5_R5B2 -0.44867 0.93610 -0.479 0.632064
## B5_R5B3 -2.67000 0.68951 -3.872 0.000132 ***
## B5_R5B4 -1.09082 0.74268 -1.469 0.142921
## B5_R1A10 -16.44633 0.34152 -48.157 < 0.0000000000000002 ***
## B5_R1A11 -15.78972 0.33032 -47.801 < 0.0000000000000002 ***
## B5_R1A12 -17.00643 0.97682 -17.410 < 0.0000000000000002 ***
## B5_R1A13 -15.88448 0.61394 -25.873 < 0.0000000000000002 ***
## B5_R1A14 -17.12267 0.43093 -39.734 < 0.0000000000000002 ***
## B5_R1A15 -17.20248 0.93729 -18.353 < 0.0000000000000002 ***
## B5_R1A16 -1.36021 0.71704 -1.897 0.058768 .
## B5_R1A2 -0.20741 0.47791 -0.434 0.664592
## B5_R1A3 -14.29585 2.07661 -6.884 0.0000000000328132 ***
## B5_R1A4 -15.65221 0.43504 -35.978 < 0.0000000000000002 ***
## B5_R1A5 1.71666 0.72038 2.383 0.017780 *
## B5_R1A6 -2.01596 1.03652 -1.945 0.052697 .
## B5_R1A7 -15.81669 0.42907 -36.862 < 0.0000000000000002 ***
## B5_R1A8 -16.69972 0.93021 -17.953 < 0.0000000000000002 ***
## B5_R1A9 1.73313 0.92997 1.864 0.063326 .
## B5_R24A1 -0.19053 0.57374 -0.332 0.740058
## B5_R24A2 -0.24732 0.81337 -0.304 0.761279
## B5_R24A3 1.75343 1.13471 1.545 0.123312
## B5_R24A4 0.17230 0.58878 0.293 0.769994
## B5_R24A5 -1.28061 0.65340 -1.960 0.050908 .
## B5_R24A6 15.72936 0.50888 30.910 < 0.0000000000000002 ***
## KLASIFIKAS2 -0.03092 0.50484 -0.061 0.951195
## KODE_KAB02 -0.04911 0.49173 -0.100 0.920513
## KODE_KAB03 0.28865 0.47007 0.614 0.539628
## KODE_KAB04 0.33403 0.55614 0.601 0.548533
## KODE_KAB71 -0.10208 0.53472 -0.191 0.848726
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.9852)
##
## Number of Fisher Scoring iterations: 20
##
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2
## 1 1 1
## 2 25 2030
##
## Accuracy : 0.9874
## 95% CI : (0.9815, 0.9917)
## No Information Rate : 0.9874
## P-Value [Acc > NIR] : 0.5519
##
## Kappa : 0.0697
##
## Mcnemar's Test P-Value : 0.000006462
##
## Sensitivity : 0.99951
## Specificity : 0.03846
## Pos Pred Value : 0.98783
## Neg Pred Value : 0.50000
## Prevalence : 0.98736
## Detection Rate : 0.98687
## Detection Prevalence : 0.99903
## Balanced Accuracy : 0.51898
##
## 'Positive' Class : 2
##
## 1 2
## 40 9939log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("2", "1"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##9. Pemodelan Variabel Aktif Mempersiapkan Usaha Seminggu yang Lalu
fit.logit2<-svyglm(B5_R12B~B2_R1+B2_R2+B4_K3+B4_K6+B4_K8+B4_K9
+B4_K10+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E
+B5_R5A1+B5_R5A2+B5_R5A3+B5_R5A4+B5_R5B+B5_R24A
+B5_R1A+KLASIFIKAS+KODE_KAB,design=des,family=binomial)
summary(fit.logit2)
predict = predict(fit.logit2, newdata = testing, type = "response")
summary(predict)
predict <- factor(ifelse(predict > 0.5, "2","1"))
log_conf <- confusionMatrix(predict, testing$B5_R12B, positive = "2")
log_conf
B5_R12B.p = predict(fit.logit1, newdata = sakernas_diy, type = "response")
sakernas_diy['B5_R12B.p'] <- factor(ifelse(B5_R12B.p > 0.5, "2","1"))
B5_R12B = predict(fit.logit2, newdata = susenas_diy.15, type = "response")
susenas_diy.15['B5_R12B'] <- factor(ifelse(B5_R12B > 0.5, "2","1"))
summary(susenas_diy.15$B5_R12B)##
## Call:
## svyglm(formula = B5_R12B ~ B2_R1 + B2_R2 + B4_K3 + B4_K6 + B4_K8 +
## B4_K9 + B4_K10 + B5_R4A + B5_R4B + B5_R4C + B5_R4D + B5_R4E +
## B5_R5A1 + B5_R5A2 + B5_R5A3 + B5_R5A4 + B5_R5B + B5_R24A +
## B5_R1A + KLASIFIKAS + KODE_KAB, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~psu + ssu, strata = ~strata, weights = ~w.adj2_20,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.23234 3.20228 3.195 0.001542 **
## B2_R1 -0.33011 0.44755 -0.738 0.461331
## B2_R2 -0.02017 0.46402 -0.043 0.965364
## B4_K32 3.57011 1.85923 1.920 0.055758 .
## B4_K33 3.70750 2.38413 1.555 0.120958
## B4_K34 19.45686 1.11607 17.433 < 0.0000000000000002 ***
## B4_K35 3.18345 2.59763 1.226 0.221318
## B4_K36 19.30206 1.76369 10.944 < 0.0000000000000002 ***
## B4_K37 17.05535 2.08182 8.193 0.00000000000000698 ***
## B4_K38 20.67942 2.55946 8.080 0.00000000000001503 ***
## B4_K39 0.69513 2.30755 0.301 0.763435
## B4_K62 -1.67868 2.02724 -0.828 0.408281
## B4_K8 0.05713 0.02830 2.018 0.044416 *
## B4_K92 0.72391 3.55797 0.203 0.838910
## B4_K93 -16.35393 3.09188 -5.289 0.00000023379657001 ***
## B4_K102 -1.53806 1.58749 -0.969 0.333374
## B4_K103 16.81401 1.27350 13.203 < 0.0000000000000002 ***
## B4_K104 16.22258 1.17701 13.783 < 0.0000000000000002 ***
## B5_R4A2 -3.13545 0.72707 -4.312 0.00002178744684861 ***
## B5_R4A3 -8.54614 1.51820 -5.629 0.00000004087561480 ***
## B5_R4B5 14.47382 0.83870 17.257 < 0.0000000000000002 ***
## B5_R4B6 -9.39342 1.63769 -5.736 0.00000002324820705 ***
## B5_R4C2 15.66195 0.77966 20.088 < 0.0000000000000002 ***
## B5_R4C3 15.68051 1.61354 9.718 < 0.0000000000000002 ***
## B5_R4D5 12.39044 2.01660 6.144 0.00000000248955611 ***
## B5_R4D6 13.27373 1.73167 7.665 0.00000000000023648 ***
## B5_R4E2 19.87304 2.25279 8.822 < 0.0000000000000002 ***
## B5_R4E3 -0.15689 1.14095 -0.138 0.890721
## B5_R5A12 0.63727 1.47398 0.432 0.665792
## B5_R5A24 16.11785 0.80149 20.110 < 0.0000000000000002 ***
## B5_R5A32 0.31702 0.88039 0.360 0.719026
## B5_R5A44 0.09215 0.79646 0.116 0.907965
## B5_R5B2 -1.18715 1.39506 -0.851 0.395451
## B5_R5B3 -2.61833 1.49172 -1.755 0.080216 .
## B5_R5B4 0.06488 1.68408 0.039 0.969292
## B5_R24A1 3.82746 2.23499 1.713 0.087811 .
## B5_R24A2 0.50941 0.88450 0.576 0.565085
## B5_R24A3 0.37848 1.40222 0.270 0.787407
## B5_R24A4 1.26685 1.04727 1.210 0.227334
## B5_R24A5 0.27674 1.37252 0.202 0.840341
## B5_R24A6 17.45604 0.90452 19.299 < 0.0000000000000002 ***
## B5_R1A10 -3.31336 2.57905 -1.285 0.199858
## B5_R1A11 -3.07678 2.68039 -1.148 0.251908
## B5_R1A12 -5.70728 2.92394 -1.952 0.051857 .
## B5_R1A13 -3.98530 2.75045 -1.449 0.148368
## B5_R1A14 -2.52701 2.55256 -0.990 0.322957
## B5_R1A15 -4.35624 2.81305 -1.549 0.122512
## B5_R1A16 13.79879 2.85556 4.832 0.00000213573141763 ***
## B5_R1A2 12.77348 3.05174 4.186 0.00003717772096481 ***
## B5_R1A3 -1.47111 3.96709 -0.371 0.711022
## B5_R1A4 -3.33531 2.48308 -1.343 0.180194
## B5_R1A5 14.67751 1.97044 7.449 0.00000000000096108 ***
## B5_R1A6 -6.42399 2.08438 -3.082 0.002243 **
## B5_R1A7 -3.40792 2.60259 -1.309 0.191367
## B5_R1A8 15.17645 3.86302 3.929 0.000106 ***
## B5_R1A9 17.41068 4.31833 4.032 0.00006987980509906 ***
## KLASIFIKAS2 -0.53772 0.85937 -0.626 0.531970
## KODE_KAB02 -1.31067 1.22913 -1.066 0.287108
## KODE_KAB03 -0.43606 1.37520 -0.317 0.751390
## KODE_KAB04 -2.20553 1.27088 -1.735 0.083666 .
## KODE_KAB71 -1.73681 1.35977 -1.277 0.202467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.5339679)
##
## Number of Fisher Scoring iterations: 22
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.004794 0.999024 0.999880 0.996633 1.000000 1.000000
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2
## 1 0 3
## 2 5 2049
##
## Accuracy : 0.9961
## 95% CI : (0.9924, 0.9983)
## No Information Rate : 0.9976
## P-Value [Acc > NIR] : 0.9321
##
## Kappa : -0.0018
##
## Mcnemar's Test P-Value : 0.7237
##
## Sensitivity : 0.9985
## Specificity : 0.0000
## Pos Pred Value : 0.9976
## Neg Pred Value : 0.0000
## Prevalence : 0.9976
## Detection Rate : 0.9961
## Detection Prevalence : 0.9985
## Balanced Accuracy : 0.4993
##
## 'Positive' Class : 2
##
## 1 2
## 20 9959log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("4", "3", "2", "1", "0"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##10. Pemodelan Variabel Alasan Tidak Aktif Mencari Pekerjaan dan Tidak Aktif Mempersiapkan Usaha Seminggu yang Lalu
fit.logit3<-svyglm(B5_R17A~B2_R1+B2_R2+B4_K3+B4_K6+B4_K8+B4_K9+B4_K10
+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E+B5_R5A1+B5_R5A2
+B5_R5A3+B5_R5A4+B5_R5B+B5_R24A+B5_R12A+B5_R12B+B5_R1A
+KLASIFIKAS+KODE_KAB,design=des,family=binomial)
summary(fit.logit3)
predict = predict(fit.logit3, newdata = testing, type = "response")
summary(predict)
predict <- cut(predict, breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1), labels = c(0, 1, 2, 3, 4), right = TRUE)
log_conf <- confusionMatrix(predict, testing$B5_R17A)
log_conf
B5_R17A.p = predict(fit.logit3, newdata = sakernas_diy, type = "response")
sakernas_diy['B5_R17A.p'] <- cut(B5_R17A.p, breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1), labels = c(0, 1, 2, 3, 4), right = TRUE)
B5_R17A = predict(fit.logit3, newdata = susenas_diy.15, type = "response")
susenas_diy.15['B5_R17A'] <- cut(B5_R17A, breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1), labels = c(0, 1, 2, 3, 4), right = TRUE)
summary(susenas_diy.15$B5_R17A)##
## Call:
## svyglm(formula = B5_R17A ~ B2_R1 + B2_R2 + B4_K3 + B4_K6 + B4_K8 +
## B4_K9 + B4_K10 + B5_R4A + B5_R4B + B5_R4C + B5_R4D + B5_R4E +
## B5_R5A1 + B5_R5A2 + B5_R5A3 + B5_R5A4 + B5_R5B + B5_R24A +
## B5_R12A + B5_R12B + B5_R1A + KLASIFIKAS + KODE_KAB, design = des,
## family = binomial)
##
## Survey design:
## svydesign(ids = ~psu + ssu, strata = ~strata, weights = ~w.adj2_20,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -79.416921 0.421614 -188.364 < 0.0000000000000002 ***
## B2_R1 0.012860 0.043775 0.294 0.76913
## B2_R2 -0.020598 0.046934 -0.439 0.66106
## B4_K32 -0.013252 0.039654 -0.334 0.73846
## B4_K33 0.013515 0.063790 0.212 0.83236
## B4_K34 -0.007904 0.250078 -0.032 0.97481
## B4_K35 0.014444 0.075802 0.191 0.84901
## B4_K36 0.028482 0.134965 0.211 0.83300
## B4_K37 0.029088 0.084783 0.343 0.73177
## B4_K38 -0.071295 0.306961 -0.232 0.81649
## B4_K39 0.021516 0.098577 0.218 0.82737
## B4_K62 -0.011118 0.038222 -0.291 0.77135
## B4_K8 -0.002314 0.001859 -1.245 0.21426
## B4_K92 -0.034889 0.190294 -0.183 0.85465
## B4_K93 -0.002542 0.105654 -0.024 0.98082
## B4_K102 0.032229 0.059412 0.542 0.58789
## B4_K103 0.031815 0.098891 0.322 0.74789
## B4_K104 0.031661 0.083676 0.378 0.70541
## B5_R4A2 0.004718 0.093527 0.050 0.95980
## B5_R4A3 -0.054589 0.223529 -0.244 0.80723
## B5_R4B5 0.020757 0.090540 0.229 0.81883
## B5_R4B6 0.001644 0.185501 0.009 0.99294
## B5_R4C2 -0.005880 0.093459 -0.063 0.94988
## B5_R4C3 0.020605 0.202431 0.102 0.91899
## B5_R4D5 0.011557 0.144276 0.080 0.93621
## B5_R4D6 0.042562 0.295422 0.144 0.88554
## B5_R4E2 -0.002143 0.125918 -0.017 0.98643
## B5_R4E3 -0.024376 0.204624 -0.119 0.90525
## B5_R5A12 -0.018968 0.132586 -0.143 0.88634
## B5_R5A24 0.017078 0.168512 0.101 0.91934
## B5_R5A32 0.001301 0.064772 0.020 0.98398
## B5_R5A44 -0.001018 0.066901 -0.015 0.98787
## B5_R5B2 -0.002421 0.076462 -0.032 0.97477
## B5_R5B3 0.043025 0.101667 0.423 0.67245
## B5_R5B4 -0.003742 0.122226 -0.031 0.97560
## B5_R24A1 -0.011683 0.092397 -0.126 0.89947
## B5_R24A2 -0.020406 0.094704 -0.215 0.82955
## B5_R24A3 -0.002942 0.113347 -0.026 0.97931
## B5_R24A4 -0.011997 0.092503 -0.130 0.89689
## B5_R24A5 -0.017874 0.102257 -0.175 0.86136
## B5_R24A6 -0.036340 0.098814 -0.368 0.71331
## B5_R12A2 53.167693 0.147546 360.347 < 0.0000000000000002 ***
## B5_R12B2 52.849334 0.265504 199.053 < 0.0000000000000002 ***
## B5_R1A10 -0.026733 0.084386 -0.317 0.75162
## B5_R1A11 -0.028306 0.082688 -0.342 0.73235
## B5_R1A12 0.061644 0.159767 0.386 0.69989
## B5_R1A13 -0.030987 0.109988 -0.282 0.77834
## B5_R1A14 -0.020086 0.099497 -0.202 0.84014
## B5_R1A15 -0.030756 0.165868 -0.185 0.85302
## B5_R1A16 0.029021 0.503415 0.058 0.95407
## B5_R1A2 -0.041471 0.219507 -0.189 0.85028
## B5_R1A3 -0.037440 0.585773 -0.064 0.94908
## B5_R1A4 -0.023460 0.068585 -0.342 0.73254
## B5_R1A5 0.001421 0.278196 0.005 0.99593
## B5_R1A6 -0.050603 0.372633 -0.136 0.89207
## B5_R1A7 -0.019355 0.074785 -0.259 0.79596
## B5_R1A8 0.013852 0.180896 0.077 0.93901
## B5_R1A9 0.062912 0.404722 0.155 0.87657
## KLASIFIKAS2 -0.006721 0.069523 -0.097 0.92305
## KODE_KAB02 0.170198 0.087277 1.950 0.05208 .
## KODE_KAB03 0.075535 0.063344 1.192 0.23400
## KODE_KAB04 0.246515 0.093593 2.634 0.00887 **
## KODE_KAB71 0.049061 0.107687 0.456 0.64901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.0000000000001989066)
##
## Number of Fisher Scoring iterations: 25
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 1.0000 1.0000 0.9854 1.0000 1.0000
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1 2 3 4
## 0 30 0 0 0 0
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 2027
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9982, 1)
## No Information Rate : 0.9854
## P-Value [Acc > NIR] : 0.00000000000007503
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 0 Class: 1 Class: 2 Class: 3 Class: 4
## Sensitivity 1.00000 NA NA NA 1.0000
## Specificity 1.00000 1 1 1 1.0000
## Pos Pred Value 1.00000 NA NA NA 1.0000
## Neg Pred Value 1.00000 NA NA NA 1.0000
## Prevalence 0.01458 0 0 0 0.9854
## Detection Rate 0.01458 0 0 0 0.9854
## Detection Prevalence 0.01458 0 0 0 0.9854
## Balanced Accuracy 1.00000 NA NA NA 1.0000
## 0 1 2 3 4
## 60 0 0 0 9919log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("4", "3", "2", "1", "0"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##11. Pemodelan Variabel Jenis Kegiatan Seminggu yang Lalu
fit.logit4 <- svyglm(jk~B2_R1+B2_R2+B4_K3+B4_K6+B4_K8+B4_K9
+B4_K10+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E
+B5_R5A1+B5_R5A2+B5_R5A3+B5_R5A4+B5_R5B+B5_R1A+B5_R6+B5_R12A+B5_R12B
+KLASIFIKAS+KODE_KAB,design=des,family=binomial)
summary(fit.logit4)
predict = predict(fit.logit4, newdata = testing, type = "response")
predict <- cut(predict, breaks = c(0, 0.20, 0.40, 0.60, 0.80, 1), labels = c(1, 2, 4, 5, 6), right = TRUE)
log_conf <- confusionMatrix(predict, testing$jk)
log_conf
jk.p = predict(fit.logit4, newdata = sakernas_diy, type = "response")
sakernas_diy['jk.p'] <- cut(jk.p, breaks = c(0, 0.20, 0.40, 0.60, 0.80, 1), labels = c(1, 2, 4, 5, 6), right = TRUE)
sakernas_diy$jk.p = as.factor(sakernas_diy$jk.p)
jk = predict(fit.logit4, newdata = susenas_diy.15, type = "response")
susenas_diy.15['jk'] <- cut(jk, breaks = c(0, 0.20, 0.40, 0.60, 0.80, 1), labels = c(1, 2, 4, 5, 6), right = TRUE)
susenas_diy.15$jk = as.factor(susenas_diy.15$jk)
summary(susenas_diy.15$jk)##
## Call:
## svyglm(formula = jk ~ B2_R1 + B2_R2 + B4_K3 + B4_K6 + B4_K8 +
## B4_K9 + B4_K10 + B5_R4A + B5_R4B + B5_R4C + B5_R4D + B5_R4E +
## B5_R5A1 + B5_R5A2 + B5_R5A3 + B5_R5A4 + B5_R5B + B5_R1A +
## B5_R6 + B5_R12A + B5_R12B + KLASIFIKAS + KODE_KAB, design = des,
## family = binomial)
##
## Survey design:
## svydesign(ids = ~psu + ssu, strata = ~strata, weights = ~w.adj2_20,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.466739 1.286381 0.363 0.716979
## B2_R1 0.189818 0.146938 1.292 0.197383
## B2_R2 -0.366513 0.172209 -2.128 0.034103 *
## B4_K32 -0.068706 0.353391 -0.194 0.845976
## B4_K33 0.287751 0.400057 0.719 0.472515
## B4_K34 -1.558970 0.589386 -2.645 0.008585 **
## B4_K35 -0.556936 0.393419 -1.416 0.157890
## B4_K36 1.597328 0.524607 3.045 0.002529 **
## B4_K37 0.521003 0.492015 1.059 0.290465
## B4_K38 2.657149 0.786194 3.380 0.000819 ***
## B4_K39 0.829558 0.624818 1.328 0.185264
## B4_K62 0.948107 0.283266 3.347 0.000918 ***
## B4_K8 0.019978 0.007713 2.590 0.010050 *
## B4_K92 2.319836 0.632403 3.668 0.000287 ***
## B4_K93 -3.080862 0.328024 -9.392 < 0.0000000000000002 ***
## B4_K102 -0.494654 0.392842 -1.259 0.208920
## B4_K103 -1.973400 0.560883 -3.518 0.000499 ***
## B4_K104 -0.236634 0.432851 -0.547 0.584989
## B5_R4A2 1.671855 0.260547 6.417 0.000000000523 ***
## B5_R4A3 0.763919 4.658766 0.164 0.869858
## B5_R4B5 -0.770814 0.480788 -1.603 0.109905
## B5_R4B6 -0.391725 1.259048 -0.311 0.755913
## B5_R4C2 -1.857584 0.596535 -3.114 0.002019 **
## B5_R4C3 -2.097289 1.380084 -1.520 0.129613
## B5_R4D5 -2.193276 0.801328 -2.737 0.006558 **
## B5_R4D6 2.187277 0.754664 2.898 0.004020 **
## B5_R4E2 0.110890 0.523555 0.212 0.832402
## B5_R4E3 1.546048 0.882662 1.752 0.080839 .
## B5_R5A12 1.623345 0.750367 2.163 0.031277 *
## B5_R5A24 2.071189 0.700159 2.958 0.003334 **
## B5_R5A32 0.660686 0.239252 2.761 0.006099 **
## B5_R5A44 0.250351 0.199079 1.258 0.209506
## B5_R5B2 -1.082212 0.488140 -2.217 0.027350 *
## B5_R5B3 -1.410907 0.620142 -2.275 0.023583 *
## B5_R5B4 -0.979260 0.761645 -1.286 0.199505
## B5_R1A10 0.746801 0.302204 2.471 0.014006 *
## B5_R1A11 0.040072 0.325854 0.123 0.902206
## B5_R1A12 0.902654 0.698362 1.293 0.197140
## B5_R1A13 0.172722 0.384098 0.450 0.653255
## B5_R1A14 0.507861 0.364399 1.394 0.164411
## B5_R1A15 1.264028 0.538988 2.345 0.019650 *
## B5_R1A16 -9.876439 0.619309 -15.948 < 0.0000000000000002 ***
## B5_R1A2 -0.883427 0.445983 -1.981 0.048494 *
## B5_R1A3 -1.066625 1.023969 -1.042 0.298385
## B5_R1A4 0.147058 0.264517 0.556 0.578647
## B5_R1A5 -0.740454 0.649407 -1.140 0.255086
## B5_R1A6 2.841248 1.220155 2.329 0.020525 *
## B5_R1A7 0.007809 0.343013 0.023 0.981851
## B5_R1A8 -0.365893 0.398168 -0.919 0.358844
## B5_R1A9 -0.126815 0.765325 -0.166 0.868501
## B5_R61 3.621198 0.376165 9.627 < 0.0000000000000002 ***
## B5_R62 8.715622 0.570058 15.289 < 0.0000000000000002 ***
## B5_R12A2 -2.844355 0.522416 -5.445 0.000000105993 ***
## B5_R12B2 -2.852302 0.638590 -4.467 0.000011161987 ***
## KLASIFIKAS2 0.707699 0.205200 3.449 0.000641 ***
## KODE_KAB02 0.211516 0.263229 0.804 0.422279
## KODE_KAB03 0.256666 0.251565 1.020 0.308395
## KODE_KAB04 0.998973 0.292097 3.420 0.000710 ***
## KODE_KAB71 0.519869 0.340911 1.525 0.128296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.971898)
##
## Number of Fisher Scoring iterations: 13
##
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 4 5 6
## 1 1317 6 4 6 6
## 2 22 10 1 5 9
## 4 11 2 0 4 4
## 5 6 3 0 1 6
## 6 7 42 59 129 397
##
## Overall Statistics
##
## Accuracy : 0.8386
## 95% CI : (0.822, 0.8543)
## No Information Rate : 0.6626
## P-Value [Acc > NIR] : < 0.00000000000000022
##
## Kappa : 0.6797
##
## Mcnemar's Test P-Value : < 0.00000000000000022
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 4 Class: 5 Class: 6
## Sensitivity 0.9663 0.158730 0.00000 0.0068966 0.9408
## Specificity 0.9683 0.981444 0.98946 0.9921548 0.8550
## Pos Pred Value 0.9836 0.212766 0.00000 0.0625000 0.6262
## Neg Pred Value 0.9359 0.973632 0.96857 0.9294463 0.9824
## Prevalence 0.6626 0.030627 0.03111 0.0704910 0.2052
## Detection Rate 0.6403 0.004861 0.00000 0.0004861 0.1930
## Detection Prevalence 0.6509 0.022849 0.01021 0.0077783 0.3082
## Balanced Accuracy 0.9673 0.570087 0.49473 0.4995257 0.8979
## 1 2 4 5 6
## 6438 192 115 85 3149log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("6", "5", "4", "2", "1"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##12. Filter dan SPlit Data Kabupaten Kulonprogo
susenas_diy01=filter(susenas_diy.15, KODE_KAB == "01")
sakernas_diy01=filter(sakernas_diy, KODE_KAB == "01")
set.seed(123)
Train <- createDataPartition(susenas_diy01$STATUS, p=0.8, list=FALSE)
training <- susenas_diy01[Train, ]
testing <- susenas_diy01[-Train, ]
#label <- sample(x=2,size=nrow(susenas_diy),replace=T,prob=c(0.8,0.2))
#training <- susenas_diy[label==1,]
#testing <- susenas_diy[label==2,]
options(survey.lonely.psu = "adjust")
des<-svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = training)##13. Pemodelan Variabel Status Penduduk Miskin
fit.logit4<-svyglm(STATUS~B2_R1+B2_R2+B4_K3+B4_K6+B4_K8+B4_K9+B4_K10+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E
+B5_R5A1+B5_R5A2+B5_R5A3+B5_R5A4+B5_R5B,design=des, family=binomial)
summary(fit.logit4)
predict = predict(fit.logit4, newdata = testing, type = "response")
summary(predict)
predict <- cut(predict, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
log_conf <- confusionMatrix(predict, testing$STATUS)
log_conf
STATUS.p = predict(fit.logit4, newdata = susenas_diy01, type = "response")
susenas_diy01['STATUS.p'] <- cut(STATUS.p, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
summary(susenas_diy01$STATUS.p)
STATUS = predict(fit.logit4, newdata = sakernas_diy01, type = "response")
sakernas_diy01['STATUS'] <- cut(STATUS, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
summary(sakernas_diy01$STATUS)##
## Call:
## svyglm(formula = STATUS ~ B2_R1 + B2_R2 + B4_K3 + B4_K6 + B4_K8 +
## B4_K9 + B4_K10 + B5_R4A + B5_R4B + B5_R4C + B5_R4D + B5_R4E +
## B5_R5A1 + B5_R5A2 + B5_R5A3 + B5_R5A4 + B5_R5B, design = des,
## family = binomial)
##
## Survey design:
## svydesign(ids = ~PSU + SSU, strata = ~STRATA, weights = ~FWT,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.024523 1.350300 2.240 0.0315 *
## B2_R1 -0.348629 0.267627 -1.303 0.2012
## B2_R2 0.360835 0.288776 1.250 0.2198
## B4_K32 0.254807 0.294533 0.865 0.3929
## B4_K33 -0.148436 0.528618 -0.281 0.7805
## B4_K34 -1.827440 1.370488 -1.333 0.1910
## B4_K35 -0.137231 0.564266 -0.243 0.8093
## B4_K36 1.128191 0.993494 1.136 0.2638
## B4_K37 -0.567590 0.588398 -0.965 0.3413
## B4_K38 15.308438 1.288205 11.884 0.0000000000000771 ***
## B4_K39 -1.033104 0.714002 -1.447 0.1568
## B4_K62 0.015573 0.282097 0.055 0.9563
## B4_K8 -0.012380 0.013835 -0.895 0.3770
## B4_K92 15.540570 0.673096 23.088 < 0.0000000000000002 ***
## B4_K93 0.456768 0.601824 0.759 0.4529
## B4_K102 0.340135 0.364983 0.932 0.3578
## B4_K103 -0.173653 0.614838 -0.282 0.7793
## B4_K104 0.498836 0.559587 0.891 0.3788
## B5_R4A2 -0.002697 0.691392 -0.004 0.9969
## B5_R4A3 14.760253 0.979843 15.064 < 0.0000000000000002 ***
## B5_R4B5 1.732770 0.893597 1.939 0.0606 .
## B5_R4B6 1.952187 0.993076 1.966 0.0573 .
## B5_R4C2 -0.408989 0.637092 -0.642 0.5251
## B5_R4C3 0.935532 1.136914 0.823 0.4162
## B5_R4D5 -1.565931 0.692545 -2.261 0.0301 *
## B5_R4D6 0.273050 1.846469 0.148 0.8833
## B5_R4E2 0.006529 0.778760 0.008 0.9934
## B5_R4E3 0.436154 1.536661 0.284 0.7782
## B5_R5A12 -0.688042 0.374547 -1.837 0.0747 .
## B5_R5A24 -0.463514 0.676210 -0.685 0.4976
## B5_R5A32 -0.409551 0.380211 -1.077 0.2888
## B5_R5A44 -0.068508 0.317971 -0.215 0.8307
## B5_R5B2 -14.211760 0.804933 -17.656 < 0.0000000000000002 ***
## B5_R5B3 -0.133001 0.344084 -0.387 0.7014
## B5_R5B4 0.375338 0.774884 0.484 0.6311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.9881595)
##
## Number of Fisher Scoring iterations: 16
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6507 0.9048 0.9338 0.9191 0.9501 1.0000
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2
## 1 0 0
## 2 34 363
##
## Accuracy : 0.9144
## 95% CI : (0.8824, 0.94)
## No Information Rate : 0.9144
## P-Value [Acc > NIR] : 0.5455
##
## Kappa : 0
##
## Mcnemar's Test P-Value : 0.00000001519
##
## Sensitivity : 0.00000
## Specificity : 1.00000
## Pos Pred Value : NaN
## Neg Pred Value : 0.91436
## Prevalence : 0.08564
## Detection Rate : 0.00000
## Detection Prevalence : 0.00000
## Balanced Accuracy : 0.50000
##
## 'Positive' Class : 1
##
## 1 2
## 3 1986
## 1 2
## 85 1815log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("2", "1"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##14. Filter dan SPlit Data Kabupaten Bantul
susenas_diy02=filter(susenas_diy.15, KODE_KAB == "02")
sakernas_diy02=filter(sakernas_diy, KODE_KAB == "02")
set.seed(123)
Train <- createDataPartition(susenas_diy02$STATUS, p=0.8, list=FALSE)
training <- susenas_diy02[Train, ]
testing <- susenas_diy02[-Train, ]
#label <- sample(x=2,size=nrow(susenas_diy),replace=T,prob=c(0.8,0.2))
#training <- susenas_diy[label==1,]
#testing <- susenas_diy[label==2,]
options(survey.lonely.psu = "adjust")
des<-svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = training)##15. Pemodelan Variabel Status Penduduk Miskin
fit.logit4<-svyglm(STATUS~B2_R1+B2_R2+B4_K3+B4_K6+B4_K8+B4_K9+B4_K10+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E
+B5_R5A1+B5_R5A2+B5_R5A3+B5_R5A4+B5_R5B,design=des, family=binomial)
summary(fit.logit4)
predict = predict(fit.logit4, newdata = testing, type = "response")
summary(predict)
predict <- cut(predict, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
log_conf <- confusionMatrix(predict, testing$STATUS)
log_conf
STATUS.p = predict(fit.logit4, newdata = susenas_diy02, type = "response")
susenas_diy02['STATUS.p'] <- cut(STATUS.p, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
summary(susenas_diy02$STATUS.p)
STATUS = predict(fit.logit4, newdata = sakernas_diy02, type = "response")
sakernas_diy02['STATUS'] <- cut(STATUS, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
summary(sakernas_diy02$STATUS)##
## Call:
## svyglm(formula = STATUS ~ B2_R1 + B2_R2 + B4_K3 + B4_K6 + B4_K8 +
## B4_K9 + B4_K10 + B5_R4A + B5_R4B + B5_R4C + B5_R4D + B5_R4E +
## B5_R5A1 + B5_R5A2 + B5_R5A3 + B5_R5A4 + B5_R5B, design = des,
## family = binomial)
##
## Survey design:
## svydesign(ids = ~PSU + SSU, strata = ~STRATA, weights = ~FWT,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.88765 1.36888 13.798 < 0.0000000000000002 ***
## B2_R1 -0.68058 0.34742 -1.959 0.0557 .
## B2_R2 0.37891 0.42264 0.897 0.3743
## B4_K32 -0.26927 0.27906 -0.965 0.3392
## B4_K33 -1.30057 0.55691 -2.335 0.0236 *
## B4_K34 11.14164 0.97265 11.455 0.00000000000000137 ***
## B4_K35 -0.95578 0.60854 -1.571 0.1226
## B4_K36 -2.27573 1.23121 -1.848 0.0705 .
## B4_K37 -0.33547 0.87949 -0.381 0.7045
## B4_K38 11.90338 1.18961 10.006 0.00000000000015752 ***
## B4_K39 -1.18180 0.80852 -1.462 0.1501
## B4_K62 0.33483 0.27012 1.240 0.2209
## B4_K8 -0.03609 0.01657 -2.178 0.0342 *
## B4_K92 0.62435 0.90206 0.692 0.4921
## B4_K93 0.44963 0.63006 0.714 0.4788
## B4_K102 -0.09124 0.48268 -0.189 0.8508
## B4_K103 -0.04707 0.53809 -0.087 0.9306
## B4_K104 0.34077 1.06364 0.320 0.7500
## B5_R4A2 -0.08107 0.69736 -0.116 0.9079
## B5_R4A3 -1.85807 0.94620 -1.964 0.0551 .
## B5_R4B5 -0.47512 0.66559 -0.714 0.4787
## B5_R4B6 -1.43807 1.37774 -1.044 0.3016
## B5_R4C2 0.17802 0.52016 0.342 0.7336
## B5_R4C3 -1.27276 0.87091 -1.461 0.1502
## B5_R4D5 2.33967 1.68701 1.387 0.1716
## B5_R4D6 17.81568 1.85108 9.625 0.00000000000057346 ***
## B5_R4E2 1.23255 1.00546 1.226 0.2260
## B5_R4E3 -0.23408 1.47997 -0.158 0.8750
## B5_R5A12 0.31721 0.40150 0.790 0.4332
## B5_R5A24 -12.44549 0.80421 -15.475 < 0.0000000000000002 ***
## B5_R5A32 0.01089 0.44238 0.025 0.9805
## B5_R5A44 -0.57964 0.35302 -1.642 0.1069
## B5_R5B2 -12.56265 0.80562 -15.594 < 0.0000000000000002 ***
## B5_R5B3 -1.24023 0.49539 -2.504 0.0156 *
## B5_R5B4 -0.14529 0.67288 -0.216 0.8299
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.9835945)
##
## Number of Fisher Scoring iterations: 15
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.4237 0.9470 0.9713 0.9502 0.9824 1.0000
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2
## 1 1 1
## 2 26 402
##
## Accuracy : 0.9372
## 95% CI : (0.91, 0.9582)
## No Information Rate : 0.9372
## P-Value [Acc > NIR] : 0.551
##
## Kappa : 0.0608
##
## Mcnemar's Test P-Value : 0.00000386
##
## Sensitivity : 0.037037
## Specificity : 0.997519
## Pos Pred Value : 0.500000
## Neg Pred Value : 0.939252
## Prevalence : 0.062791
## Detection Rate : 0.002326
## Detection Prevalence : 0.004651
## Balanced Accuracy : 0.517278
##
## 'Positive' Class : 1
##
## 1 2
## 7 2148
## 1 2
## 73 2162log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("2", "1"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##16. Filter dan SPlit Data Kabupaten Gunung Kidul
susenas_diy03=filter(susenas_diy.15, KODE_KAB == "03")
sakernas_diy03=filter(sakernas_diy, KODE_KAB == "03")
set.seed(123)
Train <- createDataPartition(susenas_diy03$STATUS, p=0.8, list=FALSE)
training <- susenas_diy03[Train, ]
testing <- susenas_diy03[-Train, ]
#label <- sample(x=2,size=nrow(susenas_diy),replace=T,prob=c(0.8,0.2))
#training <- susenas_diy[label==1,]
#testing <- susenas_diy[label==2,]
options(survey.lonely.psu = "adjust")
des<-svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = training)##17. Pemodelan Variabel Status Penduduk Miskin
fit.logit4<-svyglm(STATUS~B2_R1+B2_R2+B4_K6+B4_K8+B4_K9+B4_K10+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E
+B5_R5A1+B5_R5A2+B5_R5A3+B5_R5A4+B5_R5B
,design=des, family=binomial)
summary(fit.logit4)
predict = predict(fit.logit4, newdata = testing, type = "response")
summary(predict)
predict <- cut(predict, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
log_conf <- confusionMatrix(predict, testing$STATUS)
log_conf
STATUS.p = predict(fit.logit4, newdata = susenas_diy03, type = "response")
susenas_diy03['STATUS.p'] <- cut(STATUS.p, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
summary(susenas_diy03$STATUS.p)
STATUS = predict(fit.logit4, newdata = sakernas_diy03, type = "response")
sakernas_diy03['STATUS'] <- cut(STATUS, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
summary(sakernas_diy03$STATUS)##
## Call:
## svyglm(formula = STATUS ~ B2_R1 + B2_R2 + B4_K6 + B4_K8 + B4_K9 +
## B4_K10 + B5_R4A + B5_R4B + B5_R4C + B5_R4D + B5_R4E + B5_R5A1 +
## B5_R5A2 + B5_R5A3 + B5_R5A4 + B5_R5B, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~PSU + SSU, strata = ~STRATA, weights = ~FWT,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.54482 1.46511 3.785 0.000382 ***
## B2_R1 -0.72250 0.43267 -1.670 0.100623
## B2_R2 0.53267 0.48722 1.093 0.279033
## B4_K62 -0.43422 0.33650 -1.290 0.202316
## B4_K8 -0.01658 0.01054 -1.573 0.121381
## B4_K92 13.83511 0.62090 22.282 < 0.0000000000000002 ***
## B4_K93 0.60178 0.46257 1.301 0.198700
## B4_K102 0.79341 0.59268 1.339 0.186184
## B4_K103 3.05822 1.22728 2.492 0.015751 *
## B4_K104 2.50686 0.98805 2.537 0.014041 *
## B5_R4A2 -0.64114 0.71963 -0.891 0.376852
## B5_R4A3 -0.91312 0.67357 -1.356 0.180756
## B5_R4B5 -0.60305 0.52887 -1.140 0.259120
## B5_R4B6 0.07325 0.94821 0.077 0.938708
## B5_R4C2 -1.56896 0.61474 -2.552 0.013510 *
## B5_R4C3 0.10504 1.24025 0.085 0.932812
## B5_R4D5 -0.70789 1.30123 -0.544 0.588629
## B5_R4D6 15.29335 1.56277 9.786 0.00000000000012 ***
## B5_R4E2 0.80367 0.95994 0.837 0.406097
## B5_R4E3 -2.24040 1.04215 -2.150 0.035987 *
## B5_R5A12 0.19771 0.85606 0.231 0.818206
## B5_R5A24 -1.50508 1.22549 -1.228 0.224622
## B5_R5A32 -0.61242 0.48736 -1.257 0.214202
## B5_R5A44 -0.10092 0.51745 -0.195 0.846089
## B5_R5B2 -13.96894 1.73475 -8.052 0.00000000007165 ***
## B5_R5B3 0.16279 0.84324 0.193 0.847630
## B5_R5B4 0.86584 0.85429 1.014 0.315249
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.9213262)
##
## Number of Fisher Scoring iterations: 15
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.4286 0.9513 0.9710 0.9588 0.9811 1.0000
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2
## 1 0 1
## 2 19 421
##
## Accuracy : 0.9546
## 95% CI : (0.9308, 0.9721)
## No Information Rate : 0.9569
## P-Value [Acc > NIR] : 0.6491157
##
## Kappa : -0.0043
##
## Mcnemar's Test P-Value : 0.0001439
##
## Sensitivity : 0.000000
## Specificity : 0.997630
## Pos Pred Value : 0.000000
## Neg Pred Value : 0.956818
## Prevalence : 0.043084
## Detection Rate : 0.000000
## Detection Prevalence : 0.002268
## Balanced Accuracy : 0.498815
##
## 'Positive' Class : 1
##
## 1 2
## 4 2207
## 1 2
## 65 2132log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("2", "1"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##18. Filter dan SPlit Data Kabupaten Sleman
susenas_diy04=filter(susenas_diy.15, KODE_KAB == "04")
sakernas_diy04=filter(sakernas_diy, KODE_KAB == "04")
set.seed(123)
Train <- createDataPartition(susenas_diy04$STATUS, p=0.8, list=FALSE)
training <- susenas_diy04[Train, ]
testing <- susenas_diy04[-Train, ]
#label <- sample(x=2,size=nrow(susenas_diy),replace=T,prob=c(0.8,0.2))
#training <- susenas_diy[label==1,]
#testing <- susenas_diy[label==2,]
options(survey.lonely.psu = "adjust")
des<-svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = training)##19. Pemodelan Variabel Status Penduduk Miskin
fit.logit4<-svyglm(STATUS~B2_R1+B2_R2+B4_K3+B4_K6+B4_K8+B4_K9+B4_K10+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E
+B5_R5A1+B5_R5A2+B5_R5A3+B5_R5A4+B5_R5B
,design=des, family=binomial)
summary(fit.logit4)
predict = predict(fit.logit4, newdata = testing, type = "response")
summary(predict)
predict <- cut(predict, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
log_conf <- confusionMatrix(predict, testing$STATUS)
log_conf
STATUS.p = predict(fit.logit4, newdata = susenas_diy04, type = "response")
susenas_diy04['STATUS.p'] <- cut(STATUS.p, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
summary(susenas_diy04$STATUS.p)
STATUS = predict(fit.logit4, newdata = sakernas_diy04, type = "response")
sakernas_diy04['STATUS'] <- cut(STATUS, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
summary(sakernas_diy04$STATUS)##
## Call:
## svyglm(formula = STATUS ~ B2_R1 + B2_R2 + B4_K3 + B4_K6 + B4_K8 +
## B4_K9 + B4_K10 + B5_R4A + B5_R4B + B5_R4C + B5_R4D + B5_R4E +
## B5_R5A1 + B5_R5A2 + B5_R5A3 + B5_R5A4 + B5_R5B, design = des,
## family = binomial)
##
## Survey design:
## svydesign(ids = ~PSU + SSU, strata = ~STRATA, weights = ~FWT,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.7769842 1.3084985 3.651 0.000550 ***
## B2_R1 -0.8113788 0.4175579 -1.943 0.056694 .
## B2_R2 0.7436532 0.4767158 1.560 0.124031
## B4_K32 -0.4542802 0.3316196 -1.370 0.175828
## B4_K33 -1.1206910 0.4255303 -2.634 0.010729 *
## B4_K34 16.5195696 1.0756764 15.357 < 0.0000000000000002 ***
## B4_K35 0.1192907 0.6350161 0.188 0.851625
## B4_K36 -4.2036082 1.0962685 -3.834 0.000304 ***
## B4_K37 6.2526699 1.6578504 3.772 0.000373 ***
## B4_K38 16.2612202 0.9907702 16.413 < 0.0000000000000002 ***
## B4_K39 -1.5562025 1.7443496 -0.892 0.375882
## B4_K62 -0.0637732 0.4161012 -0.153 0.878705
## B4_K8 0.0007675 0.0191491 0.040 0.968161
## B4_K92 17.7958523 0.9118728 19.516 < 0.0000000000000002 ***
## B4_K93 1.7554862 0.6642727 2.643 0.010475 *
## B4_K102 -2.0137557 1.4792026 -1.361 0.178485
## B4_K103 -2.9869919 1.0958672 -2.726 0.008398 **
## B4_K104 -2.4493117 1.5173654 -1.614 0.111734
## B5_R4A2 0.2201079 0.6042394 0.364 0.716935
## B5_R4A3 6.8827890 2.1914412 3.141 0.002616 **
## B5_R4B5 -2.8619907 1.1716715 -2.443 0.017541 *
## B5_R4B6 16.5341033 1.2382728 13.353 < 0.0000000000000002 ***
## B5_R4C2 0.1223507 1.2902081 0.095 0.924766
## B5_R4C3 19.4243681 1.8439164 10.534 0.00000000000000287 ***
## B5_R4D5 -1.4333447 1.7306448 -0.828 0.410832
## B5_R4D6 16.7167993 2.5736833 6.495 0.00000001803630847 ***
## B5_R4E2 17.2914880 0.9456747 18.285 < 0.0000000000000002 ***
## B5_R4E3 -1.4762294 1.2001828 -1.230 0.223497
## B5_R5A12 -0.8306769 1.1402710 -0.728 0.469147
## B5_R5A24 0.3207312 1.0906374 0.294 0.769714
## B5_R5A32 0.4259493 0.6827545 0.624 0.535079
## B5_R5A44 -0.7189553 0.5095255 -1.411 0.163400
## B5_R5B2 0.9576947 1.6101502 0.595 0.554223
## B5_R5B3 0.5309798 1.2020164 0.442 0.660265
## B5_R5B4 0.7507790 1.7608059 0.426 0.671354
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.8326204)
##
## Number of Fisher Scoring iterations: 19
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.7596 0.9791 0.9899 0.9805 0.9978 1.0000
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2
## 1 0 0
## 2 11 417
##
## Accuracy : 0.9743
## 95% CI : (0.9545, 0.9871)
## No Information Rate : 0.9743
## P-Value [Acc > NIR] : 0.579274
##
## Kappa : 0
##
## Mcnemar's Test P-Value : 0.002569
##
## Sensitivity : 0.0000
## Specificity : 1.0000
## Pos Pred Value : NaN
## Neg Pred Value : 0.9743
## Prevalence : 0.0257
## Detection Rate : 0.0000
## Detection Prevalence : 0.0000
## Balanced Accuracy : 0.5000
##
## 'Positive' Class : 1
##
## 1 2
## 7 2135
## 1 2
## 11 2313log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("2", "1"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
##20. Filter dan SPlit Data Kota Yogyakarta
susenas_diy05=filter(susenas_diy.15, KODE_KAB == "71")
sakernas_diy05=filter(sakernas_diy, KODE_KAB == "71")
set.seed(123)
Train <- createDataPartition(susenas_diy05$STATUS, p=0.8, list=FALSE)
training <- susenas_diy05[Train, ]
testing <- susenas_diy05[-Train, ]
#label <- sample(x=2,size=nrow(susenas_diy),replace=T,prob=c(0.8,0.2))
#training <- susenas_diy[label==1,]
#testing <- susenas_diy[label==2,]
options(survey.lonely.psu = "adjust")
des<-svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = training)##21. Pemodelan Variabel Status Penduduk Miskin
fit.logit4<-svyglm(STATUS~B2_R1+B2_R2+B4_K3+B4_K6+B4_K8+B4_K9+B4_K10+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E
+B5_R5A1+B5_R5A2+B5_R5A3+B5_R5A4+B5_R5B
,design=des, family=binomial)
summary(fit.logit4)
predict = predict(fit.logit4, newdata = testing, type = "response")
summary(predict)
predict <- cut(predict, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
log_conf <- confusionMatrix(predict, testing$STATUS)
log_conf
STATUS.p = predict(fit.logit4, newdata = susenas_diy05, type = "response")
susenas_diy05['STATUS.p'] <- cut(STATUS.p, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
summary(susenas_diy05$STATUS.p)
STATUS = predict(fit.logit4, newdata = sakernas_diy05, type = "response")
sakernas_diy05['STATUS'] <- cut(STATUS, breaks = c(0, 0.5, 1), labels = c(1, 2), right = TRUE)
summary(sakernas_diy05$STATUS)##
## Call:
## svyglm(formula = STATUS ~ B2_R1 + B2_R2 + B4_K3 + B4_K6 + B4_K8 +
## B4_K9 + B4_K10 + B5_R4A + B5_R4B + B5_R4C + B5_R4D + B5_R4E +
## B5_R5A1 + B5_R5A2 + B5_R5A3 + B5_R5A4 + B5_R5B, design = des,
## family = binomial)
##
## Survey design:
## svydesign(ids = ~PSU + SSU, strata = ~STRATA, weights = ~FWT,
## data = training)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.23673 1.32655 12.994 0.000000000000000612 ***
## B2_R1 0.12456 0.44132 0.282 0.7792
## B2_R2 -0.49797 0.46251 -1.077 0.2881
## B4_K32 -0.21261 0.29985 -0.709 0.4824
## B4_K33 0.47870 0.37127 1.289 0.2047
## B4_K34 16.28720 1.09584 14.863 < 0.0000000000000002 ***
## B4_K35 0.46123 0.59625 0.774 0.4437
## B4_K36 -0.10663 1.06396 -0.100 0.9207
## B4_K37 15.42686 1.51250 10.200 0.000000000001088999 ***
## B4_K38 15.26017 0.80899 18.863 < 0.0000000000000002 ***
## B4_K39 -0.03295 0.87758 -0.038 0.9702
## B4_K62 0.40672 0.31441 1.294 0.2032
## B4_K8 0.01685 0.01412 1.194 0.2397
## B4_K92 -14.14264 1.19180 -11.867 0.000000000000011119 ***
## B4_K93 -14.20607 0.88579 -16.038 < 0.0000000000000002 ***
## B4_K102 0.01372 0.43913 0.031 0.9752
## B4_K103 -0.68766 0.58803 -1.169 0.2492
## B4_K104 0.06561 0.61605 0.107 0.9157
## B5_R4A2 -0.06192 0.80014 -0.077 0.9387
## B5_R4A3 13.45763 1.11241 12.098 0.000000000000006057 ***
## B5_R4B5 -1.08706 0.95469 -1.139 0.2616
## B5_R4B6 -1.34231 1.31483 -1.021 0.3134
## B5_R4C2 1.56745 1.13716 1.378 0.1757
## B5_R4C3 1.44623 2.30510 0.627 0.5340
## B5_R4D5 -2.67940 1.34666 -1.990 0.0535 .
## B5_R4D6 -3.25052 1.28104 -2.537 0.0152 *
## B5_R4E2 14.94326 1.16901 12.783 0.000000000000001041 ***
## B5_R4E3 1.09534 1.85013 0.592 0.5572
## B5_R5A12 0.64087 0.45208 1.418 0.1640
## B5_R5A24 -0.35035 0.99448 -0.352 0.7265
## B5_R5A32 0.14127 0.57212 0.247 0.8062
## B5_R5A44 -0.48043 0.37725 -1.274 0.2102
## B5_R5B2 0.32611 1.08255 0.301 0.7648
## B5_R5B3 -0.91855 0.48301 -1.902 0.0644 .
## B5_R5B4 -0.73461 0.94967 -0.774 0.4438
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.9509052)
##
## Number of Fisher Scoring iterations: 16
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.5639 0.8692 0.9168 0.9068 0.9561 1.0000
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2
## 1 0 0
## 2 26 269
##
## Accuracy : 0.9119
## 95% CI : (0.8735, 0.9416)
## No Information Rate : 0.9119
## P-Value [Acc > NIR] : 0.552
##
## Kappa : 0
##
## Mcnemar's Test P-Value : 0.0000009443
##
## Sensitivity : 0.00000
## Specificity : 1.00000
## Pos Pred Value : NaN
## Neg Pred Value : 0.91186
## Prevalence : 0.08814
## Detection Rate : 0.00000
## Detection Prevalence : 0.00000
## Balanced Accuracy : 0.50000
##
## 'Positive' Class : 1
##
## 1 2
## 4 1478
## 1 2
## 5 1629log_conf$table %>%
data.frame() %>%
mutate(Prediction = factor(Prediction, levels = c("2", "1"))) %>%
group_by(Reference) %>%
mutate(total = sum(Freq)) %>%
ungroup() %>%
ggplot(aes(Reference, Prediction, fill = Freq)) +
geom_tile() +
geom_text(aes(label = Freq), size = 5) +
scale_fill_gradient(low = "#ea4434", high = "#badb33") +
scale_x_discrete(position = "top") +
geom_tile(color = "black", fill = "black", alpha = 0)
sakernas_diy=rbind(sakernas_diy01, sakernas_diy02, sakernas_diy03, sakernas_diy04, sakernas_diy05)
susenas_diy.15=rbind(susenas_diy01, susenas_diy02, susenas_diy03, susenas_diy04, susenas_diy05)##22. Pemodelan Variabel Pengeluaran Rata-rata Perkapita
set.seed(123)
ctrl <- trainControl(method = "repeatedcv", number = 2000, savePredictions = TRUE)
mod_fit1 <- train(KAPITA~B2_R1+B2_R2+B4_K3+B4_K6+B4_K8+B4_K9+B4_K10
+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E+B5_R5A1+B5_R5A2
+B5_R5A3+B5_R5A4+B5_R5B+B5_R1A+B5_R20_KAT+B5_R24A
+KLASIFIKAS+KODE_KAB,
data=susenas_diy.15, method="knn",
trControl = ctrl, tuneLength = 10, weights=FWT)
print(mod_fit1)
susenas_diy.15['KAPITA.p'] = predict(mod_fit1, newdata = susenas_diy.15)
sakernas_diy['KAPITA'] = predict(mod_fit1, newdata = sakernas_diy)
summary(sakernas_diy$KAPITA)
summary(susenas_diy.15$KAPITA)
h<-hist(sakernas_diy$KAPITA, breaks=20, xlab="Upah Barang Sebulan Status Buruh/Karyawan/Pegawai", main="Sakernas")
x<-sakernas_diy$KAPITA
xfit<-seq(min(x),max(x),length=40) # parameter sumbu x
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) # parameter sumbu y
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit, yfit, col="blue", lwd=2) # menambahkan garis kurva norm
h<-hist(susenas_diy.15$KAPITA, breaks=20, xlab="Upah Barang Sebulan Status Buruh/Karyawan/Pegawai", main="Susenas")
x<-susenas_diy.15$KAPITA
xfit<-seq(min(x),max(x),length=40) # parameter sumbu x
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) # parameter sumbu y
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit, yfit, col="blue", lwd=2) # menambahkan garis kurva norm## k-Nearest Neighbors
##
## 9979 samples
## 22 predictor
##
## No pre-processing
## Resampling: Cross-Validated (2000 fold, repeated 1 times)
## Summary of sample sizes: 9974, 9973, 9974, 9974, 9975, 9974, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 1032734.2 0.4464682 751667.9
## 7 1012935.6 0.4496893 742324.9
## 9 1001842.5 0.4551220 737178.9
## 11 997137.2 0.4532972 735985.1
## 13 994231.8 0.4518401 735552.8
## 15 993463.6 0.4552187 736972.1
## 17 993315.6 0.4519457 738122.0
## 19 992301.4 0.4519719 738988.1
## 21 989721.4 0.4539326 737819.4
## 23 989635.9 0.4568148 738174.6
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 23.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 518671 943493 1202664 1364300 1565526 5213752
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 158071 563358 905111 1386516 1603787 40199452
##23. Filter Sampel Susenas yang Termasuk Status Pekerja Bebas/Berusaha Sendiri
susenas_diy.156=filter(susenas_diy.15, B5_R24A %in% c("1", "5"))
sakernas_diy.156=filter(sakernas_diy, B5_R24A %in% c("1", "5"))##24. Pemodelan Variabel Hari Kerja Sebulan Status Pekerja Bebas/Berusaha Sendiri
set.seed(123)
ctrl <- trainControl(method = "repeatedcv", number = 400, savePredictions = TRUE)
mod_fit1 <- train(B5_R28A1~B2_R1+B2_R2+B4_K3+B4_K6+B4_K8+B4_K9+B4_K10
+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E+B5_R5A1+B5_R5A2
+B5_R5A3+B5_R5A4+B5_R5B+B5_R1A+B5_R20_KAT+B5_R24A
+KLASIFIKAS+KODE_KAB, data=sakernas_diy.156,
method="knn", trControl = ctrl, tuneLength = 10,
weights =w.adj2_20)
print(mod_fit1)
sakernas_diy.156['B5_R28A1.p'] = predict(mod_fit1, newdata = sakernas_diy.156)
susenas_diy.156['B5_R28A1'] = predict(mod_fit1, newdata = susenas_diy.156)
summary(susenas_diy.156$B5_R28A1)
h<-hist(sakernas_diy.156$B5_R28A1, breaks=20, xlab="Hari Kerja Sebulan Status Buruh/Karyawan/Pegawai", main="Sakernas")
x<-sakernas_diy.156$B5_R28A1
xfit<-seq(min(x),max(x),length=40) # parameter sumbu x
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) # parameter sumbu y
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit, yfit, col="blue", lwd=2) # menambahkan garis kurva norm
h<-hist(susenas_diy.156$B5_R28A1, breaks=20, xlab="Hari Kerja Sebulan Status Buruh/Karyawan/Pegawai", main="Susenas")
x<-susenas_diy.156$B5_R28A1
xfit<-seq(min(x),max(x),length=40) # parameter sumbu x
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) # parameter sumbu y
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit, yfit, col="blue", lwd=2) # menambahkan garis kurva norm## k-Nearest Neighbors
##
## 2082 samples
## 22 predictor
##
## No pre-processing
## Resampling: Cross-Validated (400 fold, repeated 1 times)
## Summary of sample sizes: 2076, 2078, 2076, 2076, 2077, 2077, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 7.410215 0.3703408 6.031850
## 7 7.298348 0.3915439 5.948300
## 9 7.300453 0.3905857 5.943369
## 11 7.319454 0.3805453 5.971379
## 13 7.331573 0.3779763 5.974851
## 15 7.344956 0.3783406 5.996985
## 17 7.375754 0.3760303 6.016069
## 19 7.396402 0.3785482 6.035629
## 21 7.401049 0.3775085 6.036468
## 23 7.390309 0.3823423 6.021591
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 7.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.196 16.683 19.000 18.566 20.982 27.832
##25. Pemodelan Variabel Pendapatan Uang Sebulan Status Pekerja Bebas/Berusaha Sendiri
set.seed(123)
ctrl <- trainControl(method = "repeatedcv", number = 400, savePredictions = TRUE)
mod_fit1 <- train(B5_R28B1~B2_R1+B2_R2+B4_K3+B4_K6+B4_K8+B4_K9+B4_K10
+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E+B5_R5A1+B5_R5A2
+B5_R5A3+B5_R5A4+B5_R5B+B5_R1A+B5_R20_KAT+B5_R24A+KLASIFIKAS
+B5_R28A1+KODE_KAB, data=sakernas_diy.156,
method="knn", trControl = ctrl, tuneLength = 10,
weights =w.adj2_20)
print(mod_fit1)
sakernas_diy.156['B5_R28B1.p'] = predict(mod_fit1, newdata = sakernas_diy.156)
susenas_diy.156['B5_R28B1'] = predict(mod_fit1, newdata = susenas_diy.156)
summary(susenas_diy.156$B5_R28B1)
h<-hist(sakernas_diy.156$B5_R28B1, breaks=20, xlab="Pendapatan Uang Sebulan Status Buruh/Karyawan/Pegawai", main="Sakernas")
x<-sakernas_diy.156$B5_R28B1
xfit<-seq(min(x),max(x),length=40) # parameter sumbu x
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) # parameter sumbu y
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit, yfit, col="blue", lwd=2) # menambahkan garis kurva norm
h<-hist(susenas_diy.156$B5_R28B1, breaks=20, xlab="Pendapatan Uang Sebulan Status Buruh/Karyawan/Pegawai", main="Susenas")
x<-susenas_diy.156$B5_R28B1
xfit<-seq(min(x),max(x),length=40) # parameter sumbu x
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) # parameter sumbu y
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit, yfit, col="blue", lwd=2) # menambahkan garis kurva norm## k-Nearest Neighbors
##
## 2082 samples
## 23 predictor
##
## No pre-processing
## Resampling: Cross-Validated (400 fold, repeated 1 times)
## Summary of sample sizes: 2077, 2078, 2077, 2076, 2077, 2077, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 765781.9 0.4586417 582920.9
## 7 755807.1 0.4627992 576939.2
## 9 748113.9 0.4695051 572273.6
## 11 741112.1 0.4646979 566174.3
## 13 734925.9 0.4638033 562103.3
## 15 731796.1 0.4652545 560264.6
## 17 726728.1 0.4713041 557993.5
## 19 724029.6 0.4721085 557738.8
## 21 721664.9 0.4721334 556806.2
## 23 722016.9 0.4755045 557585.9
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 21.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 1242784 1479048 1454553 1685952 2430952
##26. Pemodelan Variabel Pendapatan Barang Sebulan Status Pekerja Bebas/Berusaha Sendiri Uang
set.seed(123)
ctrl <- trainControl(method = "repeatedcv", number = 400, savePredictions = TRUE)
mod_fit1 <- train(B5_R28B2~B2_R1+B2_R2+B4_K3+B4_K6+B4_K8+B4_K9+B4_K10
+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E+B5_R5A1+B5_R5A2
+B5_R5A3+B5_R5A4+B5_R5B+B5_R1A+B5_R20_KAT+KLASIFIKAS
+B5_R24A+B5_R28A1+KODE_KAB, data=sakernas_diy.156,
method="knn", trControl = ctrl, tuneLength = 10,
weights =w.adj2_20)
print(mod_fit1)
sakernas_diy.156['B5_R28B2.p'] = predict(mod_fit1, newdata = sakernas_diy.156)
susenas_diy.156['B5_R28B2'] = predict(mod_fit1, newdata = susenas_diy.156)
summary(susenas_diy.156$B5_R28B2)
h<-hist(sakernas_diy.156$B5_R28B2, breaks=20, xlab="Pendapatan Barang Sebulan Status Buruh/Karyawan/Pegawai", main="Sakernas")
x<-sakernas_diy.156$B5_R28B2
xfit<-seq(min(x),max(x),length=40) # parameter sumbu x
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) # parameter sumbu y
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit, yfit, col="blue", lwd=2) # menambahkan garis kurva norm
h<-hist(susenas_diy.156$B5_R28B2, breaks=20, xlab="Pendapatan Barang Sebulan Status Buruh/Karyawan/Pegawai", main="Susenas")
x<-susenas_diy.156$B5_R28B2
xfit<-seq(min(x),max(x),length=40) # parameter sumbu x
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) # parameter sumbu y
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit, yfit, col="blue", lwd=2) # menambahkan garis kurva norm## k-Nearest Neighbors
##
## 2082 samples
## 23 predictor
##
## No pre-processing
## Resampling: Cross-Validated (400 fold, repeated 1 times)
## Summary of sample sizes: 2076, 2077, 2077, 2077, 2077, 2076, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 92029.90 0.6413008 52678.59
## 7 91310.00 0.6315607 52631.21
## 9 89722.81 0.6375341 52687.83
## 11 88098.63 0.6384388 52135.22
## 13 88484.10 0.6337841 52651.68
## 15 88997.09 0.6284319 53401.61
## 17 90391.51 0.6182283 54448.28
## 19 90015.37 0.6119754 54331.86
## 21 90212.60 0.6120098 54595.34
## 23 90802.05 0.6135864 55239.80
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 11.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 13409 47273 88589 91989 903636
##27. Filter Sampel Susenas yang Termasuk Status Buruh/Karyawan/Pegawai
susenas_diy.buruh=filter(susenas_diy, B5_R24A %in% c("4"))
sakernas_diy.buruh=filter(sakernas_diy, B5_R24A %in% c("4"))##28. Pemodelan Variabel Upah Uang Sebulan Status Buruh/Karyawan/Pegawai
set.seed(123)
ctrl <- trainControl(method = "repeatedcv", number = 600, savePredictions = TRUE)
mod_fit1 <- train(B5_R28C1~B2_R1+B2_R2+B4_K3+B4_K6+B4_K8+B4_K9+B4_K10
+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E+B5_R5A1+B5_R5A2
+B5_R5A3+B5_R5A4+B5_R5B+B5_R1A+B5_R20_KAT+KLASIFIKAS
+B5_R24A+KODE_KAB,
data=sakernas_diy.buruh, method="knn", trControl = ctrl,
tuneLength = 10, weights =w.adj2_20)
print(mod_fit1)
sakernas_diy.buruh['B5_R28C1.p'] = predict(mod_fit1, newdata = sakernas_diy.buruh)
susenas_diy.buruh['B5_R28C1'] = predict(mod_fit1, newdata = susenas_diy.buruh)
summary(susenas_diy.buruh$B5_R28C1)
h<-hist(sakernas_diy.buruh$B5_R28C1, breaks=20, xlab="Upah Uang Sebulan Status Buruh/Karyawan/Pegawai", main="Sakernas")
x<-sakernas_diy.buruh$B5_R28C1
xfit<-seq(min(x),max(x),length=40) # parameter sumbu x
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) # parameter sumbu y
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit, yfit, col="blue", lwd=2) # menambahkan garis kurva norm
h<-hist(susenas_diy.buruh$B5_R28C1, breaks=20, xlab="Upah Uang Sebulan Status Buruh/Karyawan/Pegawai", main="Susenas")
x<-susenas_diy.buruh$B5_R28C1
xfit<-seq(min(x),max(x),length=40) # parameter sumbu x
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) # parameter sumbu y
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit, yfit, col="blue", lwd=2) # menambahkan garis kurva norm## k-Nearest Neighbors
##
## 2946 samples
## 22 predictor
##
## No pre-processing
## Resampling: Cross-Validated (600 fold, repeated 1 times)
## Summary of sample sizes: 2941, 2941, 2941, 2941, 2942, 2942, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 1390163 0.4393604 1039661
## 7 1373021 0.4399563 1034928
## 9 1369122 0.4377308 1037116
## 11 1380698 0.4250464 1045119
## 13 1384955 0.4081539 1049740
## 15 1390874 0.4043965 1052737
## 17 1391795 0.4027399 1055353
## 19 1390908 0.3998507 1056628
## 21 1397975 0.3955964 1062292
## 23 1399682 0.3915694 1064100
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 9.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 951000 1744667 2159474 2374006 2743029 7766667
##29. Pemodelan Variabel Upah Barang Sebulan Status Buruh/Karyawan/Pegawai
set.seed(123)
ctrl <- trainControl(method = "repeatedcv", number = 600, savePredictions = TRUE)
mod_fit1 <- train(B5_R28C2~B2_R1+B2_R2+B4_K3+B4_K6+B4_K8+B4_K9+B4_K10
+B5_R4A+B5_R4B+B5_R4C+B5_R4D+B5_R4E+B5_R5A1+B5_R5A2
+B5_R5A3+B5_R5A4+B5_R5B+B5_R1A+B5_R20_KAT+KLASIFIKAS
+B5_R24A+KODE_KAB,
data=sakernas_diy.buruh, method="knn", trControl = ctrl,
tuneLength = 10, weights =w.adj2_20)
print(mod_fit1)
sakernas_diy.buruh['B5_R28C2.p'] = predict(mod_fit1, newdata = sakernas_diy.buruh)
susenas_diy.buruh['B5_R28C2'] = predict(mod_fit1, newdata = susenas_diy.buruh)
summary(susenas_diy.buruh$B5_R28C2)
h<-hist(sakernas_diy.buruh$B5_R28C2, breaks=20, xlab="Upah Barang Sebulan Status Buruh/Karyawan/Pegawai", main="Sakernas")
x<-sakernas_diy.buruh$B5_R28C2
xfit<-seq(min(x),max(x),length=40) # parameter sumbu x
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) # parameter sumbu y
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit, yfit, col="blue", lwd=2) # menambahkan garis kurva norm
h<-hist(susenas_diy.buruh$B5_R28C2, breaks=20, xlab="Upah Barang Sebulan Status Buruh/Karyawan/Pegawai", main="Susenas")
x<-susenas_diy.buruh$B5_R28C2
xfit<-seq(min(x),max(x),length=40) # parameter sumbu x
yfit<-dnorm(xfit,mean=mean(x),sd=sd(x)) # parameter sumbu y
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit, yfit, col="blue", lwd=2) # menambahkan garis kurva norm## k-Nearest Neighbors
##
## 2946 samples
## 22 predictor
##
## No pre-processing
## Resampling: Cross-Validated (600 fold, repeated 1 times)
## Summary of sample sizes: 2941, 2941, 2941, 2941, 2941, 2941, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 102992.84 0.3830682 67512.04
## 7 99816.23 0.3894454 66198.49
## 9 97942.59 0.3925047 65261.00
## 11 97371.94 0.4007439 64919.69
## 13 96409.84 0.3966741 64607.95
## 15 95582.68 0.3912917 64294.00
## 17 95049.93 0.3922184 63981.48
## 19 95090.40 0.3949255 63935.69
## 21 95002.48 0.3943672 63950.54
## 23 95048.69 0.3970204 64053.90
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 21.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2917 41497 58781 71128 81231 422381
#TUJUAN 3
Memperbaiki presisi dari hasil estimasi dengan cara menggabungkan estimasi dari dua survei yaitu Susenas dan Sakernas dengan metode Generalized Least Square.
##Variabel Susenas yang tidak dikumpulkan di Sakernas
##1. Varibel Pengeluaran Rata-rata Perkapita
options(survey.lonely.psu = "adjust")
des1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy)
svymean(~KAPITA, des1)
options(survey.lonely.psu = "adjust")
des2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy.15)
svymean(~KAPITA.p, des2)
svymean(~KAPITA, des2)## mean SE
## KAPITA 1394555 12761
## mean SE
## KAPITA.p 1406226 14230
## mean SE
## KAPITA 1476320 42204mns = svymean(~KAPITA+KAPITA.p, des2)
vcov(mns)## KAPITA KAPITA.p
## KAPITA 1781195124 372687722
## KAPITA.p 372687722 202497609var_xA = 12761^2
var_xB = 14230^2
myu_xA = 1394555
myu_xB = 1406226
myu_B = 1476320
var_B = 42204^2
cov = 372687722
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~KAPITA, des2), level = 0.95)## [1] 0.5542648
## [1] 1399757
## [1] 1464414
## [1] 37429.8
## [1] 0.02555958
## [1] 42204
## [1] 0.0285873
## [1] 1391052
## [1] 1537777
## 2.5 % 97.5 %
## KAPITA 1393602 1559039##1.1 Kabupaten Kulonprogo
sakernas_diy01=filter(sakernas_diy, KODE_KAB == "01")
susenas_diy01=filter(susenas_diy.15, KODE_KAB == "01")
options(survey.lonely.psu = "adjust")
des1.1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy01)
svymean(~KAPITA, des1.1)
options(survey.lonely.psu = "adjust")
des2.1 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy01)
svymean(~KAPITA.p, des2.1)
svymean(~KAPITA, des2.1)## mean SE
## KAPITA 1179587 19874
## mean SE
## KAPITA.p 1155682 19223
## mean SE
## KAPITA 972560 56462mns = svymean(~KAPITA+KAPITA.p, des2.1)
vcov(mns)## KAPITA KAPITA.p
## KAPITA 3187935754 798238260
## KAPITA.p 798238260 369524484var_xA = 19874^2
var_xB = 19223^2
myu_xA = 1179587
myu_xB = 1155682
myu_B = 972560
var_B = 56462^2
cov = 798238260
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~KAPITA, des2.1), level = 0.95)## [1] 0.4833537
## [1] 1167237
## [1] 997520
## [1] 48523.1
## [1] 0.04864374
## [1] 56462
## [1] 0.05805503
## [1] 902414.7
## [1] 1092625
## 2.5 % 97.5 %
## KAPITA 861897.1 1083223##1.2 Kabupaten Bantul
sakernas_diy02=filter(sakernas_diy, KODE_KAB == "02")
susenas_diy02=filter(susenas_diy.15, KODE_KAB == "02")
options(survey.lonely.psu = "adjust")
des1.2 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy02)
svymean(~KAPITA, des1.2)
options(survey.lonely.psu = "adjust")
des2.2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy02)
svymean(~KAPITA.p, des2.2)
svymean(~KAPITA, des2.2)## mean SE
## KAPITA 1357166 19353
## mean SE
## KAPITA.p 1372957 20988
## mean SE
## KAPITA 1407380 75401mns = svymean(~KAPITA+KAPITA.p, des2.2)
vcov(mns)## KAPITA KAPITA.p
## KAPITA 5685239383 1189085529
## KAPITA.p 1189085529 440510012var_xA = 19353^2
var_xB = 20988^2
myu_xA = 1357166
myu_xB = 1372957
myu_B = 1407380
var_B = 75401^2
cov = 1189085529
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~KAPITA, des2.2), level = 0.95)## [1] 0.540463
## [1] 1364423
## [1] 1384342
## [1] 62853.07
## [1] 0.04540285
## [1] 75401
## [1] 0.05357544
## [1] 1261150
## [1] 1507534
## 2.5 % 97.5 %
## KAPITA 1259597 1555162##1.3 Kabupaten Gunung Kidul
sakernas_diy03=filter(sakernas_diy, KODE_KAB == "03")
susenas_diy03=filter(susenas_diy.15, KODE_KAB == "03")
options(survey.lonely.psu = "adjust")
des1.3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy03)
svymean(~KAPITA, des1.3)
options(survey.lonely.psu = "adjust")
des2.3 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy03)
svymean(~KAPITA.p, des2.3)
svymean(~KAPITA, des2.3)## mean SE
## KAPITA 987420 13296
## mean SE
## KAPITA.p 962599 13100
## mean SE
## KAPITA 893632 44251mns = svymean(~KAPITA+KAPITA.p, des2.3)
vcov(mns)## KAPITA KAPITA.p
## KAPITA 1958139444 318590922
## KAPITA.p 318590922 171615868var_xA = 13296^2
var_xB = 13100^2
myu_xA = 987420
myu_xB = 962599
myu_B = 893632
var_B = 44251^2
cov = 318590922
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~KAPITA, des2.3), level = 0.95)## [1] 0.492575
## [1] 974825.2
## [1] 916329.7
## [1] 40826.63
## [1] 0.04455451
## [1] 44251
## [1] 0.04951815
## [1] 836309.5
## [1] 996349.9
## 2.5 % 97.5 %
## KAPITA 806901.5 980361.8##1.4 Kabupaten Sleman
sakernas_diy04=filter(sakernas_diy, KODE_KAB == "04")
susenas_diy04=filter(susenas_diy.15, KODE_KAB == "04")
options(survey.lonely.psu = "adjust")
des1.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy04)
svymean(~KAPITA, des1.4)
options(survey.lonely.psu = "adjust")
des2.4 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy04)
svymean(~KAPITA.p, des2.4)
svymean(~KAPITA, des2.4)## mean SE
## KAPITA 1637740 35738
## mean SE
## KAPITA.p 1681517 43500
## mean SE
## KAPITA 1882128 107899mns = svymean(~KAPITA+KAPITA.p, des2.4)
vcov(mns)## KAPITA KAPITA.p
## KAPITA 11642195420 2941050945
## KAPITA.p 2941050945 1892238451var_xA = 35738^2
var_xB = 43500^2
myu_xA = 1637740
myu_xB = 1681517
myu_B = 1882128
var_B = 107899^2
cov = 2941050945
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~KAPITA, des2.4), level = 0.95)## [1] 0.597027
## [1] 1655381
## [1] 1841506
## [1] 94409.15
## [1] 0.05126737
## [1] 107899
## [1] 0.05732819
## [1] 1656464
## [1] 2026548
## 2.5 % 97.5 %
## KAPITA 1670650 2093606##1.5 Kota Yogyakarta
sakernas_diy71=filter(sakernas_diy, KODE_KAB == "71")
susenas_diy71=filter(susenas_diy.15, KODE_KAB == "71")
options(survey.lonely.psu = "adjust")
des1.5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy71)
svymean(~KAPITA, des1.5)
options(survey.lonely.psu = "adjust")
des2.5 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy71)
svymean(~KAPITA.p, des2.5)
svymean(~KAPITA, des2.5)## mean SE
## KAPITA 1707144 43604
## mean SE
## KAPITA.p 1712189 40658
## mean SE
## KAPITA 1981858 115799mns = svymean(~KAPITA+KAPITA.p, des2.5)
vcov(mns)## KAPITA KAPITA.p
## KAPITA 13409484396 2721311998
## KAPITA.p 2721311998 1653057945var_xA = 43604^2
var_xB = 40658^2
myu_xA = 1707144
myu_xB = 1712189
myu_B = 1981858
var_B = 115799^2
cov = 2721311998
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~KAPITA, des2.5), level = 0.95)## [1] 0.4650803
## [1] 1709843
## [1] 1977995
## [1] 106423.3
## [1] 0.0538036
## [1] 115799
## [1] 0.05842951
## [1] 1769406
## [1] 2186585
## 2.5 % 97.5 %
## KAPITA 1754896 2208821##2. Variabel Status Penduduk Miskin (Miskin)
options(survey.lonely.psu = "adjust")
des3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy)
svymean(~factor(STATUS), des3)
options(survey.lonely.psu = "adjust")
des4 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy.15)
svymean(~factor(STATUS.p), des4)
svymean(~factor(STATUS), des4)## mean SE
## factor(STATUS)1 0.020059 0.0018
## factor(STATUS)2 0.979941 0.0018
## mean SE
## factor(STATUS.p)1 0.0025571 0.0007
## factor(STATUS.p)2 0.9974429 0.0007
## mean SE
## factor(STATUS)1 0.05026 0.0051
## factor(STATUS)2 0.94974 0.0051mns1 = svymean(~STATUS+ STATUS.p, des4)
vcov(mns1)## STATUS1 STATUS2 STATUS.p1 STATUS.p2
## STATUS1 0.000025928260 -0.000025928260 0.0000014793991 -0.0000014793991
## STATUS2 -0.000025928260 0.000025928260 -0.0000014793991 0.0000014793991
## STATUS.p1 0.000001479399 -0.000001479399 0.0000005399095 -0.0000005399095
## STATUS.p2 -0.000001479399 0.000001479399 -0.0000005399095 0.0000005399095##Kategori Miskin
var_xA = 0.0018^2
var_xB = 7e-04^2
myu_xA = 0.020059
myu_xB = 0.0025571
myu_B = 0.05026
var_B = 0.0051^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 1.479399e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~factor(STATUS), des4), level = 0.95)## [1] 0.1313673
## [1] 0.004856277
## [1] 0.05720163
## [1] 0.005042146
## [1] 0.08814689
## [1] 0.0051
## [1] 0.1014723
## [1] 0.04731903
## [1] 0.06708424
## 2.5 % 97.5 %
## factor(STATUS)1 0.04027963 0.06023982
## factor(STATUS)2 0.93976018 0.95972037##kategori Tidak Miskin
var_xA = 0.0018^2
var_xB = 7e-04^2
myu_xA = 0.979941
myu_xB = 0.9974429
myu_B = 0.94974
var_B = 0.0051^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 1.479399e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~factor(STATUS), des4), level = 0.95)## [1] 0.1313673
## [1] 0.9951437
## [1] 0.9427984
## [1] 0.005042146
## [1] 0.005348064
## [1] 0.0051
## [1] 0.005369891
## [1] 0.9329158
## [1] 0.952681
## 2.5 % 97.5 %
## factor(STATUS)1 0.04027963 0.06023982
## factor(STATUS)2 0.93976018 0.95972037##2.1 Kabupaten Kulonprogo
options(survey.lonely.psu = "adjust")
des3.1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy01)
svymean(~factor(STATUS), des3.1)
options(survey.lonely.psu = "adjust")
des4.1 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy01)
svymean(~factor(STATUS.p), des4.1)
svymean(~factor(STATUS), des4.1)## mean SE
## factor(STATUS)1 0.04109 0.0074
## factor(STATUS)2 0.95891 0.0074
## mean SE
## factor(STATUS.p)1 0.0011573 0.0007
## factor(STATUS.p)2 0.9988427 0.0007
## mean SE
## factor(STATUS)1 0.083258 0.012
## factor(STATUS)2 0.916742 0.012mns1 = svymean(~STATUS+ STATUS.p, des4.1)
vcov(mns1)## STATUS1 STATUS2 STATUS.p1 STATUS.p2
## STATUS1 0.000144690780 -0.000144690780 0.0000026468255 -0.0000026468255
## STATUS2 -0.000144690780 0.000144690780 -0.0000026468255 0.0000026468255
## STATUS.p1 0.000002646825 -0.000002646825 0.0000005558921 -0.0000005558921
## STATUS.p2 -0.000002646825 0.000002646825 -0.0000005558921 0.0000005558921##Kategori Miskin
var_xA = 0.0074^2
var_xB = 7e-04^2
myu_xA = 0.04109
myu_xB = 0.0011573
myu_B = 0.083258
var_B = 0.012^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2.646825e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~factor(STATUS), des4.1), level = 0.95)## [1] 0.008868778
## [1] 0.001511454
## [1] 0.08517103
## [1] 0.01199472
## [1] 0.1408309
## [1] 0.012
## [1] 0.1441303
## [1] 0.06166139
## [1] 0.1086807
## 2.5 % 97.5 %
## factor(STATUS)1 0.05968193 0.1068338
## factor(STATUS)2 0.89316624 0.9403181##Kategori Tidak Miskin
var_xA = 0.0074^2
var_xB = 7e-04^2
myu_xA = 0.95891
myu_xB = 0.9988427
myu_B = 0.916742
var_B = 0.012^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2.646825e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~factor(STATUS), des4.1), level = 0.95)## [1] 0.008868778
## [1] 0.9984885
## [1] 0.914829
## [1] 0.01199472
## [1] 0.01311143
## [1] 0.012
## [1] 0.01308983
## [1] 0.8913193
## [1] 0.9383386
## 2.5 % 97.5 %
## factor(STATUS)1 0.05968193 0.1068338
## factor(STATUS)2 0.89316624 0.9403181##2.2 Kabupaten Bantul
options(survey.lonely.psu = "adjust")
des3.2 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy02)
svymean(~factor(STATUS), des3.2)
options(survey.lonely.psu = "adjust")
des4.2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy02)
svymean(~factor(STATUS.p), des4.2)
svymean(~factor(STATUS), des4.2)## mean SE
## factor(STATUS)1 0.030388 0.0043
## factor(STATUS)2 0.969612 0.0043
## mean SE
## factor(STATUS.p)1 0.0036819 0.0017
## factor(STATUS.p)2 0.9963181 0.0017
## mean SE
## factor(STATUS)1 0.0563 0.0103
## factor(STATUS)2 0.9437 0.0103mns1 = svymean(~STATUS+ STATUS.p, des4.2)
vcov(mns1)## STATUS1 STATUS2 STATUS.p1 STATUS.p2
## STATUS1 0.000106169883 -0.000106169883 0.000006797216 -0.000006797216
## STATUS2 -0.000106169883 0.000106169883 -0.000006797216 0.000006797216
## STATUS.p1 0.000006797216 -0.000006797216 0.000003010557 -0.000003010557
## STATUS.p2 -0.000006797216 0.000006797216 -0.000003010557 0.000003010557##Kategori Miskin
var_xA = 0.0043^2
var_xB = 0.0017^2
myu_xA = 0.030388
myu_xB = 0.0036819
myu_B = 0.0563
var_B = 0.0103^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 6.797216e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~factor(STATUS), des4.2), level = 0.95)## [1] 0.1351731
## [1] 0.007291845
## [1] 0.06479051
## [1] 0.01019456
## [1] 0.1573465
## [1] 0.0103
## [1] 0.1829485
## [1] 0.04480918
## [1] 0.08477184
## 2.5 % 97.5 %
## factor(STATUS)1 0.03610498 0.07649544
## factor(STATUS)2 0.92350456 0.96389502##Kategori Tidak Miskin
var_xA = 0.0043^2
var_xB = 0.0017^2
myu_xA = 0.969612
myu_xB = 0.9963181
myu_B = 0.9437
var_B = 0.0103^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 6.797216e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~factor(STATUS), des4.2), level = 0.95)## [1] 0.1351731
## [1] 0.9927082
## [1] 0.9352095
## [1] 0.01019456
## [1] 0.01090083
## [1] 0.0103
## [1] 0.01091449
## [1] 0.9152282
## [1] 0.9551908
## 2.5 % 97.5 %
## factor(STATUS)1 0.03610498 0.07649544
## factor(STATUS)2 0.92350456 0.96389502##2.3 Kabupaten Gunung Kidul
options(survey.lonely.psu = "adjust")
des3.3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy03)
svymean(~factor(STATUS), des3.3)
options(survey.lonely.psu = "adjust")
des4.3 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy03)
svymean(~factor(STATUS.p), des4.3)
svymean(~factor(STATUS), des4.3)## mean SE
## factor(STATUS)1 0.028575 0.005
## factor(STATUS)2 0.971425 0.005
## mean SE
## factor(STATUS.p)1 0.0014432 0.0011
## factor(STATUS.p)2 0.9985568 0.0011
## mean SE
## factor(STATUS)1 0.042956 0.0126
## factor(STATUS)2 0.957044 0.0126mns1 = svymean(~STATUS+ STATUS.p, des4.3)
vcov(mns1)## STATUS1 STATUS2 STATUS.p1 STATUS.p2
## STATUS1 0.000158104978 -0.000158104978 0.000003258177 -0.000003258177
## STATUS2 -0.000158104978 0.000158104978 -0.000003258177 0.000003258177
## STATUS.p1 0.000003258177 -0.000003258177 0.000001194933 -0.000001194933
## STATUS.p2 -0.000003258177 0.000003258177 -0.000001194933 0.000001194933##Kategori Miskin
var_xA = 0.005^2
var_xB = 0.0011^2
myu_xA = 0.028575
myu_xB = 0.0014432
myu_B = 0.042956
var_B = 0.0126^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 3.258177e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~factor(STATUS), des4.3), level = 0.95)## [1] 0.04616559
## [1] 0.002695755
## [1] 0.04632877
## [1] 0.01258392
## [1] 0.2716221
## [1] 0.0126
## [1] 0.2933234
## [1] 0.02166429
## [1] 0.07099324
## 2.5 % 97.5 %
## factor(STATUS)1 0.01831113 0.06760023
## factor(STATUS)2 0.93239977 0.98168887##Kategori Tidak Miskin
var_xA = 0.005^2
var_xB = 0.0011^2
myu_xA = 0.971425
myu_xB = 0.9985568
myu_B = 0.957044
var_B = 0.0126^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 3.258177e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~factor(STATUS), des4.3), level = 0.95)## [1] 0.04616559
## [1] 0.9973042
## [1] 0.9536712
## [1] 0.01258392
## [1] 0.01319524
## [1] 0.0126
## [1] 0.01316554
## [1] 0.9290068
## [1] 0.9783357
## 2.5 % 97.5 %
## factor(STATUS)1 0.01831113 0.06760023
## factor(STATUS)2 0.93239977 0.98168887##2.4 Kabupaten Sleman
options(survey.lonely.psu = "adjust")
des3.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy04)
svymean(~factor(STATUS), des3.4)
options(survey.lonely.psu = "adjust")
des4.4 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy04)
svymean(~factor(STATUS.p), des4.4)
svymean(~factor(STATUS), des4.4)## mean SE
## factor(STATUS)1 0.0044512 0.0017
## factor(STATUS)2 0.9955488 0.0017
## mean SE
## factor(STATUS.p)1 0.0029378 0.0016
## factor(STATUS.p)2 0.9970622 0.0016
## mean SE
## factor(STATUS)1 0.022013 0.0078
## factor(STATUS)2 0.977987 0.0078mns1 = svymean(~STATUS+ STATUS.p, des4.4)
vcov(mns1)## STATUS1 STATUS2 STATUS.p1 STATUS.p2
## STATUS1 0.000060744130 -0.000060744130 0.000008492901 -0.000008492901
## STATUS2 -0.000060744130 0.000060744130 -0.000008492901 0.000008492901
## STATUS.p1 0.000008492901 -0.000008492901 0.000002601663 -0.000002601663
## STATUS.p2 -0.000008492901 0.000008492901 -0.000002601663 0.000002601663##Kategori Miskin
var_xA = 0.0017^2
var_xB = 0.0016^2
myu_xA = 0.0044512
myu_xB = 0.0029378
myu_B = 0.022013
var_B = 0.0078^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 8.492901e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~factor(STATUS), des4.4), level = 0.95)## [1] 0.4697248
## [1] 0.003648681
## [1] 0.02437138
## [1] 0.006899656
## [1] 0.2831049
## [1] 0.0078
## [1] 0.3543361
## [1] 0.01084805
## [1] 0.0378947
## 2.5 % 97.5 %
## factor(STATUS)1 0.006736898 0.03728824
## factor(STATUS)2 0.962711764 0.99326310##Kategori Tidak Miskin
var_xA = 0.0017^2
var_xB = 0.0016^2
myu_xA = 0.9955488
myu_xB = 0.9970622
myu_B = 0.977987
var_B = 0.0078^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 8.492901e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~factor(STATUS), des4.4), level = 0.95)## [1] 0.4697248
## [1] 0.9963513
## [1] 0.9756286
## [1] 0.006899656
## [1] 0.007072011
## [1] 0.0078
## [1] 0.007975566
## [1] 0.9621053
## [1] 0.9891519
## 2.5 % 97.5 %
## factor(STATUS)1 0.006736898 0.03728824
## factor(STATUS)2 0.962711764 0.99326310##2.5 Kota Yogyakarta
options(survey.lonely.psu = "adjust")
des3.5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy05)
svymean(~factor(STATUS), des3.5)
options(survey.lonely.psu = "adjust")
des4.5 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy05)
svymean(~factor(STATUS.p), des4.5)
svymean(~factor(STATUS), des4.5)## mean SE
## factor(STATUS)1 0.003343 0.0018
## factor(STATUS)2 0.996657 0.0018
## mean SE
## factor(STATUS.p)1 0.0021575 0.0011
## factor(STATUS.p)2 0.9978425 0.0011
## mean SE
## factor(STATUS)1 0.094743 0.0188
## factor(STATUS)2 0.905257 0.0188mns1 = svymean(~STATUS+ STATUS.p, des4.5)
vcov(mns1)## STATUS1 STATUS2 STATUS.p1 STATUS.p2
## STATUS1 0.0003516090597 -0.0003516090597 0.0000004941671 -0.0000004941671
## STATUS2 -0.0003516090597 0.0003516090597 -0.0000004941671 0.0000004941671
## STATUS.p1 0.0000004941671 -0.0000004941671 0.0000013079821 -0.0000013079821
## STATUS.p2 -0.0000004941671 0.0000004941671 -0.0000013079821 0.0000013079821##Kategori Miskin
var_xA = 0.0018^2
var_xB = 0.0011^2
myu_xA = 0.003343
myu_xB = 0.0021575
myu_B = 0.094743
var_B = 0.0188^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 4.941671e-07
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~factor(STATUS), des4.5), level = 0.95)## [1] 0.2719101
## [1] 0.002479849
## [1] 0.09487465
## [1] 0.01879854
## [1] 0.1981408
## [1] 0.0188
## [1] 0.1984315
## [1] 0.05802951
## [1] 0.1317198
## 2.5 % 97.5 %
## factor(STATUS)1 0.05799095 0.1314945
## factor(STATUS)2 0.86850553 0.9420090##Kategori Tidak Miskin
var_xA = 0.0018^2
var_xB = 0.0011^2
myu_xA = 0.996657
myu_xB = 0.9978425
myu_B = 0.905257
var_B = 0.0188^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 4.941671e-07
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~factor(STATUS), des4.5), level = 0.95)## [1] 0.2719101
## [1] 0.9975202
## [1] 0.9051254
## [1] 0.01879854
## [1] 0.02076899
## [1] 0.0188
## [1] 0.02076758
## [1] 0.8682802
## [1] 0.9419705
## 2.5 % 97.5 %
## factor(STATUS)1 0.05799095 0.1314945
## factor(STATUS)2 0.86850553 0.9420090##Variabel Sakernas yang Tidak Dikumpulkan di Susenas
##4. Skenario 1(Berdasarkan Hasil Prediksi Variabel Pembentuk Angka Pengangguran)
#Filter buat TPAK
susenas_diy.15$bekerja <- ifelse(susenas_diy.15$B5_R5A1 == "1" | susenas_diy.15$B5_R6=="1", 1, 0)
susenas_diy.15$pengangguran <- ifelse(susenas_diy.15$B5_R5A1=="2" &
susenas_diy.15$B5_R6=="2" &
susenas_diy.15$B5_R12A=="1" |
susenas_diy.15$B5_R5A1=="2" &
susenas_diy.15$B5_R6=="2" &
susenas_diy.15$B5_R12A=="2" &
susenas_diy.15$B5_R12B=="1" |
susenas_diy.15$B5_R5A1=="2" &
susenas_diy.15$B5_R6=="2" &
susenas_diy.15$B5_R12A=="2" &
susenas_diy.15$B5_R12B=="2" &
between(susenas_diy.15$B5_R17A, 0, 4), 1, 0)
susenas_diy.15$TPAK = ifelse(susenas_diy.15$pengangguran==1| susenas_diy.15$bekerja==1, 1, 0)
#Filter buat TPT
susenas.tpt = susenas_diy.15 %>% filter(bekerja==1 | pengangguran==1)
susenas.tpt$TPT[susenas.tpt$pengangguran==1]=1
susenas.tpt$TPT[susenas.tpt$bekerja==1]=0des7.1 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas.tpt)
svytotal(~bekerja, des7.1)
svytotal(~pengangguran, des7.1)
svytotal(~TPT, des7.1)
svymean(~TPT, des7.1)
des7.2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy.15)
svymean(~TPAK, des7.2)
svytotal(~TPAK, des7.2)## total SE
## bekerja 2131665 53283
## total SE
## pengangguran 14924 2846
## total SE
## TPT 14924 2846
## mean SE
## TPT 0.0069522 0.0013
## mean SE
## TPAK 0.70354 0.0065
## total SE
## TPAK 2146589 53116#Filter buat TPAK
#Data Pencacahan
sakernas_diy$bekerja <- ifelse(sakernas_diy$B5_R5A1 == "1" | sakernas_diy$B5_R6=="1", 1, 0)
sakernas_diy$pengangguran <- ifelse(sakernas_diy$B5_R5A1=="2" &
sakernas_diy$B5_R6=="2" &
sakernas_diy$B5_R12A=="1" |
sakernas_diy$B5_R5A1=="2" &
sakernas_diy$B5_R6=="2" &
sakernas_diy$B5_R12A=="2" &
sakernas_diy$B5_R12B=="1" |
sakernas_diy$B5_R5A1=="2" &
sakernas_diy$B5_R6=="2" &
sakernas_diy$B5_R12A=="2" &
sakernas_diy$B5_R12B=="2" &
between(sakernas_diy$B5_R17A, 0, 4), 1, 0)
#Hasil Prediksi
sakernas_diy$bekerja.p <- ifelse(sakernas_diy$B5_R5A1 == "1" | sakernas_diy$B5_R6=="1", 1, 0)
sakernas_diy$pengangguran.p <- ifelse(sakernas_diy$B5_R5A1=="2" &
sakernas_diy$B5_R6=="2" &
sakernas_diy$B5_R12A.p=="1" |
sakernas_diy$B5_R5A1=="2" &
sakernas_diy$B5_R6=="2" &
sakernas_diy$B5_R12A.p=="2" &
sakernas_diy$B5_R12B.p=="1" |
sakernas_diy$B5_R5A1=="2" &
sakernas_diy$B5_R6=="2" &
sakernas_diy$B5_R12A.p=="2" &
sakernas_diy$B5_R12B.p=="2" &
between(sakernas_diy$B5_R17A.p, 0, 4), 1, 0)
sakernas_diy$TPAK = ifelse(sakernas_diy$pengangguran=="1"| sakernas_diy$bekerja=="1", 1, 0)
sakernas_diy$TPAK.p = ifelse(sakernas_diy$pengangguran.p=="1"| sakernas_diy$bekerja.p=="1", 1, 0)
#Filter buat TPT
#Data Pencacahan
sakernas2 = sakernas_diy %>% filter(pengangguran=="1" | bekerja=="1")
sakernas2$TPT[sakernas2$pengangguran=="1"]=1
sakernas2$TPT[sakernas2$bekerja=="1"]=0
#Hasil Prediksi
sakernas3 = sakernas_diy %>% filter(pengangguran.p=="1" | bekerja.p=="1")
sakernas3$TPT.p[sakernas3$pengangguran.p=="1"]=1
sakernas3$TPT.p[sakernas3$bekerja.p=="1"]=0
sakernas.tpt <- sakernas2[(sakernas2$id_unik %in% sakernas3$id_unik), ]
sakernas.tpt$TPT.p[sakernas.tpt$pengangguran.p=="1"]=1
sakernas.tpt$TPT.p[sakernas.tpt$bekerja.p=="1"]=0des7.3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.tpt)
des7.3.1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas2)
des7.3.2 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas3)
des7.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy)
svytotal(~bekerja, des7.4)
svytotal(~TPT, des7.3.1)
svymean(~TPT, des7.3.1)
svytotal(~bekerja.p, des7.4)
svytotal(~TPT.p, des7.3.2)
svymean(~TPT.p, des7.3.2)
svymean(~TPAK, des7.4)
svytotal(~TPAK, des7.4)
svymean(~TPAK.p, des7.4)
svytotal(~TPAK.p, des7.4)## total SE
## bekerja 2202541 50266
## total SE
## TPT 23610 3659.3
## mean SE
## TPT 0.010606 0.0016
## total SE
## bekerja.p 2202541 50266
## total SE
## TPT.p 23305 3545
## mean SE
## TPT.p 0.01047 0.0015
## mean SE
## TPAK 0.71467 0.006
## total SE
## TPAK 2226152 51447
## mean SE
## TPAK.p 0.71458 0.006
## total SE
## TPAK.p 2225846 51460- Variabel Bekerja Skenario 1
mns3.1 = svytotal(~bekerja+bekerja.p, des7.4)
vcov(mns3.1)## bekerja bekerja.p
## bekerja 2526657817 2526657817
## bekerja.p 2526657817 2526657817var_xA = 53283^2
var_xB = 50266^2
myu_xA = 2131665
myu_xB = 2202541
myu_B = 2202541
var_B = 50266^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2526657817
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~bekerja, des7.4), level = 0.95)## [1] 0.4708887
## [1] 2169166
## [1] 2169166
## [1] 36563.67
## [1] 0.01685609
## [1] 2097502
## [1] 2240831
## 2.5 % 97.5 %
## bekerja 2104022 2301061- Variabel Pengangguran Skenario 1
mns3.2 = svytotal(~TPT+TPT.p, des7.3)
vcov(mns3.2)
#table(sakernas.tpt$pengangguran, sakernas.tpt$pengangguran.p)## TPT TPT.p
## TPT 12045461 12045461
## TPT.p 12045461 12045461var_xA = 2846^2
var_xB = 3545^2
myu_xA = 14924
myu_xB = 23305
myu_B = 23610
var_B = 3659.3^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 12045461
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~TPT, des7.3), level = 0.95)## [1] 0.6080797
## [1] 18208.68
## [1] 18725.19
## [1] 2523.859
## [1] 0.1347841
## [1] 13778.43
## [1] 23671.96
## 2.5 % 97.5 %
## TPT 15885.82 29490.55- Variabel Angkatan Kerja Skenario 1
mns3.3 = svytotal(~TPAK+TPAK.p, des7.4)
vcov(mns3.3)## TPAK TPAK.p
## TPAK 2646838138 2647177550
## TPAK.p 2647177550 2648139781var_xA = 53116^2
var_xB = 51460^2
myu_xA = 2146589
myu_xB = 2225846
myu_B = 2226152
var_B = 51447^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2647177550
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~TPAK, des7.4), level = 0.95)## [1] 0.4841686
## [1] 2187472
## [1] 2187792
## [1] 36953.69
## [1] 0.01689086
## [1] 2115363
## [1] 2260221
## 2.5 % 97.5 %
## TPAK 2125317 2326987- Variabel TPAK Skenario 1
mns3.5 = svymean(~TPAK+TPAK.p, des7.4)
vcov(mns3.5)## TPAK TPAK.p
## TPAK 0.00003566489 0.00003563725
## TPAK.p 0.00003563725 0.00003567382var_xA = 0.0065^2
var_xB = 0.006^2
myu_xA = 0.70354
myu_xB = 0.71458
myu_B = 0.71467
var_B = 0.006^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 3.563725e-05
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~TPAK, des7.4), level = 0.95)## [1] 0.4600639
## [1] 0.7095009
## [1] 0.7096421
## [1] 0.004446324
## [1] 0.006265586
## [1] 0.7009273
## [1] 0.7183569
## 2.5 % 97.5 %
## TPAK 0.7029696 0.7263794- Variabel TPT Skenario 1
mns3.4 = svymean(~TPT+TPT.p, des7.3)
vcov(mns3.4)## TPT TPT.p
## TPT 0.000002227262 0.000002227262
## TPT.p 0.000002227262 0.000002227262var_xA = 0.0013^2
var_xB = 0.0015^2
myu_xA = 0.0069522
myu_xB = 0.01047
myu_B = 0.010606
var_B = 0.0016^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2.227262e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~TPT, des7.3), level = 0.95)## [1] 0.571066
## [1] 0.008461104
## [1] 0.008617406
## [1] 0.001140588
## [1] 0.1323586
## [1] 0.006381854
## [1] 0.01085296
## 2.5 % 97.5 %
## TPT 0.007270833 0.01312094##4.1. Kabupaten Kulonprogo
susenas.tpt.01=filter(susenas.tpt, KODE_KAB == "01")
susenas_diy.01=filter(susenas_diy.15, KODE_KAB == "01")
des7.1.1 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas.tpt.01)
svytotal(~bekerja, des7.1.1)
svytotal(~pengangguran, des7.1.1)
svytotal(~TPT, des7.1.1)
svymean(~TPT, des7.1.1)
des7.2.1 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy.01)
svymean(~TPAK, des7.2.1)
svytotal(~TPAK, des7.2.1)## total SE
## bekerja 244796 9420.8
## total SE
## pengangguran 823.92 325.98
## total SE
## TPT 823.92 325.98
## mean SE
## TPT 0.0033545 0.0013
## mean SE
## TPAK 0.72814 0.0137
## total SE
## TPAK 245620 9440.6sakernas.tpt.01=filter(sakernas.tpt, KODE_KAB == "01")
sakernas2.01=filter(sakernas2, KODE_KAB == "01")
sakernas3.01=filter(sakernas3, KODE_KAB == "01")
sakernas_diy.01=filter(sakernas_diy, KODE_KAB == "01")
des7.3_1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.tpt.01)
des7.3.1_1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas2.01)
des7.3.2_1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas3.01)
des7.4_1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy.01)
svytotal(~bekerja, des7.4_1)
svytotal(~TPT, des7.3.1_1)
svymean(~TPT, des7.3.1_1)
svytotal(~bekerja.p, des7.4_1)
svytotal(~TPT.p, des7.3.2_1)
svymean(~TPT.p, des7.3.2_1)
svymean(~TPAK, des7.4_1)
svytotal(~TPAK, des7.4_1)
svymean(~TPAK.p, des7.4_1)
svytotal(~TPAK.p, des7.4_1)## total SE
## bekerja 292756 16900
## total SE
## TPT 1836.4 742.98
## mean SE
## TPT 0.0062338 0.0023
## total SE
## bekerja.p 292756 16900
## total SE
## TPT.p 2172.1 922.91
## mean SE
## TPT.p 0.007365 0.0029
## mean SE
## TPAK 0.76451 0.013
## total SE
## TPAK 294592 17345
## mean SE
## TPAK.p 0.76538 0.0129
## total SE
## TPAK.p 294928 17507- Variabel Bekerja Skenario 1
mns3.1.1 = svytotal(~bekerja+bekerja.p, des7.4_1)
vcov(mns3.1.1)## bekerja bekerja.p
## bekerja 285617650 285617650
## bekerja.p 285617650 285617650- Variabel bekerja skenario 1
var_xA = 9420.8^2
var_xB = 16900^2
myu_xA = 244796
myu_xB = 292756
myu_B = 292756
var_B = 16900^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 285617650
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~bekerja, des7.4_1), level = 0.95)## [1] 0.7629257
## [1] 256166.1
## [1] 256165.1
## [1] 8227.947
## [1] 0.0321197
## [1] 240038.3
## [1] 272291.9
## 2.5 % 97.5 %
## bekerja 259632.2 325879.8- Variabel Pengangguran Skenario 1
mns3.2.1 = svytotal(~TPT+TPT.p, des7.3_1)
vcov(mns3.2.1)## TPT TPT.p
## TPT 552022.5 552022.5
## TPT.p 552022.5 552022.5var_xA = 325.98^2
var_xB = 922.91^2
myu_xA = 823.92
myu_xB = 2172.1
myu_B = 1836.4
var_B = 742.98^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 552022.5
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~TPT, des7.3_1), level = 0.95)## [1] 0.8890813
## [1] 973.4583
## [1] 1059.567
## [1] 483.6727
## [1] 0.4564813
## [1] 111.5689
## [1] 2007.566
## 2.5 % 97.5 %
## TPT 380.2049 3292.642- Variabel Angkatan Kerja Skenario 1
mns3.3.1 = svytotal(~TPAK+TPAK.p, des7.4_1)
vcov(mns3.3.1)## TPAK TPAK.p
## TPAK 300860351 303620684
## TPAK.p 303620684 306493723var_xA = 9440.6^2
var_xB = 17507^2
myu_xA = 245620
myu_xB = 294928
myu_B = 294592
var_B = 17345^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 303620684
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~TPAK, des7.4_1), level = 0.95)## [1] 0.7747209
## [1] 256728.1
## [1] 256750.3
## [1] 8236.121
## [1] 0.03207833
## [1] 240607.5
## [1] 272893.1
## 2.5 % 97.5 %
## TPAK 260596.2 328588.6- Variabel TPAK Skenario 1
mns3.5.1 = svymean(~TPAK+TPAK.p, des7.4_1)
vcov(mns3.5.1)## TPAK TPAK.p
## TPAK 0.0001684726 0.0001675456
## TPAK.p 0.0001675456 0.0001673356var_xA = 0.0137^2
var_xB = 0.0129^2
myu_xA = 0.72814
myu_xB = 0.76538
myu_B = 0.76451
var_B = 0.013^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 0.0001675456
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~TPAK, des7.4_1), level = 0.95)## [1] 0.469952
## [1] 0.747879
## [1] 0.7468896
## [1] 0.009472291
## [1] 0.01268232
## [1] 0.7283239
## [1] 0.7654552
## 2.5 % 97.5 %
## TPAK 0.7390694 0.7899489- Variabel TPT Skenario 1
mns3.4.1 = svymean(~TPT+TPT.p, des7.3_1)
vcov(mns3.4.1)## TPT TPT.p
## TPT 0.000005361005 0.000005361005
## TPT.p 0.000005361005 0.000005361005var_xA = 0.0013^2
var_xB = 0.0029^2
myu_xA = 0.0033545
myu_xB = 0.007365
myu_B = 0.0062338
var_B = 0.0023^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 5.361005e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~TPT, des7.3_1), level = 0.95)## [1] 0.8326733
## [1] 0.004025564
## [1] 0.004105056
## [1] 0.001563464
## [1] 0.3808629
## [1] 0.001040668
## [1] 0.007169445
## 2.5 % 97.5 %
## TPT 0.001695706 0.01077185##4.2. Kabupaten Bantul
susenas.tpt.02=filter(susenas.tpt, KODE_KAB == "02")
susenas_diy.02=filter(susenas_diy.15, KODE_KAB == "02")
des7.1.2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas.tpt.02)
svytotal(~bekerja, des7.1.2)
svytotal(~pengangguran, des7.1.2)
svytotal(~TPT, des7.1.2)
svymean(~TPT, des7.1.2)
des7.2.2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy.02)
svymean(~TPAK, des7.2.2)
svytotal(~TPAK, des7.2.2)## total SE
## bekerja 559231 23685
## total SE
## pengangguran 7932.8 2338.8
## total SE
## TPT 7932.8 2338.8
## mean SE
## TPT 0.013987 0.0042
## mean SE
## TPAK 0.70574 0.0109
## total SE
## TPAK 567164 23495sakernas.tpt.02=filter(sakernas.tpt, KODE_KAB == "02")
sakernas2.02=filter(sakernas2, KODE_KAB == "02")
sakernas3.02=filter(sakernas3, KODE_KAB == "02")
sakernas_diy.02=filter(sakernas_diy, KODE_KAB == "02")
des7.3_2 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.tpt.02)
des7.3.1_2 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas2.02)
des7.3.2_2 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas3.02)
des7.4_2 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy.02)
svytotal(~bekerja, des7.4_2)
svytotal(~TPT, des7.3.1_2)
svymean(~TPT, des7.3.1_2)
svytotal(~bekerja.p, des7.4_2)
svytotal(~TPT.p, des7.3.2_2)
svymean(~TPT.p, des7.3.2_2)
svymean(~TPAK, des7.4_2)
svytotal(~TPAK, des7.4_2)
svymean(~TPAK.p, des7.4_2)
svytotal(~TPAK.p, des7.4_2)## total SE
## bekerja 564360 24064
## total SE
## TPT 7476.4 2107.1
## mean SE
## TPT 0.013074 0.0035
## total SE
## bekerja.p 564360 24064
## total SE
## TPT.p 7476.4 2107.1
## mean SE
## TPT.p 0.013074 0.0035
## mean SE
## TPAK 0.71919 0.0139
## total SE
## TPAK 571837 24712
## mean SE
## TPAK.p 0.71919 0.0139
## total SE
## TPAK.p 571837 24712- Variabel Bekerja Skenario 1
mns3.1.2 = svytotal(~bekerja+bekerja.p, des7.4_2)
vcov(mns3.1.2)## bekerja bekerja.p
## bekerja 579061549 579061549
## bekerja.p 579061549 579061549var_xA = 23685^2
var_xB = 24064^2
myu_xA = 559231
myu_xB = 564360
myu_B = 564360
var_B = 24064^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 579061549
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~bekerja, des7.4_2), level = 0.95)## [1] 0.5079368
## [1] 561754.8
## [1] 561754.9
## [1] 16880.66
## [1] 0.03004988
## [1] 528668.8
## [1] 594841
## 2.5 % 97.5 %
## bekerja 517196.2 611524.1- Variabel Pengangguran Skenario 1
mns3.2.2 = svytotal(~TPT+TPT.p, des7.3_2)
vcov(mns3.2.2)## TPT TPT.p
## TPT 4439702 4439702
## TPT.p 4439702 4439702var_xA = 2338.8^2
var_xB = 2107.1^2
myu_xA = 7932.8
myu_xB = 7476.4
myu_B = 7476.4
var_B = 2107.1^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 4439702
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~TPT, des7.3_2), level = 0.95)## [1] 0.4480257
## [1] 7680.879
## [1] 7680.871
## [1] 1565.518
## [1] 0.2038203
## [1] 4612.457
## [1] 10749.29
## 2.5 % 97.5 %
## TPT 3346.688 11606.21- Variabel Angkatan Kerja Skenario 1
mns3.3.2 = svytotal(~TPAK+TPAK.p, des7.4_2)
vcov(mns3.3.2)## TPAK TPAK.p
## TPAK 610691160 610691160
## TPAK.p 610691160 610691160var_xA = 23495^2
var_xB = 24712^2
myu_xA = 567164
myu_xB = 571837
myu_B = 571837
var_B = 24712^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 610691160
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~TPAK, des7.4_2), level = 0.95)## [1] 0.5252292
## [1] 569382.6
## [1] 569382.6
## [1] 17027.21
## [1] 0.02990469
## [1] 536009.2
## [1] 602755.9
## 2.5 % 97.5 %
## TPAK 523401.6 620271.6- Variabel TPAK Skenario 1
mns3.5.2 = svymean(~TPAK+TPAK.p, des7.4_2)
vcov(mns3.5.2)## TPAK TPAK.p
## TPAK 0.0001943775 0.0001943775
## TPAK.p 0.0001943775 0.0001943775var_xA = 0.0109^2
var_xB = 0.0139^2
myu_xA = 0.70574
myu_xB = 0.71919
myu_B = 0.71919
var_B = 0.0139^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 0.0001943775
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~TPAK, des7.4_2), level = 0.95)## [1] 0.6192231
## [1] 0.7108614
## [1] 0.7108111
## [1] 0.008492329
## [1] 0.01194738
## [1] 0.6941662
## [1] 0.7274561
## 2.5 % 97.5 %
## TPAK 0.6918673 0.7465186- Variabel TPT Skenario 1
mns3.4.2 = svymean(~TPT+TPT.p, des7.3_2)
vcov(mns3.4.2)## TPT TPT.p
## TPT 0.00001245426 0.00001245426
## TPT.p 0.00001245426 0.00001245426var_xA = 0.0042^2
var_xB = 0.0035^2
myu_xA = 0.013987
myu_xB = 0.013074
myu_B = 0.013074
var_B = 0.0035^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 1.245426e-05
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~TPT, des7.3_2), level = 0.95)## [1] 0.4098361
## [1] 0.01344818
## [1] 0.01345442
## [1] 0.002657195
## [1] 0.1974961
## [1] 0.008246317
## [1] 0.01866252
## 2.5 % 97.5 %
## TPT 0.006157622 0.01999128##4.3. Kabupaten Gunung Kidul
susenas.tpt.03=filter(susenas.tpt, KODE_KAB == "03")
susenas_diy.03=filter(susenas_diy.15, KODE_KAB == "03")
des7.1.3 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas.tpt.03)
svytotal(~bekerja, des7.1.3)
svytotal(~pengangguran, des7.1.3)
svytotal(~TPT, des7.1.3)
svymean(~TPT, des7.1.3)
des7.2.3 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy.03)
svymean(~TPAK, des7.2.3)
svytotal(~TPAK, des7.2.3)## total SE
## bekerja 456758 15909
## total SE
## pengangguran 1275.8 641.24
## total SE
## TPT 1275.8 641.24
## mean SE
## TPT 0.0027853 0.0014
## mean SE
## TPAK 0.77408 0.0109
## total SE
## TPAK 458034 15955sakernas.tpt.03=filter(sakernas.tpt, KODE_KAB == "03")
sakernas2.03=filter(sakernas2, KODE_KAB == "03")
sakernas3.03=filter(sakernas3, KODE_KAB == "03")
sakernas_diy.03=filter(sakernas_diy, KODE_KAB == "03")
des7.3_3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.tpt.03)
des7.3.1_3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas2.03)
des7.3.2_3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas3.03)
des7.4_3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy.03)
svytotal(~bekerja, des7.4_3)
svytotal(~TPT, des7.3.1_3)
svymean(~TPT, des7.3.1_3)
svytotal(~bekerja.p, des7.4_3)
svytotal(~TPT.p, des7.3.2_3)
svymean(~TPT.p, des7.3.2_3)
svymean(~TPAK, des7.4_3)
svytotal(~TPAK, des7.4_3)
svymean(~TPAK.p, des7.4_3)
svytotal(~TPAK.p, des7.4_3)## total SE
## bekerja 445957 15151
## total SE
## TPT 2570.7 888.63
## mean SE
## TPT 0.0057313 0.0019
## total SE
## bekerja.p 445957 15151
## total SE
## TPT.p 2851.6 1005.6
## mean SE
## TPT.p 0.0063538 0.0021
## mean SE
## TPAK 0.75505 0.0098
## total SE
## TPAK 448528 15616
## mean SE
## TPAK.p 0.75552 0.0097
## total SE
## TPAK.p 448809 15655- Variabel Bekerja Skenario 1
mns3.1.3 = svytotal(~bekerja+bekerja.p, des7.4_3)
vcov(mns3.1.3)## bekerja bekerja.p
## bekerja 229554879 229554879
## bekerja.p 229554879 229554879var_xA = 15909^2
var_xB = 15151^2
myu_xA = 456758
myu_xB = 445957
myu_B = 445957
var_B = 15151^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 229554879
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~bekerja, des7.4_3), level = 0.95)## [1] 0.4756101
## [1] 451094.1
## [1] 451094.1
## [1] 10971.47
## [1] 0.02432191
## [1] 429590
## [1] 472598.2
## 2.5 % 97.5 %
## bekerja 416261.8 475652.9- Variabel Pengangguran Skenario 1
mns3.2.3 = svytotal(~TPT+TPT.p, des7.3_3)
vcov(mns3.2.3)## TPT TPT.p
## TPT 789668.3 789668.3
## TPT.p 789668.3 789668.3var_xA = 641.24^2
var_xB = 1005.6^2
myu_xA = 1275.8
myu_xB = 2851.6
myu_B = 2570.7
var_B = 888.63^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 789668.3
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~TPT, des7.3_3), level = 0.95)## [1] 0.7109231
## [1] 1731.327
## [1] 1695.882
## [1] 592.6824
## [1] 0.3494833
## [1] 534.2242
## [1] 2857.539
## 2.5 % 97.5 %
## TPT 828.9712 4312.348- Variabel Angkatan Kerja Skenario 1
mns3.3.3 = svytotal(~TPAK+TPAK.p, des7.4_3)
vcov(mns3.3.3)## TPAK TPAK.p
## TPAK 243859230 244436172
## TPAK.p 244436172 245092059var_xA = 15955^2
var_xB = 15655^2
myu_xA = 458034
myu_xB = 448809
myu_B = 448528
var_B = 15616^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 244436172
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~TPAK, des7.4_3), level = 0.95)## [1] 0.4905102
## [1] 453334
## [1] 453041.1
## [1] 11147.89
## [1] 0.0246068
## [1] 431191.2
## [1] 474891
## 2.5 % 97.5 %
## TPAK 417921.3 479134.8- Variabel TPAK Skenario 1
mns3.5.3 = svymean(~TPAK+TPAK.p, des7.4_3)
vcov(mns3.5.3)## TPAK TPAK.p
## TPAK 0.00009631563 0.00009519356
## TPAK.p 0.00009519356 0.00009429198var_xA = 0.0109^2
var_xB = 0.0097^2
myu_xA = 0.77408
myu_xB = 0.75552
myu_B = 0.75505
var_B = 0.0098^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 9.519356e-05
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~TPAK, des7.4_3), level = 0.95)## [1] 0.4419446
## [1] 0.7637225
## [1] 0.7633487
## [1] 0.007312748
## [1] 0.009579827
## [1] 0.7490157
## [1] 0.7776817
## 2.5 % 97.5 %
## TPAK 0.7358146 0.7742849- Variabel TPT Skenario 1
mns3.4.3 = svymean(~TPT+TPT.p, des7.3_3)
vcov(mns3.4.3)## TPT TPT.p
## TPT 0.000003535039 0.000003535039
## TPT.p 0.000003535039 0.000003535039var_xA = 0.0014^2
var_xB = 0.0021^2
myu_xA = 0.0027853
myu_xB = 0.0063538
myu_B = 0.0057313
var_B = 0.0019^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 3.535039e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~TPT, des7.3_3), level = 0.95)## [1] 0.6923077
## [1] 0.0038833
## [1] 0.003750957
## [1] 0.001283833
## [1] 0.342268
## [1] 0.001234645
## [1] 0.006267268
## 2.5 % 97.5 %
## TPT 0.002046259 0.009416389##4.4. Kabupaten Sleman
susenas.tpt.04=filter(susenas.tpt, KODE_KAB == "04")
susenas_diy.04=filter(susenas_diy.15, KODE_KAB == "04")
des7.1.4 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas.tpt.04)
svytotal(~bekerja, des7.1.4)
svytotal(~pengangguran, des7.1.4)
svytotal(~TPT, des7.1.4)
svymean(~TPT, des7.1.4)
des7.2.4 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy.04)
svymean(~TPAK, des7.2.4)
svytotal(~TPAK, des7.2.4)## total SE
## bekerja 643825 41872
## total SE
## pengangguran 2626.9 1142
## total SE
## TPT 2626.9 1142
## mean SE
## TPT 0.0040635 0.0018
## mean SE
## TPAK 0.66868 0.0158
## total SE
## TPAK 646452 41731sakernas.tpt.04=filter(sakernas.tpt, KODE_KAB == "04")
sakernas2.04=filter(sakernas2, KODE_KAB == "04")
sakernas3.04=filter(sakernas3, KODE_KAB == "04")
sakernas_diy.04=filter(sakernas_diy, KODE_KAB == "04")
des7.3_4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.tpt.04)
des7.3.1_4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas2.04)
des7.3.2_4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas3.04)
des7.4_4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy.04)
svytotal(~bekerja, des7.4_4)
svytotal(~TPT, des7.3.1_4)
svymean(~TPT, des7.3.1_4)
svytotal(~bekerja.p, des7.4_4)
svytotal(~TPT.p, des7.3.2_4)
svymean(~TPT.p, des7.3.2_4)
svymean(~TPAK, des7.4_4)
svytotal(~TPAK, des7.4_4)
svymean(~TPAK.p, des7.4_4)
svytotal(~TPAK.p, des7.4_4)## total SE
## bekerja 633981 32009
## total SE
## TPT 6338.8 2298.9
## mean SE
## TPT 0.0098995 0.0035
## total SE
## bekerja.p 633981 32009
## total SE
## TPT.p 5823.2 2011.1
## mean SE
## TPT.p 0.0091015 0.003
## mean SE
## TPAK 0.68804 0.0121
## total SE
## TPAK 640320 32710
## mean SE
## TPAK.p 0.68749 0.012
## total SE
## TPAK.p 639804 32628- Variabel Bekerja Skenario 1
mns3.1.4 = svytotal(~bekerja+bekerja.p, des7.4_4)
vcov(mns3.1.4)## bekerja bekerja.p
## bekerja 1024605312 1024605312
## bekerja.p 1024605312 1024605312var_xA = 41872^2
var_xB = 32009^2
myu_xA = 643825
myu_xB = 633981
myu_B = 633981
var_B = 32009^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 1024605312
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~bekerja, des7.4_4), level = 0.95)## [1] 0.3688391
## [1] 637611.9
## [1] 637612
## [1] 25429.33
## [1] 0.03988214
## [1] 587770.5
## [1] 687453.4
## 2.5 % 97.5 %
## bekerja 571243.2 696718- Variabel Pengangguran Skenario 1
mns3.2.4 = svytotal(~TPT+TPT.p, des7.3_4)
vcov(mns3.2.4)## TPT TPT.p
## TPT 4044556 4044556
## TPT.p 4044556 4044556var_xA = 1142^2
var_xB = 2011.1^2
myu_xA = 2626.9
myu_xB = 5823.2
myu_B = 6338.8
var_B = 2298.9^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 4044556
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~TPT, des7.3_4), level = 0.95)## [1] 0.7561712
## [1] 3406.25
## [1] 3921.83
## [1] 1492.159
## [1] 0.3804752
## [1] 997.198
## [1] 6846.463
## 2.5 % 97.5 %
## TPT 1881.462 9764.862- Variabel Angkatan Kerja Skenario 1
mns3.3.4 = svytotal(~TPAK+TPAK.p, des7.4_4)
vcov(mns3.3.4)## TPAK TPAK.p
## TPAK 1069967033 1067135223
## TPAK.p 1067135223 1064569330var_xA = 41731^2
var_xB = 32628^2
myu_xA = 646452
myu_xB = 639804
myu_B = 640320
var_B = 32710^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 1067135223
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~TPAK, des7.4_4), level = 0.95)## [1] 0.3793879
## [1] 642326.2
## [1] 642848.2
## [1] 25770.46
## [1] 0.04008794
## [1] 592338.1
## [1] 693358.3
## 2.5 % 97.5 %
## TPAK 576208.4 704430.6- Variabel TPAK Skenario 1
mns3.5.4 = svymean(~TPAK+TPAK.p, des7.4_4)
vcov(mns3.5.4)## TPAK TPAK.p
## TPAK 0.0001457487 0.0001442317
## TPAK.p 0.0001442317 0.0001430196var_xA = 0.0158^2
var_xB = 0.012^2
myu_xA = 0.66868
myu_xB = 0.68749
myu_B = 0.68804
var_B = 0.0121^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 0.0001442317
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~TPAK, des7.4_4), level = 0.95)## [1] 0.3658165
## [1] 0.680609
## [1] 0.6811479
## [1] 0.009672785
## [1] 0.01420071
## [1] 0.6621893
## [1] 0.7001066
## 2.5 % 97.5 %
## TPAK 0.6643778 0.7117017- Variabel TPT Skenario 1
mns3.4.4 = svymean(~TPT+TPT.p, des7.3_4)
vcov(mns3.4.4)## TPT TPT.p
## TPT 0.000009117403 0.000009117403
## TPT.p 0.000009117403 0.000009117403var_xA = 0.0018^2
var_xB = 0.003^2
myu_xA = 0.0040635
myu_xB = 0.0091015
myu_B = 0.0098995
var_B = 0.0035^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 9.117403e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~TPT, des7.3_4), level = 0.95)## [1] 0.7352941
## [1] 0.005397088
## [1] 0.006146765
## [1] 0.002336359
## [1] 0.3800958
## [1] 0.001567501
## [1] 0.01072603
## 2.5 % 97.5 %
## TPT 0.003183362 0.0150196##4.5. Kota Yogyakarta
susenas.tpt.05=filter(susenas.tpt, KODE_KAB == "71")
susenas_diy.05=filter(susenas_diy.15, KODE_KAB == "71")
des7.1.5 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas.tpt.05)
svytotal(~bekerja, des7.1.5)
svytotal(~pengangguran, des7.1.5)
svytotal(~TPT, des7.1.5)
svymean(~TPT, des7.1.5)
des7.2.5 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy.05)
svymean(~TPAK, des7.2.5)
svytotal(~TPAK, des7.2.5)## total SE
## bekerja 227055 13528
## total SE
## pengangguran 2264.2 898.89
## total SE
## TPT 2264.2 898.89
## mean SE
## TPT 0.0098734 0.0039
## mean SE
## TPAK 0.65203 0.0169
## total SE
## TPAK 229320 13568sakernas.tpt.05=filter(sakernas.tpt, KODE_KAB == "71")
sakernas2.05=filter(sakernas2, KODE_KAB == "71")
sakernas3.05=filter(sakernas3, KODE_KAB == "71")
sakernas_diy.05=filter(sakernas_diy, KODE_KAB == "71")
des7.3_5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.tpt.05)
des7.3.1_5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas2.05)
des7.3.2_5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas3.05)
des7.4_5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy.05)
svytotal(~bekerja, des7.4_5)
svytotal(~TPT, des7.3.1_5)
svymean(~TPT, des7.3.1_5)
svytotal(~bekerja.p, des7.4_5)
svytotal(~TPT.p, des7.3.2_5)
svymean(~TPT.p, des7.3.2_5)
svymean(~TPAK, des7.4_5)
svytotal(~TPAK, des7.4_5)
svymean(~TPAK.p, des7.4_5)
svytotal(~TPAK.p, des7.4_5)## total SE
## bekerja 265487 20195
## total SE
## TPT 5388 1524.6
## mean SE
## TPT 0.019891 0.0054
## total SE
## bekerja.p 265487 20195
## total SE
## TPT.p 4981.5 1489.8
## mean SE
## TPT.p 0.018418 0.0053
## mean SE
## TPAK 0.66101 0.0164
## total SE
## TPAK 270875 20529
## mean SE
## TPAK.p 0.66001 0.0169
## total SE
## TPAK.p 270469 20525- Variabel Bekerja Skenario 1
mns3.1.5 = svytotal(~bekerja+bekerja.p, des7.4_5)
vcov(mns3.1.5)## bekerja bekerja.p
## bekerja 407818426 407818426
## bekerja.p 407818426 407818426var_xA = 13528^2
var_xB = 20195^2
myu_xA = 227055
myu_xB = 265487
myu_B = 265487
var_B = 20195^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 407818426
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~bekerja, des7.4_5), level = 0.95)## [1] 0.6902625
## [1] 238958.8
## [1] 238960.1
## [1] 11240.54
## [1] 0.04703941
## [1] 216928.6
## [1] 260991.6
## 2.5 % 97.5 %
## bekerja 225906.7 305067.8- Variabel Pengangguran Skenario 1
mns3.2.5 = svytotal(~TPT+TPT.p, des7.3_5)
vcov(mns3.2.5)## TPT TPT.p
## TPT 2219512 2219512
## TPT.p 2219512 2219512var_xA = 898.89^2
var_xB = 1489.8^2
myu_xA = 2264.2
myu_xB = 4981.5
myu_B = 5388
var_B = 1524.6^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2219512
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~TPT, des7.3_5), level = 0.95)## [1] 0.7331127
## [1] 2989.413
## [1] 3395.906
## [1] 835.0131
## [1] 0.2458882
## [1] 1759.28
## [1] 5032.531
## 2.5 % 97.5 %
## TPT 2061.533 7901.453- Variabel Angkatan Kerja Skenario 1
mns3.3.5 = svytotal(~TPAK+TPAK.p, des7.4_5)
vcov(mns3.3.5)## TPAK TPAK.p
## TPAK 421460364 421294311
## TPAK.p 421294311 421293510var_xA = 13568^2
var_xB = 20525^2
myu_xA = 229320
myu_xB = 270469
myu_B = 270875
var_B = 20529^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 421294311
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~TPAK, des7.4_5), level = 0.95)## [1] 0.6959021
## [1] 241833.3
## [1] 242238.1
## [1] 11324.63
## [1] 0.04675001
## [1] 220041.8
## [1] 264434.3
## 2.5 % 97.5 %
## TPAK 230638.2 311112.3- Variabel TPAK Skenario 1
mns3.5.5 = svymean(~TPAK+TPAK.p, des7.4_5)
vcov(mns3.5.5)## TPAK TPAK.p
## TPAK 0.0002696273 0.0002772316
## TPAK.p 0.0002772316 0.0002857993var_xA = 0.0169^2
var_xB = 0.0169^2
myu_xA = 0.65203
myu_xB = 0.66001
myu_B = 0.66101
var_B = 0.0164^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 0.0002772316
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~TPAK, des7.4_5), level = 0.95)## [1] 0.5
## [1] 0.65602
## [1] 0.657137
## [1] 0.01159355
## [1] 0.01764252
## [1] 0.6344137
## [1] 0.6798604
## 2.5 % 97.5 %
## TPAK 0.6288234 0.6931899- Variabel TPT Skenario 1
mns3.4.5 = svymean(~TPT+TPT.p, des7.3_5)
vcov(mns3.4.5)## TPT TPT.p
## TPT 0.00002834268 0.00002834268
## TPT.p 0.00002834268 0.00002834268var_xA = 0.0039^2
var_xB = 0.0053^2
myu_xA = 0.0098734
myu_xB = 0.018418
myu_B = 0.019891
var_B = 0.0054^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2.834268e-05
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~TPT, des7.3_5), level = 0.95)## [1] 0.6487298
## [1] 0.01287486
## [1] 0.014298
## [1] 0.003256972
## [1] 0.2277921
## [1] 0.007914337
## [1] 0.02068167
## 2.5 % 97.5 %
## TPT 0.007983572 0.02885242##5. Skenario 2 (Berdasarkan Hasil Prediksi Jenis Kegiatan)
susenas_diy.15$TPAK = ifelse(susenas_diy.15$jk == "1" | susenas_diy.15$jk=="2", 1, 0)
susenas_diy.15$bekerja = ifelse(susenas_diy.15$jk == "1", 1, 0)
susenas.tpt1 = susenas_diy.15 %>% filter(jk=="1" | jk=="2")
susenas.tpt1$TPT[susenas.tpt1$jk=="2"]=1
susenas.tpt1$TPT[susenas.tpt1$jk=="1"]=0des7.1.1 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas.tpt1)
svytotal(~TPT, des7.1.1)
svymean(~TPT, des7.1.1)
des7.2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy.15)
svymean(~TPAK, des7.2)
svytotal(~TPAK, des7.2)
svytotal(~bekerja, des7.2)## total SE
## TPT 61879 5805.8
## mean SE
## TPT 0.029952 0.0027
## mean SE
## TPAK 0.67712 0.0068
## total SE
## TPAK 2065977 52622
## total SE
## bekerja 2004098 51549sakernas_diy$TPAK = ifelse(sakernas_diy$jk == "1" | sakernas_diy$jk=="2", 1, 0)
sakernas_diy$bekerja = ifelse(sakernas_diy$jk == "1", 1, 0)
sakernas5 = sakernas_diy %>% filter(jk=="1" | jk=="2")
sakernas5$TPT[sakernas5$jk=="2"]=1
sakernas5$TPT[sakernas5$jk=="1"]=0
sakernas_diy$TPAK.p = ifelse(sakernas_diy$jk.p == "1" | sakernas_diy$jk.p=="2", 1, 0)
sakernas_diy$bekerja.p = ifelse(sakernas_diy$jk.p == "1", 1, 0)
sakernas6 = sakernas_diy %>% filter(jk.p=="1" | jk.p=="2")
sakernas6$TPT.p[sakernas6$jk.p=="2"]=1
sakernas6$TPT.p[sakernas6$jk.p=="1"]=0
sakernas.tpt1 <- sakernas5[(sakernas5$id_unik %in% sakernas6$id_unik), ]
sakernas.tpt1$TPT.p[sakernas.tpt1$jk.p=="2"]=1
sakernas.tpt1$TPT.p[sakernas.tpt1$jk.p=="1"]=0des7.3.3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.tpt1)
des7.3.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas5)
des7.3.5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas6)
des7.3.6 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy)
svytotal(~TPT, des7.3.4)
svymean(~TPT, des7.3.4)
svytotal(~TPT.p, des7.3.5)
svymean(~TPT.p, des7.3.5)
svytotal(~bekerja, des7.3.6)
svytotal(~bekerja.p, des7.3.6)
svytotal(~TPAK, des7.3.6)
svytotal(~TPAK.p, des7.3.6)
svymean(~TPAK, des7.3.6)
svymean(~TPAK.p, des7.3.6)## total SE
## TPT 95833 7243.6
## mean SE
## TPT 0.043888 0.0031
## total SE
## TPT.p 71916 5357.9
## mean SE
## TPT.p 0.034107 0.0025
## total SE
## bekerja 2087771 50155
## total SE
## bekerja.p 2036638 48505
## total SE
## TPAK 2183605 51953
## total SE
## TPAK.p 2108554 49530
## mean SE
## TPAK 0.70102 0.0061
## mean SE
## TPAK.p 0.67692 0.0061#1. Variabel Bekerja Skenario 2
mns3.1.1 = svytotal(~bekerja+bekerja.p, des7.3.6)
vcov(mns3.1.1)## bekerja bekerja.p
## bekerja 2515568799 2414988207
## bekerja.p 2414988207 2352738676var_xA = 51549^2
var_xB = 48505^2
myu_xA = 2004098
myu_xB = 2036638
myu_B = 2087771
var_B = 50155^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2414988207
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~bekerja, des7.3.6), level = 0.95)## [1] 0.4696046
## [1] 2021357
## [1] 2072086
## [1] 36761.75
## [1] 0.01774143
## [1] 2000033
## [1] 2144139
## 2.5 % 97.5 %
## bekerja 1989468 2186074#2. Variabel Pengangguran Skenario 2
mns3.2.1 = svytotal(~TPT+TPT.p, des7.3.3)
vcov(mns3.2.1)
#table(sakernas.tpt1$TPT, sakernas.tpt1$TPT)## TPT TPT.p
## TPT 13704444 9185408
## TPT.p 9185408 24759717var_xA = 5805.8^2
var_xB = 5357.9^2
myu_xA = 61879
myu_xB = 71916
myu_B = 95833
var_B = 7243.6^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 9185408
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~TPT, des7.3.3), level = 0.95)## [1] 0.4599434
## [1] 67299.55
## [1] 94355.87
## [1] 7149.681
## [1] 0.07577357
## [1] 80342.5
## [1] 108369.2
## 2.5 % 97.5 %
## TPT 20074.88 34586.27#3. Variabel Angkatan Kerja Skenario 2
mns3.3.1 = svytotal(~TPAK+TPAK.p, des7.3.6)
vcov(mns3.3.1)## TPAK TPAK.p
## TPAK 2699099641 2543308390
## TPAK.p 2543308390 2453224583var_xA = 52622^2
var_xB = 49530^2
myu_xA = 2065977
myu_xB = 2108554
myu_B = 2183605
var_B = 51953^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2543308390
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svytotal(~TPAK, des7.3.6), level = 0.95)## [1] 0.4697591
## [1] 2088553
## [1] 2162870
## [1] 38216.47
## [1] 0.01766934
## [1] 2087965
## [1] 2237774
## 2.5 % 97.5 %
## TPAK 2081779 2285430#4. Variabel TPAK Skenario 2
mns3.4.1 = svymean(~TPAK+TPAK.p, des7.3.6)
vcov(mns3.4.1)## TPAK TPAK.p
## TPAK 0.00003781874 0.00003462845
## TPAK.p 0.00003462845 0.00003760976var_xA = 0.0068^2
var_xB = 0.0061^2
myu_xA = 0.67712
myu_xB = 0.67692
myu_B = 0.70102
var_B = 0.0061^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 3.462845e-05
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~TPAK, des7.3.6), level = 0.95)## [1] 0.4458957
## [1] 0.6770092
## [1] 0.701103
## [1] 0.00477918
## [1] 0.006816659
## [1] 0.6917358
## [1] 0.7104702
## 2.5 % 97.5 %
## TPAK 0.6889621 0.7130685#5. Variabel TPT Skenario 2
mns3.4.1 = svymean(~TPT+TPT.p, des7.3.3)
vcov(mns3.4.1)## TPT TPT.p
## TPT 0.000003172067 0.000002049135
## TPT.p 0.000002049135 0.000005419683var_xA = 0.0027^2
var_xB = 0.0025^2
myu_xA = 0.029952
myu_xB = 0.034107
myu_B = 0.043888
var_B = 0.0031^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2.049135e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~TPT, des7.3.3), level = 0.95)## [1] 0.4615953
## [1] 0.03218907
## [1] 0.04325918
## [1] 0.003049571
## [1] 0.07049535
## [1] 0.03728203
## [1] 0.04923634
## 2.5 % 97.5 %
## TPT 0.009662869 0.016644385.1. Kabupaten Kulonprogo
susenas_kulonprogo.15=filter(susenas_diy.15, KODE_KAB == "01")
susenas_kulonprogo.tpt=filter(susenas.tpt1, KODE_KAB == "01")des7.1.1 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_kulonprogo.tpt)
svytotal(~TPT, des7.1.1)
svymean(~TPT, des7.1.1)
des7.2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_kulonprogo.15)
svymean(~TPAK, des7.2)
svytotal(~TPAK, des7.2)
svytotal(~bekerja, des7.2)## total SE
## TPT 3426.4 856.49
## mean SE
## TPT 0.014279 0.0035
## mean SE
## TPAK 0.71135 0.0132
## total SE
## TPAK 239957 9168.8
## total SE
## bekerja 236530 9103.3sakernas5_kulonprogo=filter(sakernas5, KODE_KAB == "01")
sakernas6_kulonprogo=filter(sakernas6, KODE_KAB == "01")
sakernas.diy_kulonprogo=filter(sakernas_diy, KODE_KAB == "01")
sakernas.tpt1_kulonprogo=filter(sakernas.tpt1, KODE_KAB == "01")des7.3.3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.tpt1_kulonprogo)
des7.3.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas5_kulonprogo)
des7.3.5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas6_kulonprogo)
des7.3.6 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.diy_kulonprogo)
svytotal(~TPT, des7.3.4)
svymean(~TPT, des7.3.4)
svytotal(~TPT.p, des7.3.5)
svymean(~TPT.p, des7.3.5)
svytotal(~bekerja, des7.3.6)
svytotal(~bekerja.p, des7.3.6)
svytotal(~TPAK, des7.3.6)
svytotal(~TPAK.p, des7.3.6)
svymean(~TPAK, des7.3.6)
svymean(~TPAK.p, des7.3.6)## total SE
## TPT 7103.2 1333.9
## mean SE
## TPT 0.024668 0.0043
## total SE
## TPT.p 6670.3 1270.5
## mean SE
## TPT.p 0.02348 0.0044
## total SE
## bekerja 280845 17023
## total SE
## bekerja.p 277414 16569
## total SE
## TPAK 287948 17495
## total SE
## TPAK.p 284084 16820
## mean SE
## TPAK 0.74727 0.0132
## mean SE
## TPAK.p 0.73724 0.0125#1. Variabel Bekerja Skenario 2
mns3.1.1 = svytotal(~bekerja+bekerja.p, des7.3.6)
vcov(mns3.1.1)## bekerja bekerja.p
## bekerja 289773745 280709104
## bekerja.p 280709104 274522443var_xA = 9103.3^2
var_xB = 16569^2
myu_xA = 236530
myu_xB = 277414
myu_B = 280845
var_B = 17023^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 280709104
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.7681319
## [1] 246009.7
## [1] 248734.1
## [1] 8325.21
## [1] 0.03347033
## [1] 232416.6
## [1] 265051.5#2. Variabel Pengangguran Skenario 2
mns3.2.1 = svytotal(~TPT+TPT.p, des7.3.3)
vcov(mns3.2.1)
#table(sakernas.tpt1$TPT, sakernas.tpt1$TPT)## TPT TPT.p
## TPT 370704.5 133312
## TPT.p 133312.0 1160112var_xA = 856.49^2
var_xB = 1270.5^2
myu_xA = 3426.4
myu_xB = 6670.3
myu_B = 7103.2
var_B = 1333.9^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 133312
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.6875406
## [1] 4439.987
## [1] 6919.002
## [1] 1331.059
## [1] 0.1923774
## [1] 4310.125
## [1] 9527.878#3. Variabel Angkatan Kerja Skenario 2
mns3.3.1 = svytotal(~TPAK+TPAK.p, des7.3.6)
vcov(mns3.3.1)## TPAK TPAK.p
## TPAK 306062634 292759993
## TPAK.p 292759993 282914490var_xA = 9168.8^2
var_xB = 16820^2
myu_xA = 239957
myu_xB = 284084
myu_B = 287948
var_B = 17495^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 292759993
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.770922
## [1] 250065.5
## [1] 252745.4
## [1] 8516.099
## [1] 0.03369437
## [1] 236053.9
## [1] 269437#4. Variabel TPAK Skenario 2
mns3.4.1 = svymean(~TPAK+TPAK.p, des7.3.6)
vcov(mns3.4.1)## TPAK TPAK.p
## TPAK 0.0001742854 0.0001538663
## TPAK.p 0.0001538663 0.0001552137var_xA = 0.0132^2
var_xB = 0.0125^2
myu_xA = 0.71135
myu_xB = 0.73724
myu_B = 0.74727
var_B = 0.0132^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 0.0001538663
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.4727828
## [1] 0.7249997
## [1] 0.7352164
## [1] 0.01012938
## [1] 0.01377742
## [1] 0.7153628
## [1] 0.75507#5. Variabel TPT Skenario 2
mns3.4.1 = svymean(~TPT+TPT.p, des7.3.3)
vcov(mns3.4.1)## TPT TPT.p
## TPT 0.000005056066 0.000001697217
## TPT.p 0.000001697217 0.000013669045var_xA = 0.0035^2
var_xB = 0.0044^2
myu_xA = 0.014279
myu_xB = 0.02348
myu_B = 0.024668
var_B = 0.0043^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 1.697217e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.6124644
## [1] 0.01784471
## [1] 0.02417398
## [1] 0.004289391
## [1] 0.1774384
## [1] 0.01576677
## [1] 0.032581185.2. Kabupaten Bantul
susenas_bantul.15=filter(susenas_diy.15, KODE_KAB == "02")
susenas_bantul.tpt=filter(susenas.tpt1, KODE_KAB == "02")des7.1.1 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_bantul.tpt)
svytotal(~TPT, des7.1.1)
svymean(~TPT, des7.1.1)
des7.2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_bantul.15)
svymean(~TPAK, des7.2)
svytotal(~TPAK, des7.2)
svytotal(~bekerja, des7.2)## total SE
## TPT 16270 2603.8
## mean SE
## TPT 0.029747 0.0046
## mean SE
## TPAK 0.68059 0.0124
## total SE
## TPAK 546952 23717
## total SE
## bekerja 530682 23213sakernas5_bantul=filter(sakernas5, KODE_KAB == "02")
sakernas6_bantul=filter(sakernas6, KODE_KAB == "02")
sakernas.diy_bantul=filter(sakernas_diy, KODE_KAB == "02")
sakernas.tpt1_bantul=filter(sakernas.tpt1, KODE_KAB == "02")des7.3.3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.tpt1_bantul)
des7.3.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas5_bantul)
des7.3.5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas6_bantul)
des7.3.6 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.diy_bantul)
svytotal(~TPT, des7.3.4)
svymean(~TPT, des7.3.4)
svytotal(~TPT.p, des7.3.5)
svymean(~TPT.p, des7.3.5)
svytotal(~bekerja, des7.3.6)
svytotal(~bekerja.p, des7.3.6)
svytotal(~TPAK, des7.3.6)
svytotal(~TPAK.p, des7.3.6)
svymean(~TPAK, des7.3.6)
svymean(~TPAK.p, des7.3.6)## total SE
## TPT 22152 3822.4
## mean SE
## TPT 0.039308 0.0062
## total SE
## TPT.p 12197 2505.1
## mean SE
## TPT.p 0.022417 0.0044
## total SE
## bekerja 541402 23649
## total SE
## bekerja.p 531883 22576
## total SE
## TPAK 563555 25033
## total SE
## TPAK.p 544080 23294
## mean SE
## TPAK 0.70878 0.0141
## mean SE
## TPAK.p 0.68428 0.0138#1. Variabel Bekerja Skenario 2
mns3.1.1 = svytotal(~bekerja+bekerja.p, des7.3.6)
vcov(mns3.1.1)## bekerja bekerja.p
## bekerja 559255604 529381755
## bekerja.p 529381755 509655935var_xA = 23213^2
var_xB = 22576^2
myu_xA = 530682
myu_xB = 531883
myu_B = 541402
var_B = 23649^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 529381755
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.4860911
## [1] 531299.2
## [1] 540795.6
## [1] 17087.96
## [1] 0.03159781
## [1] 507303.2
## [1] 574288#2. Variabel Pengangguran Skenario 2
mns3.2.1 = svytotal(~TPT+TPT.p, des7.3.3)
vcov(mns3.2.1)
#table(sakernas.tpt1$TPT, sakernas.tpt1$TPT)## TPT TPT.p
## TPT 2432548 1488534
## TPT.p 1488534 6127965var_xA = 2603.8^2
var_xB = 2505.1^2
myu_xA = 16270
myu_xB = 12197
myu_B = 22152
var_B = 3822.4^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 1488534
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.480688
## [1] 14154.84
## [1] 22616.39
## [1] 3800.135
## [1] 0.1680257
## [1] 15168.13
## [1] 30064.66#3. Variabel Angkatan Kerja Skenario 2
mns3.3.1 = svytotal(~TPAK+TPAK.p, des7.3.6)
vcov(mns3.3.1)## TPAK TPAK.p
## TPAK 626664676 575344532
## TPAK.p 575344532 542626957var_xA = 23717^2
var_xB = 23294^2
myu_xA = 546952
myu_xB = 544080
myu_B = 563555
var_B = 25033^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 575344532
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.4910028
## [1] 545490.2
## [1] 565050.2
## [1] 18086.27
## [1] 0.03200825
## [1] 529601.1
## [1] 600499.3#4. Variabel TPAK Skenario 2
mns3.4.1 = svymean(~TPAK+TPAK.p, des7.3.6)
vcov(mns3.4.1)## TPAK TPAK.p
## TPAK 0.0001997467 0.0001825111
## TPAK.p 0.0001825111 0.0001912266var_xA = 0.0124^2
var_xB = 0.0138^2
myu_xA = 0.68059
myu_xB = 0.68428
myu_B = 0.70878
var_B = 0.0141^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 0.0001825111
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.553283
## [1] 0.6822384
## [1] 0.7068234
## [1] 0.01010119
## [1] 0.01429096
## [1] 0.6870251
## [1] 0.7266217#5. Variabel TPT Skenario 2
mns3.4.1 = svymean(~TPT+TPT.p, des7.3.3)
vcov(mns3.4.1)## TPT TPT.p
## TPT 0.000008356458 0.00000459841
## TPT.p 0.000004598410 0.00001891729var_xA = 0.0046^2
var_xB = 0.0044^2
myu_xA = 0.029747
myu_xB = 0.022417
myu_B = 0.039308
var_B = 0.0062^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 4.598410e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.4777887
## [1] 0.02591919
## [1] 0.04013984
## [1] 0.006157771
## [1] 0.153408
## [1] 0.02807061
## [1] 0.052209085.3. Kabupaten Gunung Kidul
susenas_gunungkidul.15=filter(susenas_diy.15, KODE_KAB == "03")
susenas_gunungkidul.tpt=filter(susenas.tpt1, KODE_KAB == "03")des7.1.1 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_gunungkidul.tpt)
svytotal(~TPT, des7.1.1)
svymean(~TPT, des7.1.1)
des7.2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_gunungkidul.15)
svymean(~TPAK, des7.2)
svytotal(~TPAK, des7.2)
svytotal(~bekerja, des7.2)## total SE
## TPT 21509 3370.9
## mean SE
## TPT 0.048526 0.0071
## mean SE
## TPAK 0.7491 0.011
## total SE
## TPAK 443251 15806
## total SE
## bekerja 421742 14830sakernas5_gunungkidul=filter(sakernas5, KODE_KAB == "03")
sakernas6_gunungkidul=filter(sakernas6, KODE_KAB == "03")
sakernas.diy_gunungkidul=filter(sakernas_diy, KODE_KAB == "03")
sakernas.tpt1_gunungkidul=filter(sakernas.tpt1, KODE_KAB == "03")des7.3.3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.tpt1_gunungkidul)
des7.3.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas5_gunungkidul)
des7.3.5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas6_gunungkidul)
des7.3.6 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.diy_gunungkidul)
svytotal(~TPT, des7.3.4)
svymean(~TPT, des7.3.4)
svytotal(~TPT.p, des7.3.5)
svymean(~TPT.p, des7.3.5)
svytotal(~bekerja, des7.3.6)
svytotal(~bekerja.p, des7.3.6)
svytotal(~TPAK, des7.3.6)
svytotal(~TPAK.p, des7.3.6)
svymean(~TPAK, des7.3.6)
svymean(~TPAK.p, des7.3.6)## total SE
## TPT 22944 3427.2
## mean SE
## TPT 0.051959 0.0074
## total SE
## TPT.p 20448 2802.7
## mean SE
## TPT.p 0.048362 0.0062
## total SE
## bekerja 418633 14980
## total SE
## bekerja.p 402354 13981
## total SE
## TPAK 441577 15656
## total SE
## TPAK.p 422802 14836
## mean SE
## TPAK 0.74335 0.0098
## mean SE
## TPAK.p 0.71174 0.0098#1. Variabel Bekerja Skenario 2
mns3.1.1 = svytotal(~bekerja+bekerja.p, des7.3.6)
vcov(mns3.1.1)## bekerja bekerja.p
## bekerja 224391669 206464033
## bekerja.p 206464033 195468246var_xA = 14830^2
var_xB = 13981^2
myu_xA = 421742
myu_xB = 402354
myu_B = 418633
var_B = 14980^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 206464033
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.4705577
## [1] 411477.2
## [1] 428269.4
## [1] 11035.49
## [1] 0.02576763
## [1] 406639.8
## [1] 449898.9#2. Variabel Pengangguran Skenario 2
mns3.2.1 = svytotal(~TPT+TPT.p, des7.3.3)
vcov(mns3.2.1)
#table(sakernas.tpt1$TPT, sakernas.tpt1$TPT)## TPT TPT.p
## TPT 4486137 4004242
## TPT.p 4004242 7439857var_xA = 3370.9^2
var_xB = 2802.7^2
myu_xA = 21509
myu_xB = 20448
myu_B = 22944
var_B = 3427.2^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 4004242
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.408736
## [1] 20881.67
## [1] 23165.07
## [1] 3303.238
## [1] 0.1425957
## [1] 16690.72
## [1] 29639.42#3. Variabel Angkatan Kerja Skenario 2
mns3.3.1 = svytotal(~TPAK+TPAK.p, des7.3.6)
vcov(mns3.3.1)## TPAK TPAK.p
## TPAK 245125984 228741954
## TPAK.p 228741954 220096413var_xA = 15806^2
var_xB = 14836^2
myu_xA = 443251
myu_xB = 422802
myu_B = 441577
var_B = 15656^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 228741954
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.4683758
## [1] 432379.8
## [1] 451530.6
## [1] 11565.9
## [1] 0.02561488
## [1] 428861.4
## [1] 474199.7#4. Variabel TPAK Skenario 2
mns3.4.1 = svymean(~TPAK+TPAK.p, des7.3.6)
vcov(mns3.4.1)## TPAK TPAK.p
## TPAK 0.00009535862 0.00008573822
## TPAK.p 0.00008573822 0.00009600055var_xA = 0.011^2
var_xB = 0.0098^2
myu_xA = 0.7491
myu_xB = 0.71174
myu_B = 0.74335
var_B = 0.0098^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 8.573822e-05
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.4424991
## [1] 0.7282718
## [1] 0.7581085
## [1] 0.007884825
## [1] 0.01040066
## [1] 0.7426542
## [1] 0.7735627#5. Variabel TPT Skenario 2
mns3.4.1 = svymean(~TPT+TPT.p, des7.3.3)
vcov(mns3.4.1)## TPT TPT.p
## TPT 0.00002459235 0.00002028879
## TPT.p 0.00002028879 0.00003607211var_xA = 0.0071^2
var_xB = 0.0062^2
myu_xA = 0.048526
myu_xB = 0.048362
myu_B = 0.051959
var_B = 0.0074^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2.028879e-05
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.4326393
## [1] 0.04843295
## [1] 0.05199645
## [1] 0.007080048
## [1] 0.1361641
## [1] 0.03811956
## [1] 0.065873345.4. Kabupaten Sleman
susenas_sleman.15=filter(susenas_diy.15, KODE_KAB == "04")
susenas_sleman.tpt=filter(susenas.tpt1, KODE_KAB == "04")des7.1.1 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_sleman.tpt)
svytotal(~TPT, des7.1.1)
svymean(~TPT, des7.1.1)
des7.2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_sleman.15)
svymean(~TPAK, des7.2)
svytotal(~TPAK, des7.2)
svytotal(~bekerja, des7.2)## total SE
## TPT 15825 3686
## mean SE
## TPT 0.025451 0.0058
## mean SE
## TPAK 0.64316 0.0164
## total SE
## TPAK 621786 41214
## total SE
## bekerja 605962 40539sakernas5_sleman=filter(sakernas5, KODE_KAB == "04")
sakernas6_sleman=filter(sakernas6, KODE_KAB == "04")
sakernas.diy_sleman=filter(sakernas_diy, KODE_KAB == "04")
sakernas.tpt1_sleman=filter(sakernas.tpt1, KODE_KAB == "04")des7.3.3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.tpt1_sleman)
des7.3.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas5_sleman)
des7.3.5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas6_sleman)
des7.3.6 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.diy_sleman)
svytotal(~TPT, des7.3.4)
svymean(~TPT, des7.3.4)
svytotal(~TPT.p, des7.3.5)
svymean(~TPT.p, des7.3.5)
svytotal(~bekerja, des7.3.6)
svytotal(~bekerja.p, des7.3.6)
svytotal(~TPAK, des7.3.6)
svytotal(~TPAK.p, des7.3.6)
svymean(~TPAK, des7.3.6)
svymean(~TPAK.p, des7.3.6)## total SE
## TPT 28525 4400
## mean SE
## TPT 0.045721 0.0067
## total SE
## TPT.p 23005 3190.2
## mean SE
## TPT.p 0.03812 0.0054
## total SE
## bekerja 595368 31993
## total SE
## bekerja.p 580483 31452
## total SE
## TPAK 623893 33021
## total SE
## TPAK.p 603488 31735
## mean SE
## TPAK 0.67039 0.0129
## mean SE
## TPAK.p 0.64846 0.013#1. Variabel Bekerja Skenario 2
mns3.1.1 = svytotal(~bekerja+bekerja.p, des7.3.6)
vcov(mns3.1.1)## bekerja bekerja.p
## bekerja 1023551123 999843557
## bekerja.p 999843557 989255834var_xA = 40539^2
var_xB = 31452^2
myu_xA = 605962
myu_xB = 580483
myu_B = 595368
var_B = 31993^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 999843557
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.3757554
## [1] 590056.9
## [1] 605044.6
## [1] 25373.68
## [1] 0.04193688
## [1] 555312.2
## [1] 654777#2. Variabel Pengangguran Skenario 2
mns3.2.1 = svytotal(~TPT+TPT.p, des7.3.3)
vcov(mns3.2.1)
#table(sakernas.tpt1$TPT, sakernas.tpt1$TPT)## TPT TPT.p
## TPT 5552597 3455136
## TPT.p 3455136 7485552var_xA = 3686^2
var_xB = 3190.2^2
myu_xA = 15825
myu_xB = 23005
myu_B = 28525
var_B = 4400^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 3455136
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.4282691
## [1] 19930.03
## [1] 27481.07
## [1] 4342.539
## [1] 0.1580193
## [1] 18969.7
## [1] 35992.45#3. Variabel Angkatan Kerja Skenario 2
mns3.3.1 = svytotal(~TPAK+TPAK.p, des7.3.6)
vcov(mns3.3.1)## TPAK TPAK.p
## TPAK 1090358197 1034637148
## TPAK.p 1034637148 1007124862var_xA = 41214^2
var_xB = 31735^2
myu_xA = 621786
myu_xB = 603488
myu_B = 623893
var_B = 33021^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 1034637148
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.3722174
## [1] 610298.8
## [1] 630890
## [1] 26358.12
## [1] 0.04177926
## [1] 579228.1
## [1] 682551.9#4. Variabel TPAK Skenario 2
mns3.4.1 = svymean(~TPAK+TPAK.p, des7.3.6)
vcov(mns3.4.1)## TPAK TPAK.p
## TPAK 0.0001653199 0.0001514739
## TPAK.p 0.0001514739 0.0001680650var_xA = 0.0164^2
var_xB = 0.013^2
myu_xA = 0.64316
myu_xB = 0.64846
myu_B = 0.67039
var_B = 0.0129^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 0.0001514739
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.38588
## [1] 0.6464148
## [1] 0.6685569
## [1] 0.01067806
## [1] 0.0159718
## [1] 0.6476279
## [1] 0.6894859#5. Variabel TPT Skenario 2
mns3.4.1 = svymean(~TPT+TPT.p, des7.3.3)
vcov(mns3.4.1)## TPT TPT.p
## TPT 0.00001558758 0.00001001475
## TPT.p 0.00001001475 0.00002286456var_xA = 0.0058^2
var_xB = 0.0054^2
myu_xA = 0.025451
myu_xB = 0.03812
myu_B = 0.045721
var_B = 0.0067^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 1.001475e-05
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.4643312
## [1] 0.03223739
## [1] 0.04370067
## [1] 0.006579737
## [1] 0.1505638
## [1] 0.03080438
## [1] 0.056596955.5. Kota Yogyakarta
susenas_jogja.15=filter(susenas_diy.15, KODE_KAB == "71")
susenas_jogja.tpt=filter(susenas.tpt1, KODE_KAB == "71")des7.1.1 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_jogja.tpt)
svytotal(~TPT, des7.1.1)
svymean(~TPT, des7.1.1)
des7.2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_jogja.15)
svymean(~TPAK, des7.2)
svytotal(~TPAK, des7.2)
svytotal(~bekerja, des7.2)## total SE
## TPT 4848.8 1115.4
## mean SE
## TPT 0.022655 0.0053
## mean SE
## TPAK 0.60856 0.0174
## total SE
## TPAK 214032 13195
## total SE
## bekerja 209183 13126sakernas5_jogja=filter(sakernas5, KODE_KAB == "71")
sakernas6_jogja=filter(sakernas6, KODE_KAB == "71")
sakernas.diy_jogja=filter(sakernas_diy, KODE_KAB == "71")
sakernas.tpt1_jogja=filter(sakernas.tpt1, KODE_KAB == "71")des7.3.3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.tpt1_jogja)
des7.3.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas5_jogja)
des7.3.5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas6_jogja)
des7.3.6 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas.diy_jogja)
svytotal(~TPT, des7.3.4)
svymean(~TPT, des7.3.4)
svytotal(~TPT.p, des7.3.5)
svymean(~TPT.p, des7.3.5)
svytotal(~bekerja, des7.3.6)
svytotal(~bekerja.p, des7.3.6)
svytotal(~TPAK, des7.3.6)
svytotal(~TPAK.p, des7.3.6)
svymean(~TPAK, des7.3.6)
svymean(~TPAK.p, des7.3.6)## total SE
## TPT 15109 2230.6
## mean SE
## TPT 0.056666 0.0087
## total SE
## TPT.p 9596.3 1668.9
## mean SE
## TPT.p 0.037766 0.0064
## total SE
## bekerja 251523 20460
## total SE
## bekerja.p 244504 19592
## total SE
## TPAK 266632 20758
## total SE
## TPAK.p 254100 20012
## mean SE
## TPAK 0.65065 0.0169
## mean SE
## TPAK.p 0.62007 0.0174#1. Variabel Bekerja Skenario 2
mns3.1.1 = svytotal(~bekerja+bekerja.p, des7.3.6)
vcov(mns3.1.1)## bekerja bekerja.p
## bekerja 418596659 398589759
## bekerja.p 398589759 383836217var_xA = 13126^2
var_xB = 19592^2
myu_xA = 209183
myu_xB = 244504
myu_B = 251523
var_B = 20460^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 398589759
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.6901996
## [1] 220125.5
## [1] 226208.1
## [1] 11529.89
## [1] 0.0509703
## [1] 203609.5
## [1] 248806.7#2. Variabel Pengangguran Skenario 2
mns3.2.1 = svytotal(~TPT+TPT.p, des7.3.3)
vcov(mns3.2.1)
#table(sakernas.tpt1$TPT, sakernas.tpt1$TPT)## TPT TPT.p
## TPT 862457.4 104184
## TPT.p 104184.0 2546233var_xA = 1115.4^2
var_xB = 1668.9^2
myu_xA = 4848.8
myu_xB = 9596.3
myu_B = 15109
var_B = 2230.6^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 104184.0
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.6912358
## [1] 6314.658
## [1] 14986.25
## [1] 2229.996
## [1] 0.1488028
## [1] 10615.45
## [1] 19357.04#3. Variabel Angkatan Kerja Skenario 2
mns3.3.1 = svytotal(~TPAK+TPAK.p, des7.3.6)
vcov(mns3.3.1)## TPAK TPAK.p
## TPAK 430888150 411824761
## TPAK.p 411824761 400461860var_xA = 13195^2
var_xB = 20012^2
myu_xA = 214032
myu_xB = 254100
myu_B = 266632
var_B = 20758^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 411824761
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.6969864
## [1] 226173.1
## [1] 237914
## [1] 11650.2
## [1] 0.04896812
## [1] 215079.6
## [1] 260748.5#4. Variabel TPAK Skenario 2
mns3.4.1 = svymean(~TPAK+TPAK.p, des7.3.6)
vcov(mns3.4.1)## TPAK TPAK.p
## TPAK 0.0002850374 0.0002725574
## TPAK.p 0.0002725574 0.0003014736var_xA = 0.0174^2
var_xB = 0.0174^2
myu_xA = 0.60856
myu_xB = 0.62007
myu_B = 0.65065
var_B = 0.0169^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 0.0002725574
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.5
## [1] 0.614315
## [1] 0.6454691
## [1] 0.01276425
## [1] 0.01977516
## [1] 0.6204512
## [1] 0.670487#5. Variabel TPT Skenario 2
mns3.4.1 = svymean(~TPT+TPT.p, des7.3.3)
vcov(mns3.4.1)## TPT TPT.p
## TPT 0.000015547087 0.000003043771
## TPT.p 0.000003043771 0.000036591082var_xA = 0.0053^2
var_xB = 0.0064^2
myu_xA = 0.022655
myu_xB = 0.037766
myu_B = 0.056666
var_B = 0.0087^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 3.043771e-06
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))## [1] 0.5931933
## [1] 0.02880226
## [1] 0.0559999
## [1] 0.008692286
## [1] 0.1552197
## [1] 0.03896302
## [1] 0.07303678- Variabel Pendapatan Uang Sebulan Status Berusaha Sendiri/Pekerja Bebas
options(survey.lonely.psu = "adjust")
des8 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy.156)
svymean(~B5_R28B1, des8)
options(survey.lonely.psu = "adjust")
des9 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy.156)
svymean(~B5_R28B1.p, des9)
svymean(~B5_R28B1, des9)## mean SE
## B5_R28B1 1483119 9988.9
## mean SE
## B5_R28B1.p 1247446 20557
## mean SE
## B5_R28B1 1260581 33776mns4 = svymean(~B5_R28B1+ B5_R28B1.p, des9)
vcov(mns4)## B5_R28B1 B5_R28B1.p
## B5_R28B1 1140844964 465629586
## B5_R28B1.p 465629586 422604507var_xA = 9570.6^2
var_xB = 20557^2
myu_xA = 1493578
myu_xB = 1247446
myu_B = 1260581
var_B = 33776^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 465629586
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28B1, des9), level = 0.95)## [1] 0.8218616
## [1] 1449732
## [1] 1483470
## [1] 26817.16
## [1] 0.01807732
## [1] 33776
## [1] 0.02679399
## [1] 1430908
## [1] 1536031
## 2.5 % 97.5 %
## B5_R28B1 1194381 13267826.1. Kabupaten Kulonprogo
susenas_kulonprogo.156=filter(susenas_diy.156, KODE_KAB == "01")
sakernas_kulonprogo.156=filter(sakernas_diy.156, KODE_KAB == "01")
options(survey.lonely.psu = "adjust")
des8.1 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_kulonprogo.156)
svymean(~B5_R28B1, des8.1)
options(survey.lonely.psu = "adjust")
des9.1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_kulonprogo.156)
svymean(~B5_R28B1.p, des9.1)
svymean(~B5_R28B1, des9.1)## mean SE
## B5_R28B1 1405299 16968
## mean SE
## B5_R28B1.p 1112117 50009
## mean SE
## B5_R28B1 897868 53834mns4.1 = svymean(~B5_R28B1+ B5_R28B1.p, des9.1)
vcov(mns4.1)## B5_R28B1 B5_R28B1.p
## B5_R28B1 2898144116 2081326229
## B5_R28B1.p 2081326229 2500912768var_xA = 18630^2
var_xB = 50009^2
myu_xA = 1401936
myu_xB = 1112117
myu_B = 897868
var_B = 53834^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2081326229
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28B1, des9), level = 0.95)## [1] 0.8781321
## [1] 1366616
## [1] 1109670
## [1] 37108.6
## [1] 0.03344111
## [1] 53834
## [1] 0.05995759
## [1] 1036937
## [1] 1182403
## 2.5 % 97.5 %
## B5_R28B1 1194381 13267826.2. Kabupaten Bantul
susenas_bantul.156=filter(susenas_diy.156, KODE_KAB == "02")
sakernas_bantul.156=filter(sakernas_diy.156, KODE_KAB == "02")
options(survey.lonely.psu = "adjust")
des8.2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_bantul.156)
svymean(~B5_R28B1, des8.2)
options(survey.lonely.psu = "adjust")
des9.2 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_bantul.156)
svymean(~B5_R28B1.p, des9.2)
svymean(~B5_R28B1, des9.2)## mean SE
## B5_R28B1 1543058 20460
## mean SE
## B5_R28B1.p 1331683 37894
## mean SE
## B5_R28B1 1412421 85447mns4.2 = svymean(~B5_R28B1+ B5_R28B1.p, des9.2)
vcov(mns4.2)## B5_R28B1 B5_R28B1.p
## B5_R28B1 7301135308 1797798007
## B5_R28B1.p 1797798007 1435918631var_xA = 19151^2
var_xB = 37894^2
myu_xA = 1557936
myu_xB = 1331683
myu_B = 1412421
var_B = 85447^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 1797798007
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28B1, des9), level = 0.95)## [1] 0.796551
## [1] 1511905
## [1] 1638057
## [1] 74217.9
## [1] 0.0453085
## [1] 85447
## [1] 0.06049683
## [1] 1492590
## [1] 1783524
## 2.5 % 97.5 %
## B5_R28B1 1194381 13267826.3. Kabupaten Gunung Kidul
susenas_gunungkidul.156=filter(susenas_diy.156, KODE_KAB == "03")
sakernas_gunungkidul.156=filter(sakernas_diy.156, KODE_KAB == "03")
options(survey.lonely.psu = "adjust")
des8.3 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_gunungkidul.156)
svymean(~B5_R28B1, des8.3)
options(survey.lonely.psu = "adjust")
des9.3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_gunungkidul.156)
svymean(~B5_R28B1.p, des9.3)
svymean(~B5_R28B1, des9.3)## mean SE
## B5_R28B1 1327790 14946
## mean SE
## B5_R28B1.p 1052012 48260
## mean SE
## B5_R28B1 979961 65905mns4.3 = svymean(~B5_R28B1+ B5_R28B1.p, des9.3)
vcov(mns4.3)## B5_R28B1 B5_R28B1.p
## B5_R28B1 4343429727 2536246058
## B5_R28B1.p 2536246058 2329057043var_xA = 16241^2
var_xB = 48260^2
myu_xA = 1345451
myu_xB = 1052012
myu_B = 979961
var_B = 65905^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2536246058
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28B1, des9), level = 0.95)## [1] 0.8982682
## [1] 1315599
## [1] 1267000
## [1] 43157.17
## [1] 0.03406249
## [1] 65905
## [1] 0.06725268
## [1] 1182412
## [1] 1351588
## 2.5 % 97.5 %
## B5_R28B1 1194381 13267826.4. Kabupaten Sleman
susenas_sleman.156=filter(susenas_diy.156, KODE_KAB == "04")
sakernas_sleman.156=filter(sakernas_diy.156, KODE_KAB == "04")
options(survey.lonely.psu = "adjust")
des8.4 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_sleman.156)
svymean(~B5_R28B1, des8.4)
options(survey.lonely.psu = "adjust")
des9.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_sleman.156)
svymean(~B5_R28B1.p, des9.4)
svymean(~B5_R28B1, des9.4)## mean SE
## B5_R28B1 1542818 20721
## mean SE
## B5_R28B1.p 1367639 35234
## mean SE
## B5_R28B1 1496503 57757mns4.4 = svymean(~B5_R28B1+ B5_R28B1.p, des9.4)
vcov(mns4.4)## B5_R28B1 B5_R28B1.p
## B5_R28B1 3335892627 1199487799
## B5_R28B1.p 1199487799 1241469554var_xA = 19184^2
var_xB = 35234^2
myu_xA = 1554091
myu_xB = 1367639
myu_B = 1496503
var_B = 57757^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 1199487799
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28B1, des9), level = 0.95)## [1] 0.7713359
## [1] 1511456
## [1] 1635461
## [1] 49415.84
## [1] 0.03021524
## [1] 57757
## [1] 0.03859464
## [1] 1538606
## [1] 1732316
## 2.5 % 97.5 %
## B5_R28B1 1194381 13267826.5. Kota Yogyakarta
susenas_jogja.156=filter(susenas_diy.156, KODE_KAB == "71")
sakernas_jogja.156=filter(sakernas_diy.156, KODE_KAB == "71")
options(survey.lonely.psu = "adjust")
des8.5 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_jogja.156)
svymean(~B5_R28B1, des8.5)
options(survey.lonely.psu = "adjust")
des9.5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_jogja.156)
svymean(~B5_R28B1.p, des9.5)
svymean(~B5_R28B1, des9.5)## mean SE
## B5_R28B1 1590399 19618
## mean SE
## B5_R28B1.p 1330877 39144
## mean SE
## B5_R28B1 1384087 55471mns4.5 = svymean(~B5_R28B1+ B5_R28B1.p, des9.5)
vcov(mns4.5)## B5_R28B1 B5_R28B1.p
## B5_R28B1 3077010058 1224045213
## B5_R28B1.p 1224045213 1532270603var_xA = 18655^2
var_xB = 39144^2
myu_xA = 1586293
myu_xB = 1330877
myu_B = 1384087
var_B = 55471^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 1224045213
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28B1, des9), level = 0.95)## [1] 0.8149146
## [1] 1539019
## [1] 1550362
## [1] 47751.25
## [1] 0.03080006
## [1] 55471
## [1] 0.04007768
## [1] 1456770
## [1] 1643955
## 2.5 % 97.5 %
## B5_R28B1 1194381 1326782- Variabel Pendapatan Barang Sebulan Status Berusaha Sendiri/Pekerja Bebas
options(survey.lonely.psu = "adjust")
des8 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy.156)
svymean(~B5_R28B2, des8)
options(survey.lonely.psu = "adjust")
des9 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy.156)
svymean(~B5_R28B2.p, des9)
svymean(~B5_R28B2, des9)## mean SE
## B5_R28B2 95911 4601.7
## mean SE
## B5_R28B2.p 73405 3687.4
## mean SE
## B5_R28B2 73112 7406.8mns5 = svymean(~B5_R28B2+B5_R28B2.p, des9)
vcov(mns5)## B5_R28B2 B5_R28B2.p
## B5_R28B2 54860156 17632418
## B5_R28B2.p 17632418 13596597var_xA = 4466.9^2
var_xB = 3687.4^2
myu_xA = 94352
myu_xB = 73405
myu_B = 73112
var_B = 7406.8^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 17632418
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28B2, des9), level = 0.95)## [1] 0.4052719
## [1] 81894.23
## [1] 84120.79
## [1] 6752.325
## [1] 0.08026939
## [1] 7406.8
## [1] 0.1013076
## [1] 70886.24
## [1] 97355.35
## 2.5 % 97.5 %
## B5_R28B2 58594.46 87628.447.1. Kabupaten Kulonprogo
svymean(~B5_R28B2, des8.1)
svymean(~B5_R28B2.p, des9.1)
svymean(~B5_R28B2, des9.1)## mean SE
## B5_R28B2 97121 9359.1
## mean SE
## B5_R28B2.p 52375 5024.7
## mean SE
## B5_R28B2 44799 5793.2mns5.1 = svymean(~B5_R28B2+B5_R28B2.p, des9.1)
vcov(mns5.1)## B5_R28B2 B5_R28B2.p
## B5_R28B2 33561173 25166168
## B5_R28B2.p 25166168 25247549var_xA = 8937.9^2
var_xB = 5024.7^2
myu_xA = 97942
myu_xB = 52375
myu_B = 44799
var_B = 5793.2^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 25166168
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28B2, des9), level = 0.95)## [1] 0.2401477
## [1] 63317.81
## [1] 55706.51
## [1] 5247.577
## [1] 0.09420042
## [1] 5793.2
## [1] 0.1293154
## [1] 45421.26
## [1] 65991.76
## 2.5 % 97.5 %
## B5_R28B2 58594.46 87628.447.2. Kabupaten Bantul
svymean(~B5_R28B2, des8.2)
svymean(~B5_R28B2.p, des9.2)
svymean(~B5_R28B2, des9.2)## mean SE
## B5_R28B2 96497 8323.5
## mean SE
## B5_R28B2.p 74112 6464.8
## mean SE
## B5_R28B2 63288 8981.5mns5.2 = svymean(~B5_R28B2+B5_R28B2.p, des9.2)
vcov(mns5.2)## B5_R28B2 B5_R28B2.p
## B5_R28B2 80667246 45435016
## B5_R28B2.p 45435016 41793719var_xA = 8184.8^2
var_xB = 6464.8^2
myu_xA = 93753
myu_xB = 74112
myu_B = 63288
var_B = 8981.5^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 45435016
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28B2, des9), level = 0.95)## [1] 0.3841871
## [1] 81657.82
## [1] 71491.27
## [1] 7854.358
## [1] 0.1098646
## [1] 8981.5
## [1] 0.1419147
## [1] 56096.73
## [1] 86885.81
## 2.5 % 97.5 %
## B5_R28B2 58594.46 87628.447.3. Kabupaten Gunung Kidul
svymean(~B5_R28B2, des8.3)
svymean(~B5_R28B2.p, des9.3)
svymean(~B5_R28B2, des9.3)## mean SE
## B5_R28B2 69117 5909.2
## mean SE
## B5_R28B2.p 57667 5748
## mean SE
## B5_R28B2 52479 7278mns5.3 = svymean(~B5_R28B2+B5_R28B2.p, des9.3)
vcov(mns5.3)## B5_R28B2 B5_R28B2.p
## B5_R28B2 52969966 33314320
## B5_R28B2.p 33314320 33039236var_xA = 6043.2^2
var_xB = 5748^2
myu_xA = 71267
myu_xB = 57667
myu_B = 52479
var_B = 7278^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 33314320
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28B2, des9), level = 0.95)## [1] 0.4749801
## [1] 64126.73
## [1] 58992.46
## [1] 6083.916
## [1] 0.1031304
## [1] 7278
## [1] 0.138684
## [1] 47067.99
## [1] 70916.93
## 2.5 % 97.5 %
## B5_R28B2 58594.46 87628.447.4. Kabupaten Sleman
svymean(~B5_R28B2, des8.4)
svymean(~B5_R28B2.p, des9.4)
svymean(~B5_R28B2, des9.4)## mean SE
## B5_R28B2 109128 11772
## mean SE
## B5_R28B2.p 97311 9993
## mean SE
## B5_R28B2 121019 24671mns5.4 = svymean(~B5_R28B2+B5_R28B2.p, des9.4)
vcov(mns5.4)## B5_R28B2 B5_R28B2.p
## B5_R28B2 608656351 142085026
## B5_R28B2.p 142085026 99859526var_xA = 11361^2
var_xB = 9993^2
myu_xA = 105451
myu_xB = 97311
myu_B = 121019
var_B = 24671^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 142085026
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28B2, des9), level = 0.95)## [1] 0.4361989
## [1] 100861.7
## [1] 126071
## [1] 22813.91
## [1] 0.1809607
## [1] 24671
## [1] 0.2038606
## [1] 81355.77
## [1] 170786.3
## 2.5 % 97.5 %
## B5_R28B2 58594.46 87628.447.5. Kota Yogyakarta
svymean(~B5_R28B2, des8.5)
svymean(~B5_R28B2.p, des9.5)
svymean(~B5_R28B2, des9.5)## mean SE
## B5_R28B2 115338 13414
## mean SE
## B5_R28B2.p 75536 9133.1
## mean SE
## B5_R28B2 65015 10270mns5.5 = svymean(~B5_R28B2+B5_R28B2.p, des9.5)
vcov(mns5.5)## B5_R28B2 B5_R28B2.p
## B5_R28B2 105478910 72726604
## B5_R28B2.p 72726604 83413643var_xA = 12759^2
var_xB = 9133.1^2
myu_xA = 111931
myu_xB = 75536
myu_B = 65015
var_B = 10270^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 72726604
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28B2, des9), level = 0.95)## [1] 0.3387962
## [1] 87866.49
## [1] 75765.71
## [1] 9164.617
## [1] 0.12096
## [1] 10270
## [1] 0.1579635
## [1] 57803.06
## [1] 93728.36
## 2.5 % 97.5 %
## B5_R28B2 58594.46 87628.44- Variabel Upah Uang Sebulan Status Buruh/Karyawan/Pegawai
options(survey.lonely.psu = "adjust")
des10 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy.buruh)
svymean(~B5_R28C1, des10)
options(survey.lonely.psu = "adjust")
des11 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy.buruh)
svymean(~B5_R28C1.p, des11)
svymean(~B5_R28C1, des11)## mean SE
## B5_R28C1 2354317 22808
## mean SE
## B5_R28C1.p 2329804 19379
## mean SE
## B5_R28C1 2288719 47277mns6 = svymean(~B5_R28C1+B5_R28C1.p, des11)
vcov(mns6)## B5_R28C1 B5_R28C1.p
## B5_R28C1 2235103759 643061296
## B5_R28C1.p 643061296 375540534var_xA = 23459^2
var_xB = 20429^2
myu_xA = 2347191
myu_xB = 2323417
myu_B = 2288719
var_B = 47277^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 707882574
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28C1, des11), level = 0.95)## [1] 0.4312881
## [1] 2333670
## [1] 2306110
## [1] 41440.01
## [1] 0.01796966
## [1] 47277
## [1] 0.02065653
## [1] 2224888
## [1] 2387333
## 2.5 % 97.5 %
## B5_R28C1 2196058 23813808.1. Kabupaten Kulonprogo
susenas_kulonprogo.buruh = filter(susenas_diy.buruh, KODE_KAB == "01")
sakernas_kulonprogo.buruh = filter(sakernas_diy.buruh, KODE_KAB == "01")
options(survey.lonely.psu = "adjust")
des10.1 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_kulonprogo.buruh)
svymean(~B5_R28C1, des10.1)
options(survey.lonely.psu = "adjust")
des11.1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_kulonprogo.buruh)
svymean(~B5_R28C1.p, des11.1)
svymean(~B5_R28C1, des11.1)## mean SE
## B5_R28C1 2353528 44104
## mean SE
## B5_R28C1.p 2324381 55487
## mean SE
## B5_R28C1 2222026 101170mns6.1 = svymean(~B5_R28C1+B5_R28C1.p, des11.1)
vcov(mns6.1)## B5_R28C1 B5_R28C1.p
## B5_R28C1 10235318997 3853084973
## B5_R28C1.p 3853084973 3078789421var_xA = 45556^2
var_xB = 57508^2
myu_xA = 2353137
myu_xB = 2336438
myu_B = 2222026
var_B = 101170^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 3884963699
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28C1, des11), level = 0.95)## [1] 0.6144279
## [1] 2346698
## [1] 2234079
## [1] 86205
## [1] 0.03858637
## [1] 101170
## [1] 0.04553052
## [1] 2065117
## [1] 2403041
## 2.5 % 97.5 %
## B5_R28C1 2196058 23813808.2. Kabupaten Bantul
susenas_bantul.buruh = filter(susenas_diy.buruh, KODE_KAB == "02")
sakernas_bantul.buruh = filter(sakernas_diy.buruh, KODE_KAB == "02")
options(survey.lonely.psu = "adjust")
des10.2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_bantul.buruh)
svymean(~B5_R28C1, des10.2)
options(survey.lonely.psu = "adjust")
des11.2 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_bantul.buruh)
svymean(~B5_R28C1.p, des11.2)
svymean(~B5_R28C1, des11.2)## mean SE
## B5_R28C1 2290541 35910
## mean SE
## B5_R28C1.p 2282355 40413
## mean SE
## B5_R28C1 2197913 87468mns6.2 = svymean(~B5_R28C1+B5_R28C1.p, des11.2)
vcov(mns6.2)## B5_R28C1 B5_R28C1.p
## B5_R28C1 7650679060 2709026515
## B5_R28C1.p 2709026515 1633249483var_xA = 38147^2
var_xB = 42318^2
myu_xA = 2287828
myu_xB = 2284008
myu_B = 2197913
var_B = 87468^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2932281176
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28C1, des11), level = 0.95)## [1] 0.5516973
## [1] 2286115
## [1] 2201364
## [1] 70723.22
## [1] 0.032127
## [1] 87468
## [1] 0.03979593
## [1] 2062746
## [1] 2339981
## 2.5 % 97.5 %
## B5_R28C1 2196058 23813808.3. Kabupaten Gunung Kidul
susenas_gunungkidul.buruh = filter(susenas_diy.buruh, KODE_KAB == "03")
sakernas_gunungkidul.buruh = filter(sakernas_diy.buruh, KODE_KAB == "03")
options(survey.lonely.psu = "adjust")
des10.3 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_gunungkidul.buruh)
svymean(~B5_R28C1, des10.3)
options(survey.lonely.psu = "adjust")
des11.3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_gunungkidul.buruh)
svymean(~B5_R28C1.p, des11.3)
svymean(~B5_R28C1, des11.3)## mean SE
## B5_R28C1 2134975 46998
## mean SE
## B5_R28C1.p 2151443 42191
## mean SE
## B5_R28C1 1896499 79465mns6.3 = svymean(~B5_R28C1+B5_R28C1.p, des11.3)
vcov(mns6.3)## B5_R28C1 B5_R28C1.p
## B5_R28C1 6314751150 2145918600
## B5_R28C1.p 2145918600 1780111569var_xA = 52593^2
var_xB = 46523^2
myu_xA = 2115805
myu_xB = 2125756
myu_B = 1896499
var_B = 79465^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2593612604
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28C1, des11), level = 0.95)## [1] 0.4389875
## [1] 2121388
## [1] 1891264
## [1] 70358.6
## [1] 0.03720189
## [1] 79465
## [1] 0.04190089
## [1] 1753361
## [1] 2029167
## 2.5 % 97.5 %
## B5_R28C1 2196058 23813808.4. Kabupaten Sleman
susenas_sleman.buruh = filter(susenas_diy.buruh, KODE_KAB == "04")
sakernas_sleman.buruh = filter(sakernas_diy.buruh, KODE_KAB == "04")
options(survey.lonely.psu = "adjust")
des10.4 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_sleman.buruh)
svymean(~B5_R28C1, des10.4)
options(survey.lonely.psu = "adjust")
des11.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_sleman.buruh)
svymean(~B5_R28C1.p, des11.4)
svymean(~B5_R28C1, des11.4)## mean SE
## B5_R28C1 2433001 47536
## mean SE
## B5_R28C1.p 2410030 33278
## mean SE
## B5_R28C1 2473378 88081mns6.4 = svymean(~B5_R28C1+B5_R28C1.p, des11.4)
vcov(mns6.4)## B5_R28C1 B5_R28C1.p
## B5_R28C1 7758177173 1818646862
## B5_R28C1.p 1818646862 1107412175var_xA = 47199^2
var_xB = 34653^2
myu_xA = 2422076
myu_xB = 2399262
myu_B = 2473378
var_B = 88081^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2019925486
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28C1, des11), level = 0.95)## [1] 0.3502417
## [1] 2407252
## [1] 2486819
## [1] 81044.65
## [1] 0.03258969
## [1] 88081
## [1] 0.03561162
## [1] 2327971
## [1] 2645666
## 2.5 % 97.5 %
## B5_R28C1 2196058 23813808.5. Kota Yogyakarta
susenas_jogja.buruh = filter(susenas_diy.buruh, KODE_KAB == "71")
sakernas_jogja.buruh = filter(sakernas_diy.buruh, KODE_KAB == "71")
options(survey.lonely.psu = "adjust")
des10.5 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_jogja.buruh)
svymean(~B5_R28C1, des10.5)
options(survey.lonely.psu = "adjust")
des11.5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_jogja.buruh)
svymean(~B5_R28C1.p, des11.5)
svymean(~B5_R28C1, des11.5)## mean SE
## B5_R28C1 2501808 48761
## mean SE
## B5_R28C1.p 2398315 50254
## mean SE
## B5_R28C1 2435672 150801mns6.5 = svymean(~B5_R28C1+B5_R28C1.p, des11.5)
vcov(mns6.5)## B5_R28C1 B5_R28C1.p
## B5_R28C1 22741003797 6107612179
## B5_R28C1.p 6107612179 2525503656var_xA = 53342^2
var_xB = 53842^2
myu_xA = 2502533
myu_xB = 2390471
myu_B = 2435672
var_B = 150801^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 6831012152
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28C1, des11), level = 0.95)## [1] 0.5046648
## [1] 2447025
## [1] 2568933
## [1] 120903.6
## [1] 0.04706373
## [1] 150801
## [1] 0.06191351
## [1] 2331962
## [1] 2805904
## 2.5 % 97.5 %
## B5_R28C1 2196058 2381380- Variabel Upah Barang Sebulan Status Buruh/Karyawan/Pegawai
options(survey.lonely.psu = "adjust")
des10 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy.buruh)
svymean(~B5_R28C2, des10)
options(survey.lonely.psu = "adjust")
des11 = svydesign(ids=~psu+ssu, strata=~strata, weights=~w.adj2_20, data = sakernas_diy.buruh)
svymean(~B5_R28C2.p, des11)
svymean(~B5_R28C2, des11)## mean SE
## B5_R28C2 69732 961.61
## mean SE
## B5_R28C2.p 67654 882.37
## mean SE
## B5_R28C2 71108 6621.3mns7 = svymean(~B5_R28C2+B5_R28C2.p, des11)
vcov(mns7)## B5_R28C2 B5_R28C2.p
## B5_R28C2 43842182 1836699.4
## B5_R28C2.p 1836699 778571.6var_xA = 932.1^2
var_xB = 881.48^2
myu_xA = 69877
myu_xB = 67840
myu_B = 71108
var_B = 6621.3^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 1780977.1
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28C2, des11), level = 0.95)## [1] 0.4721101
## [1] 68801.69
## [1] 73312.28
## [1] 6474.131
## [1] 0.08830896
## [1] 6621.3
## [1] 0.09311611
## [1] 60622.99
## [1] 86001.58
## 2.5 % 97.5 %
## B5_R28C2 58130.4 84085.599.1. Kabupaten Kulonprogo
svymean(~B5_R28C2, des10.1)
svymean(~B5_R28C2.p, des11.1)
svymean(~B5_R28C2, des11.1)## mean SE
## B5_R28C2 66712 1608.2
## mean SE
## B5_R28C2.p 70903 2053.7
## mean SE
## B5_R28C2 75170 9401.5mns7.1 = svymean(~B5_R28C2+B5_R28C2.p, des11.1)
vcov(mns7.1)## B5_R28C2 B5_R28C2.p
## B5_R28C2 88387959 2898235
## B5_R28C2.p 2898235 4217580var_xA = 1620.4^2
var_xB = 2001^2
myu_xA = 67345
myu_xB = 70420
myu_B = 75170
var_B = 9401.5^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2537529
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28C2, des11), level = 0.95)## [1] 0.6039493
## [1] 68562.86
## [1] 73993.04
## [1] 9349.704
## [1] 0.1263592
## [1] 9401.5
## [1] 0.1250698
## [1] 55667.62
## [1] 92318.46
## 2.5 % 97.5 %
## B5_R28C2 58130.4 84085.599.2. Kabupaten Bantul
svymean(~B5_R28C2, des10.2)
svymean(~B5_R28C2.p, des11.2)
svymean(~B5_R28C2, des11.2)## mean SE
## B5_R28C2 62650 1322.3
## mean SE
## B5_R28C2.p 59954 1574.3
## mean SE
## B5_R28C2 46216 3811.8mns7.2 = svymean(~B5_R28C2+B5_R28C2.p, des11.2)
vcov(mns7.2)## B5_R28C2 B5_R28C2.p
## B5_R28C2 14529616 2339761
## B5_R28C2.p 2339761 2478518var_xA = 1222.4^2
var_xB = 1584.4^2
myu_xA = 62717
myu_xB = 60303
myu_B = 46216
var_B = 3811.8^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2328307
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28C2, des11), level = 0.95)## [1] 0.6268623
## [1] 61816.25
## [1] 47619.52
## [1] 3629.892
## [1] 0.07622697
## [1] 3811.8
## [1] 0.08247793
## [1] 40504.94
## [1] 54734.11
## 2.5 % 97.5 %
## B5_R28C2 58130.4 84085.599.3. Kabupaten Gunung Kidul
svymean(~B5_R28C2, des10.3)
svymean(~B5_R28C2.p, des11.3)
svymean(~B5_R28C2, des11.3)## mean SE
## B5_R28C2 66689 1994
## mean SE
## B5_R28C2.p 63622 1664.4
## mean SE
## B5_R28C2 56531 4381mns7.3 = svymean(~B5_R28C2+B5_R28C2.p, des11.3)
vcov(mns7.3)## B5_R28C2 B5_R28C2.p
## B5_R28C2 19193482 2582066
## B5_R28C2.p 2582066 2770301var_xA = 1938^2
var_xB = 1650.7^2
myu_xA = 67064
myu_xB = 64309
myu_B = 56531
var_B = 4381^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 2459450
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28C2, des11), level = 0.95)## [1] 0.420453
## [1] 65467.35
## [1] 57576.54
## [1] 4273.147
## [1] 0.0742168
## [1] 4381
## [1] 0.0774973
## [1] 49201.17
## [1] 65951.91
## 2.5 % 97.5 %
## B5_R28C2 58130.4 84085.599.4. Kabupaten Sleman
svymean(~B5_R28C2, des10.4)
svymean(~B5_R28C2.p, des11.4)
svymean(~B5_R28C2, des11.4)## mean SE
## B5_R28C2 76575 1943.8
## mean SE
## B5_R28C2.p 74327 1738.9
## mean SE
## B5_R28C2 94293 16820mns7.4 = svymean(~B5_R28C2+B5_R28C2.p, des11.4)
vcov(mns7.4)## B5_R28C2 B5_R28C2.p
## B5_R28C2 282910244 8722849
## B5_R28C2.p 8722849 3023686var_xA = 1865.7^2
var_xB = 1742.9^2
myu_xA = 76332
myu_xB = 74413
myu_B = 94293
var_B = 16820^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 8490725
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28C2, des11), level = 0.95)## [1] 0.4660095
## [1] 75307.27
## [1] 96792.59
## [1] 16487.96
## [1] 0.1703432
## [1] 16820
## [1] 0.1783802
## [1] 64476.19
## [1] 129109
## 2.5 % 97.5 %
## B5_R28C2 58130.4 84085.599.5. Kota Yogyakarta
svymean(~B5_R28C2, des10.5)
svymean(~B5_R28C2.p, des11.5)
svymean(~B5_R28C2, des11.5)## mean SE
## B5_R28C2 71989 2928.5
## mean SE
## B5_R28C2.p 68070 2350.6
## mean SE
## B5_R28C2 74891 19106mns7.5 = svymean(~B5_R28C2+B5_R28C2.p, des11.5)
vcov(mns7.5)## B5_R28C2 B5_R28C2.p
## B5_R28C2 365031633 15372340
## B5_R28C2.p 15372340 5525240var_xA = 2933.9^2
var_xB = 2347.4^2
myu_xA = 72772
myu_xB = 68231
myu_B = 74891
var_B = 19106^2
(alpha = var_xB / (var_xA+var_xB))
(myu_x = (alpha*myu_xA) + ((1-alpha)*myu_xB))
cov = 14619389
myu_gls = myu_B + ((cov/var_xB)*(myu_x-myu_xB))
myu_gls
var.myu_gls = var_B - (cov^2/(var_xA+var_xB))
sqrt(var.myu_gls)
(rse = sqrt(var.myu_gls)/myu_gls)
sqrt(var_B)
(rse_dir = sqrt(var_B)/myu_B)
(CI_lower = myu_gls - 1.96*sqrt(var.myu_gls))
(CI_upper = myu_gls + 1.96*sqrt(var.myu_gls))
confint(svymean(~B5_R28C2, des11), level = 0.95)## [1] 0.3903007
## [1] 70003.36
## [1] 79593.25
## [1] 18705.63
## [1] 0.2350153
## [1] 19106
## [1] 0.2551174
## [1] 42930.21
## [1] 116256.3
## 2.5 % 97.5 %
## B5_R28C2 58130.4 84085.59