psd_penanganan asumsi_dummy
Muthi’ah Iffa Karimah
2025-09-21
Metode Best Variable Selection
library(readxl)
library(leaps)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(lmtest) ## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
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## as.Date, as.Date.numericlibrary(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
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## recodelibrary(tidyr)
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
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## smithslibrary(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:reshape2':
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## dcast, melt## The following objects are masked from 'package:zoo':
##
## yearmon, yearqtr## The following objects are masked from 'package:dplyr':
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## between, first, lastlibrary(ggplot2)rumah = read_excel("C:\\Users\\MUTHI'AH IFFA\\Downloads\\Semester 5\\PSD\\praktikum\\rumah3.xlsx")
str(rumah)## tibble [1,047 × 8] (S3: tbl_df/tbl/data.frame)
## $ jenis_properti: chr [1:1047] "Rumah" "Rumah" "Rumah" "Rumah" ...
## $ harga_rp : chr [1:1047] "2.000.000.000" "799.000.000" "2.300.000.000" "6.250.000.000" ...
## $ lokasi : num [1:1047] 1 3 1 1 3 1 6 1 11 1 ...
## $ Jml_KM(Buah) : chr [1:1047] "4" "2" "11" "5" ...
## $ Jml_KT(Buah) : chr [1:1047] "3" "2" "4" "5" ...
## $ Jml_gar(Buah) : chr [1:1047] "2" "1" "1" "2" ...
## $ LT(m²) : chr [1:1047] "90" "102" "90" "294" ...
## $ LB(m²) : chr [1:1047] "130" "90" "150" "350" ...# jumlah baris yang punya NA di kolom manapun
sum(!complete.cases(rumah))## [1] 10rumah[!complete.cases(rumah$lokasi), ]## # A tibble: 4 × 8
## jenis_properti harga_rp lokasi `Jml_KM(Buah)` `Jml_KT(Buah)` `Jml_gar(Buah)`
## <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 Rumah 6.500.000… NA 4 3 3
## 2 Rumah 589.000.0… NA 3 2 1
## 3 Rumah 1.300.000… NA 3 2 1
## 4 Rumah 563.000.0… NA 2 2 1
## # ℹ 2 more variables: `LT(m²)` <chr>, `LB(m²)` <chr>nrow(rumah)## [1] 10471047 baris masih mengandung missing value dan outlier
# Hapus mising value
rumah_clean = rumah %>% drop_na()
sum(!complete.cases(rumah_clean))## [1] 0nrow(rumah_clean) ; ncol(rumah_clean)## [1] 1037## [1] 8hasil penghapusan outlier baris menjadi berjumlah 1037
# Menghapus peubah jenis properti dan mengubah peubah menjadi numerik
rumah2 = dplyr::select(rumah_clean, -jenis_properti)
str(rumah2)## tibble [1,037 × 7] (S3: tbl_df/tbl/data.frame)
## $ harga_rp : chr [1:1037] "2.000.000.000" "799.000.000" "2.300.000.000" "6.250.000.000" ...
## $ lokasi : num [1:1037] 1 3 1 1 3 1 6 1 11 1 ...
## $ Jml_KM(Buah) : chr [1:1037] "4" "2" "11" "5" ...
## $ Jml_KT(Buah) : chr [1:1037] "3" "2" "4" "5" ...
## $ Jml_gar(Buah): chr [1:1037] "2" "1" "1" "2" ...
## $ LT(m²) : chr [1:1037] "90" "102" "90" "294" ...
## $ LB(m²) : chr [1:1037] "130" "90" "150" "350" ...library(dplyr)
library(janitor)##
## Attaching package: 'janitor'## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testrumah2 = rumah_clean %>%
clean_names() %>% # biar nama kolom jadi aman snake_case
mutate(
harga_rp = as.numeric(gsub("\\.", "", harga_rp)),
jml_km_buah = as.numeric(jml_km_buah),
jml_kt_buah = as.numeric(jml_kt_buah),
jml_gar_buah= as.numeric(jml_gar_buah),
lt_m2 = as.numeric(lt_m2),
lb_m2 = as.numeric(lb_m2)
)
str(rumah2)## tibble [1,037 × 8] (S3: tbl_df/tbl/data.frame)
## $ jenis_properti: chr [1:1037] "Rumah" "Rumah" "Rumah" "Rumah" ...
## $ harga_rp : num [1:1037] 2.00e+09 7.99e+08 2.30e+09 6.25e+09 6.00e+08 1.10e+09 5.50e+08 2.60e+09 4.50e+09 3.90e+09 ...
## $ lokasi : num [1:1037] 1 3 1 1 3 1 6 1 11 1 ...
## $ jml_km_buah : num [1:1037] 4 2 11 5 2 3 3 3 4 4 ...
## $ jml_kt_buah : num [1:1037] 3 2 4 5 1 2 3 3 4 4 ...
## $ jml_gar_buah : num [1:1037] 2 1 1 2 1 1 0 2 2 0 ...
## $ lt_m2 : num [1:1037] 90 102 90 294 66 60 60 98 288 80 ...
## $ lb_m2 : num [1:1037] 130 90 150 350 50 80 112 90 400 200 ...# Cek hasilnya
rumah2## # A tibble: 1,037 × 8
## jenis_properti harga_rp lokasi jml_km_buah jml_kt_buah jml_gar_buah lt_m2
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Rumah 2000000000 1 4 3 2 90
## 2 Rumah 799000000 3 2 2 1 102
## 3 Rumah 2300000000 1 11 4 1 90
## 4 Rumah 6250000000 1 5 5 2 294
## 5 Rumah 600000000 3 2 1 1 66
## 6 Rumah 1100000000 1 3 2 1 60
## 7 Rumah 550000000 6 3 3 0 60
## 8 Rumah 2600000000 1 3 3 2 98
## 9 Rumah 4500000000 11 4 4 2 288
## 10 Rumah 3900000000 1 4 4 0 80
## # ℹ 1,027 more rows
## # ℹ 1 more variable: lb_m2 <dbl>str(rumah2)## tibble [1,037 × 8] (S3: tbl_df/tbl/data.frame)
## $ jenis_properti: chr [1:1037] "Rumah" "Rumah" "Rumah" "Rumah" ...
## $ harga_rp : num [1:1037] 2.00e+09 7.99e+08 2.30e+09 6.25e+09 6.00e+08 1.10e+09 5.50e+08 2.60e+09 4.50e+09 3.90e+09 ...
## $ lokasi : num [1:1037] 1 3 1 1 3 1 6 1 11 1 ...
## $ jml_km_buah : num [1:1037] 4 2 11 5 2 3 3 3 4 4 ...
## $ jml_kt_buah : num [1:1037] 3 2 4 5 1 2 3 3 4 4 ...
## $ jml_gar_buah : num [1:1037] 2 1 1 2 1 1 0 2 2 0 ...
## $ lt_m2 : num [1:1037] 90 102 90 294 66 60 60 98 288 80 ...
## $ lb_m2 : num [1:1037] 130 90 150 350 50 80 112 90 400 200 ...head(rumah2)## # A tibble: 6 × 8
## jenis_properti harga_rp lokasi jml_km_buah jml_kt_buah jml_gar_buah lt_m2
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Rumah 2000000000 1 4 3 2 90
## 2 Rumah 799000000 3 2 2 1 102
## 3 Rumah 2300000000 1 11 4 1 90
## 4 Rumah 6250000000 1 5 5 2 294
## 5 Rumah 600000000 3 2 1 1 66
## 6 Rumah 1100000000 1 3 2 1 60
## # ℹ 1 more variable: lb_m2 <dbl># Cek missing value kembali
colSums(is.na(rumah2))## jenis_properti harga_rp lokasi jml_km_buah jml_kt_buah
## 0 0 0 0 0
## jml_gar_buah lt_m2 lb_m2
## 0 0 0rumah2 %>% filter(is.na(lokasi))## # A tibble: 0 × 8
## # ℹ 8 variables: jenis_properti <chr>, harga_rp <dbl>, lokasi <dbl>,
## # jml_km_buah <dbl>, jml_kt_buah <dbl>, jml_gar_buah <dbl>, lt_m2 <dbl>,
## # lb_m2 <dbl>Mengubah Dummy Variabel
rumah_dummy = fastDummies::dummy_cols(
rumah2,
select_columns = "lokasi",
remove_first_dummy = TRUE # otomatis jadi k-1
)
str(rumah_dummy)## tibble [1,037 × 18] (S3: tbl_df/tbl/data.frame)
## $ jenis_properti: chr [1:1037] "Rumah" "Rumah" "Rumah" "Rumah" ...
## $ harga_rp : num [1:1037] 2.00e+09 7.99e+08 2.30e+09 6.25e+09 6.00e+08 1.10e+09 5.50e+08 2.60e+09 4.50e+09 3.90e+09 ...
## $ lokasi : num [1:1037] 1 3 1 1 3 1 6 1 11 1 ...
## $ jml_km_buah : num [1:1037] 4 2 11 5 2 3 3 3 4 4 ...
## $ jml_kt_buah : num [1:1037] 3 2 4 5 1 2 3 3 4 4 ...
## $ jml_gar_buah : num [1:1037] 2 1 1 2 1 1 0 2 2 0 ...
## $ lt_m2 : num [1:1037] 90 102 90 294 66 60 60 98 288 80 ...
## $ lb_m2 : num [1:1037] 130 90 150 350 50 80 112 90 400 200 ...
## $ lokasi_2 : int [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_3 : int [1:1037] 0 1 0 0 1 0 0 0 0 0 ...
## $ lokasi_4 : int [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_5 : int [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_6 : int [1:1037] 0 0 0 0 0 0 1 0 0 0 ...
## $ lokasi_7 : int [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_8 : int [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_9 : int [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_10 : int [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_11 : int [1:1037] 0 0 0 0 0 0 0 0 1 0 ...View(rumah_dummy)# Hapus variable dummy "lokasi_11"
rumah2_dummy = dplyr::select(rumah_dummy,-jenis_properti, -lokasi)
str(rumah2_dummy)## tibble [1,037 × 16] (S3: tbl_df/tbl/data.frame)
## $ harga_rp : num [1:1037] 2.00e+09 7.99e+08 2.30e+09 6.25e+09 6.00e+08 1.10e+09 5.50e+08 2.60e+09 4.50e+09 3.90e+09 ...
## $ jml_km_buah : num [1:1037] 4 2 11 5 2 3 3 3 4 4 ...
## $ jml_kt_buah : num [1:1037] 3 2 4 5 1 2 3 3 4 4 ...
## $ jml_gar_buah: num [1:1037] 2 1 1 2 1 1 0 2 2 0 ...
## $ lt_m2 : num [1:1037] 90 102 90 294 66 60 60 98 288 80 ...
## $ lb_m2 : num [1:1037] 130 90 150 350 50 80 112 90 400 200 ...
## $ lokasi_2 : int [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_3 : int [1:1037] 0 1 0 0 1 0 0 0 0 0 ...
## $ lokasi_4 : int [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_5 : int [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_6 : int [1:1037] 0 0 0 0 0 0 1 0 0 0 ...
## $ lokasi_7 : int [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_8 : int [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_9 : int [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_10 : int [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_11 : int [1:1037] 0 0 0 0 0 0 0 0 1 0 ...# ubah kolom lokasi_2 sampai lokasi_10 menjadi numeric
rumah2_dummy = rumah2_dummy %>%
mutate(across(lokasi_2:lokasi_11, as.numeric))
# cek hasilnya
str(rumah2_dummy)## tibble [1,037 × 16] (S3: tbl_df/tbl/data.frame)
## $ harga_rp : num [1:1037] 2.00e+09 7.99e+08 2.30e+09 6.25e+09 6.00e+08 1.10e+09 5.50e+08 2.60e+09 4.50e+09 3.90e+09 ...
## $ jml_km_buah : num [1:1037] 4 2 11 5 2 3 3 3 4 4 ...
## $ jml_kt_buah : num [1:1037] 3 2 4 5 1 2 3 3 4 4 ...
## $ jml_gar_buah: num [1:1037] 2 1 1 2 1 1 0 2 2 0 ...
## $ lt_m2 : num [1:1037] 90 102 90 294 66 60 60 98 288 80 ...
## $ lb_m2 : num [1:1037] 130 90 150 350 50 80 112 90 400 200 ...
## $ lokasi_2 : num [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_3 : num [1:1037] 0 1 0 0 1 0 0 0 0 0 ...
## $ lokasi_4 : num [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_5 : num [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_6 : num [1:1037] 0 0 0 0 0 0 1 0 0 0 ...
## $ lokasi_7 : num [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_8 : num [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_9 : num [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_10 : num [1:1037] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_11 : num [1:1037] 0 0 0 0 0 0 0 0 1 0 ...Regresi Linear Berganda
# Model regresi
reg = lm(harga_rp ~., data = rumah2_dummy)
summary(reg)##
## Call:
## lm(formula = harga_rp ~ ., data = rumah2_dummy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.457e+10 -8.787e+08 2.119e+08 8.132e+08 1.618e+11
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.565e+09 6.212e+08 -2.519 0.011911 *
## jml_km_buah 6.233e+08 2.122e+08 2.937 0.003388 **
## jml_kt_buah -5.300e+08 2.455e+08 -2.158 0.031125 *
## jml_gar_buah 3.129e+08 1.888e+08 1.657 0.097809 .
## lt_m2 1.170e+07 9.005e+05 12.988 < 2e-16 ***
## lb_m2 1.919e+07 1.608e+06 11.937 < 2e-16 ***
## lokasi_2 -7.599e+08 5.478e+08 -1.387 0.165677
## lokasi_3 -1.317e+09 5.669e+08 -2.323 0.020399 *
## lokasi_4 -3.211e+09 2.133e+09 -1.506 0.132479
## lokasi_5 -1.757e+09 2.045e+09 -0.859 0.390664
## lokasi_6 -2.738e+09 8.517e+08 -3.215 0.001347 **
## lokasi_7 -4.194e+09 1.319e+09 -3.179 0.001520 **
## lokasi_8 -2.073e+09 3.339e+09 -0.621 0.534814
## lokasi_9 -1.165e+10 6.655e+09 -1.751 0.080313 .
## lokasi_10 -2.458e+09 6.641e+09 -0.370 0.711374
## lokasi_11 -9.000e+09 2.387e+09 -3.771 0.000172 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.622e+09 on 1021 degrees of freedom
## Multiple R-squared: 0.5638, Adjusted R-squared: 0.5574
## F-statistic: 87.98 on 15 and 1021 DF, p-value: < 2.2e-16Uji Asumsi Regresi
1. Normalitas Residual
# Uji formal
shapiro = shapiro.test(residuals(reg)) # H0: residual ~ normal
# Hasil
cat("UJI NORMALITAS (Shapiro-Wilk):\n")## UJI NORMALITAS (Shapiro-Wilk):cat("W =", shapiro$statistic, " | p-value =", shapiro$p.value, "\n")## W = 0.3135445 | p-value = 4.569649e-52if(shapiro$p.value > 0.05){
cat(">> Residual berdistribusi normal (p > 0.05)\n\n")
} else {
cat(">> Residual TIDAK berdistribusi normal (p <= 0.05)\n\n")
}## >> Residual TIDAK berdistribusi normal (p <= 0.05)# Plot QQ-plot
residuals_reg = residuals(reg) # residual dari model
fitted_reg = fitted(reg) # nilai prediksi
qqnorm(residuals_reg, main = "QQ Plot Residual")
qqline(residuals_reg, col = "red", lwd = 2)
hist(residuals_reg,
breaks = 30,
main = "Histogram Residual",
xlab = "Residuals",
col = " blue",
border = "white")
2. Homoskedastisitas (varian residual konstan)
bp = bptest(reg)
# Hasil
cat("UJI HOMOSKEDASTISITAS (Breusch-Pagan):\n")## UJI HOMOSKEDASTISITAS (Breusch-Pagan):cat("BP =", bp$statistic, " | p-value =", bp$p.value, "\n")## BP = 269.9276 | p-value = 9.583313e-49if(bp$p.value > 0.05){
cat(">> Tidak ada masalah heteroskedastisitas (varian residual konstan)\n\n")
} else {
cat(">> Ada indikasi heteroskedastisitas (varian residual tidak konstan)\n\n")
}## >> Ada indikasi heteroskedastisitas (varian residual tidak konstan)plot(fitted_reg, residuals_reg,
main = "Fitted vs Residuals",
xlab = "Fitted Values",
ylab = "Residuals",
pch = 19, col = "blue")
abline(h = 0, col = "red", lwd = 2)
3. Multikolinearitas
vif = vif(reg)
# Hasil
cat("UJI MULTIKOLINEARITAS (VIF):\n")## UJI MULTIKOLINEARITAS (VIF):print(vif)## jml_km_buah jml_kt_buah jml_gar_buah lt_m2 lb_m2 lokasi_2
## 2.736282 3.256802 1.770303 2.097615 2.608533 1.500765
## lokasi_3 lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_8
## 1.484484 1.026962 1.038302 1.179102 1.081116 1.013142
## lokasi_9 lokasi_10 lokasi_11
## 1.008828 1.004617 1.031021if(all(vif < 10)){
cat(">> Tidak ada multikolinearitas serius (semua VIF < 10)\n")
} else {
cat(">> Ada multikolinearitas serius (VIF >= 10)\n")
}## >> Tidak ada multikolinearitas serius (semua VIF < 10)4. Autokorelasi
# Durbin-Watson test
dw = dwtest(reg)
# Hasil
cat("UJI AUTOKORELASI (Durbin-Watson):\n")## UJI AUTOKORELASI (Durbin-Watson):cat("DW =", dw$statistic, " | p-value =", dw$p.value, "\n")## DW = 1.906402 | p-value = 0.05644605if(dw$p.value > 0.05){
cat(">> Tidak ada autokorelasi pada residual\n\n")
} else {
cat(">> Ada indikasi autokorelasi pada residual\n\n")
}## >> Tidak ada autokorelasi pada residualDeteksi Outlier
# Bonferroni test untuk outlier signifikan
outlier = outlierTest(reg)
# Hasil
if(is.null(outlier)){
cat("✅ Tidak ada outlier signifikan berdasarkan uji Bonferroni\n")
} else {
print(outlier)
cat("⚠️ Ada outlier signifikan, periksa kembali data di atas\n")
}## rstudent unadjusted p-value Bonferroni p
## 528 48.814739 4.1601e-269 4.3057e-266
## 345 -12.338489 1.0616e-32 1.0987e-29
## 588 -8.371971 1.8525e-16 1.9174e-13
## 323 -5.124172 3.5730e-07 3.6981e-04
## 538 5.024800 5.9481e-07 6.1563e-04
## 346 -4.892394 1.1570e-06 1.1975e-03
## ⚠️ Ada outlier signifikan, periksa kembali data di atas# Plot residual vs fitted
plot(reg, which = 1)
# Tambahkan titik outlier (jika ada)
if(!is.null(outlier)){
points(reg$fitted.values[as.numeric(names(outlier$rstudent))],
outlier$rstudent,
col = "red", pch = 19)
text(reg$fitted.values[as.numeric(names(outlier$rstudent))],
outlier$rstudent,
labels = names(outlier$rstudent),
pos = 3, col = "red")
}
std_res = rstudent(reg)
outlier_idx = which(abs(std_res)>3)
# Jumlah outlier
cat("Jumlah outlier =", length(outlier_idx), "\n")## Jumlah outlier = 12# Kalau mau lihat data keberapa yang outlier
outlier_idx## 164 171 294 323 345 346 458 460 528 538 555 588
## 164 171 294 323 345 346 458 460 528 538 555 588# Hapus baris yang outlier
rumah.clean = rumah2_dummy[-outlier_idx, ]
str(rumah.clean)## tibble [1,025 × 16] (S3: tbl_df/tbl/data.frame)
## $ harga_rp : num [1:1025] 2.00e+09 7.99e+08 2.30e+09 6.25e+09 6.00e+08 1.10e+09 5.50e+08 2.60e+09 4.50e+09 3.90e+09 ...
## $ jml_km_buah : num [1:1025] 4 2 11 5 2 3 3 3 4 4 ...
## $ jml_kt_buah : num [1:1025] 3 2 4 5 1 2 3 3 4 4 ...
## $ jml_gar_buah: num [1:1025] 2 1 1 2 1 1 0 2 2 0 ...
## $ lt_m2 : num [1:1025] 90 102 90 294 66 60 60 98 288 80 ...
## $ lb_m2 : num [1:1025] 130 90 150 350 50 80 112 90 400 200 ...
## $ lokasi_2 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_3 : num [1:1025] 0 1 0 0 1 0 0 0 0 0 ...
## $ lokasi_4 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_5 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_6 : num [1:1025] 0 0 0 0 0 0 1 0 0 0 ...
## $ lokasi_7 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_8 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_9 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_10 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_11 : num [1:1025] 0 0 0 0 0 0 0 0 1 0 ...# Cek jumlah baris sebelum dan sesudah
cat("Sebelum:", nrow(rumah2_dummy), "baris\n")## Sebelum: 1037 bariscat("Sesudah:", nrow(rumah.clean), "baris\n")## Sesudah: 1025 barisTerlihat bahwa setelah baris berkurang setelah dilakukan penghapusan outlier, dari baris yang berjumlah 1037 baris menjadi 1025 baris.
# Konversi semua kolom lokasi jadi numeric di rumah.clean
rumah.clean[, grep("^lokasi", names(rumah.clean))] =
lapply(rumah.clean[, grep("^lokasi", names(rumah.clean))], as.numeric)
# Cek struktur data setelah perubahan
View(rumah.clean)
str(rumah.clean)## tibble [1,025 × 16] (S3: tbl_df/tbl/data.frame)
## $ harga_rp : num [1:1025] 2.00e+09 7.99e+08 2.30e+09 6.25e+09 6.00e+08 1.10e+09 5.50e+08 2.60e+09 4.50e+09 3.90e+09 ...
## $ jml_km_buah : num [1:1025] 4 2 11 5 2 3 3 3 4 4 ...
## $ jml_kt_buah : num [1:1025] 3 2 4 5 1 2 3 3 4 4 ...
## $ jml_gar_buah: num [1:1025] 2 1 1 2 1 1 0 2 2 0 ...
## $ lt_m2 : num [1:1025] 90 102 90 294 66 60 60 98 288 80 ...
## $ lb_m2 : num [1:1025] 130 90 150 350 50 80 112 90 400 200 ...
## $ lokasi_2 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_3 : num [1:1025] 0 1 0 0 1 0 0 0 0 0 ...
## $ lokasi_4 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_5 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_6 : num [1:1025] 0 0 0 0 0 0 1 0 0 0 ...
## $ lokasi_7 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_8 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_9 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_10 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_11 : num [1:1025] 0 0 0 0 0 0 0 0 1 0 ...# Regresi modedl tanpa NA dan outlier
reg2 = lm(harga_rp ~ ., data = rumah.clean)
summary(reg2)##
## Call:
## lm(formula = harga_rp ~ ., data = rumah.clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.373e+10 -6.413e+08 1.503e+07 5.332e+08 2.038e+10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.952e+08 2.649e+08 1.114 0.265407
## jml_km_buah -2.885e+08 1.100e+08 -2.622 0.008862 **
## jml_kt_buah -8.379e+07 1.126e+08 -0.744 0.456781
## jml_gar_buah 5.566e+08 8.786e+07 6.335 3.57e-10 ***
## lt_m2 9.761e+06 8.068e+05 12.097 < 2e-16 ***
## lb_m2 1.754e+07 8.780e+05 19.974 < 2e-16 ***
## lokasi_2 -6.807e+08 2.156e+08 -3.157 0.001641 **
## lokasi_3 -1.141e+09 2.239e+08 -5.098 4.11e-07 ***
## lokasi_4 -2.361e+09 8.368e+08 -2.821 0.004875 **
## lokasi_5 -1.747e+09 8.028e+08 -2.176 0.029791 *
## lokasi_6 -1.800e+09 3.364e+08 -5.352 1.08e-07 ***
## lokasi_7 -1.840e+09 5.226e+08 -3.520 0.000450 ***
## lokasi_8 -1.429e+09 1.308e+09 -1.092 0.275129
## lokasi_9 -8.802e+09 2.614e+09 -3.367 0.000789 ***
## lokasi_10 -2.510e+09 2.601e+09 -0.965 0.334723
## lokasi_11 -7.247e+09 9.581e+08 -7.564 8.80e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.594e+09 on 1009 degrees of freedom
## Multiple R-squared: 0.7808, Adjusted R-squared: 0.7775
## F-statistic: 239.6 on 15 and 1009 DF, p-value: < 2.2e-16Best Subset
best.subset = regsubsets(
harga_rp ~ .,
data = rumah.clean,
nvmax = ncol(rumah.clean) - 1
)
best.subset## Subset selection object
## Call: regsubsets.formula(harga_rp ~ ., data = rumah.clean, nvmax = ncol(rumah.clean) -
## 1)
## 15 Variables (and intercept)
## Forced in Forced out
## jml_km_buah FALSE FALSE
## jml_kt_buah FALSE FALSE
## jml_gar_buah FALSE FALSE
## lt_m2 FALSE FALSE
## lb_m2 FALSE FALSE
## lokasi_2 FALSE FALSE
## lokasi_3 FALSE FALSE
## lokasi_4 FALSE FALSE
## lokasi_5 FALSE FALSE
## lokasi_6 FALSE FALSE
## lokasi_7 FALSE FALSE
## lokasi_8 FALSE FALSE
## lokasi_9 FALSE FALSE
## lokasi_10 FALSE FALSE
## lokasi_11 FALSE FALSE
## 1 subsets of each size up to 15
## Selection Algorithm: exhaustive# Ringkasan hasil Best Subset Selection
summary.best = summary(best.subset)
summary.best## Subset selection object
## Call: regsubsets.formula(harga_rp ~ ., data = rumah.clean, nvmax = ncol(rumah.clean) -
## 1)
## 15 Variables (and intercept)
## Forced in Forced out
## jml_km_buah FALSE FALSE
## jml_kt_buah FALSE FALSE
## jml_gar_buah FALSE FALSE
## lt_m2 FALSE FALSE
## lb_m2 FALSE FALSE
## lokasi_2 FALSE FALSE
## lokasi_3 FALSE FALSE
## lokasi_4 FALSE FALSE
## lokasi_5 FALSE FALSE
## lokasi_6 FALSE FALSE
## lokasi_7 FALSE FALSE
## lokasi_8 FALSE FALSE
## lokasi_9 FALSE FALSE
## lokasi_10 FALSE FALSE
## lokasi_11 FALSE FALSE
## 1 subsets of each size up to 15
## Selection Algorithm: exhaustive
## jml_km_buah jml_kt_buah jml_gar_buah lt_m2 lb_m2 lokasi_2 lokasi_3
## 1 ( 1 ) " " " " " " " " "*" " " " "
## 2 ( 1 ) " " " " " " "*" "*" " " " "
## 3 ( 1 ) " " " " " " "*" "*" " " " "
## 4 ( 1 ) " " " " "*" "*" "*" " " " "
## 5 ( 1 ) "*" " " "*" "*" "*" " " " "
## 6 ( 1 ) "*" " " "*" "*" "*" " " " "
## 7 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 8 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 9 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 10 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 11 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 12 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 13 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 14 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_8 lokasi_9 lokasi_10
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " "*" " " " " " " " "
## 7 ( 1 ) " " " " "*" " " " " " " " "
## 8 ( 1 ) " " " " "*" " " " " "*" " "
## 9 ( 1 ) " " " " "*" "*" " " "*" " "
## 10 ( 1 ) " " " " "*" "*" " " "*" " "
## 11 ( 1 ) "*" " " "*" "*" " " "*" " "
## 12 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 13 ( 1 ) "*" "*" "*" "*" "*" "*" " "
## 14 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## lokasi_11
## 1 ( 1 ) " "
## 2 ( 1 ) " "
## 3 ( 1 ) "*"
## 4 ( 1 ) "*"
## 5 ( 1 ) "*"
## 6 ( 1 ) "*"
## 7 ( 1 ) "*"
## 8 ( 1 ) "*"
## 9 ( 1 ) "*"
## 10 ( 1 ) "*"
## 11 ( 1 ) "*"
## 12 ( 1 ) "*"
## 13 ( 1 ) "*"
## 14 ( 1 ) "*"
## 15 ( 1 ) "*"# Tampilkan ukuran model
summary.best$which # variabel apa saja yang masuk tiap model## (Intercept) jml_km_buah jml_kt_buah jml_gar_buah lt_m2 lb_m2 lokasi_2
## 1 TRUE FALSE FALSE FALSE FALSE TRUE FALSE
## 2 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
## 3 TRUE FALSE FALSE FALSE TRUE TRUE FALSE
## 4 TRUE FALSE FALSE TRUE TRUE TRUE FALSE
## 5 TRUE TRUE FALSE TRUE TRUE TRUE FALSE
## 6 TRUE TRUE FALSE TRUE TRUE TRUE FALSE
## 7 TRUE TRUE FALSE TRUE TRUE TRUE FALSE
## 8 TRUE TRUE FALSE TRUE TRUE TRUE FALSE
## 9 TRUE TRUE FALSE TRUE TRUE TRUE FALSE
## 10 TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## 11 TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## 12 TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## 13 TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## 14 TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## 15 TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## lokasi_3 lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_8 lokasi_9 lokasi_10
## 1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 5 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## 6 FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## 7 TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## 8 TRUE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
## 9 TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE
## 10 TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE
## 11 TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE
## 12 TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE
## 13 TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## 14 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## 15 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## lokasi_11
## 1 FALSE
## 2 FALSE
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## 7 TRUE
## 8 TRUE
## 9 TRUE
## 10 TRUE
## 11 TRUE
## 12 TRUE
## 13 TRUE
## 14 TRUE
## 15 TRUEsummary.best$adjr2 # adjusted R^2## [1] 0.7053393 0.7445768 0.7548747 0.7617038 0.7659493 0.7687739 0.7710128
## [8] 0.7729737 0.7744659 0.7755346 0.7768815 0.7776365 0.7776633 0.7776419
## [15] 0.7775437summary.best$cp # Mallows' Cp## [1] 334.04327 154.45513 108.04321 77.62854 59.11012 47.13229 37.85715
## [8] 29.87220 24.04330 20.15800 15.01567 12.57781 13.45628 14.55420
## [15] 16.00000summary.best$bic # BIC## [1] -1239.616 -1380.161 -1416.413 -1439.446 -1451.946 -1458.465 -1462.513
## [8] -1465.404 -1466.240 -1465.186 -1465.434 -1462.989 -1457.193 -1451.176
## [15] -1444.807# Plot kriteria untuk memilih model terbaik
par(mfrow = c(1, 3))
plot(summary.best$adjr2, type = "b", xlab = "Jumlah Variabel", ylab = "Adjusted R^2")
plot(summary.best$cp, type = "b", xlab = "Jumlah Variabel", ylab = "Cp")
plot(summary.best$bic, type = "b", xlab = "Jumlah Variabel", ylab = "BIC")
Adj \(R^2\)
semakin banyak variabel, model menjelaskan variasi data lebih baik ini ditunjukkan dari garis yang menaik
kenaikannya mulai melandai di sekitar 6-8 variabel
Cp. Mallow’s
model yang baik adalah dengan nilai Cp yang paling kecil
pada plot terlihat nilai Cp turun tajam pada saat jumlah variabel sekita 6-7 variabel
BIC
Model terbaik adalah model dengan nilai BIC yang paling kecil
pada plot terlihat titik terendah nilai BIC ada saat jumlah variabel sekitar 2-3 variablel
# Cari model terbaik berdasarkan kriteria
best.adjr2 = which.max(summary.best$adjr2) # Adjusted R²
best.cp = which.min(summary.best$cp) # Mallows' Cp
best.bic = which.min(summary.best$bic)
cat("Model terbaik berdasarkan Adjusted R² adalah M", best.adjr2, "\n")## Model terbaik berdasarkan Adjusted R² adalah M 13cat("Model terbaik berdasarkan Mallows' Cp adalah M", best.cp, "\n")## Model terbaik berdasarkan Mallows' Cp adalah M 12cat("Model terbaik berdasarkan BIC adalah M", best.bic, "\n\n")## Model terbaik berdasarkan BIC adalah M 9Stepwise selection
A. Forward Stepwise Selection Model
fw.model = regsubsets(
harga_rp ~ .,
data = rumah.clean,
nvmax = 15,
method = "forward"
)
summary.fw = summary(fw.model)
summary.fw## Subset selection object
## Call: regsubsets.formula(harga_rp ~ ., data = rumah.clean, nvmax = 15,
## method = "forward")
## 15 Variables (and intercept)
## Forced in Forced out
## jml_km_buah FALSE FALSE
## jml_kt_buah FALSE FALSE
## jml_gar_buah FALSE FALSE
## lt_m2 FALSE FALSE
## lb_m2 FALSE FALSE
## lokasi_2 FALSE FALSE
## lokasi_3 FALSE FALSE
## lokasi_4 FALSE FALSE
## lokasi_5 FALSE FALSE
## lokasi_6 FALSE FALSE
## lokasi_7 FALSE FALSE
## lokasi_8 FALSE FALSE
## lokasi_9 FALSE FALSE
## lokasi_10 FALSE FALSE
## lokasi_11 FALSE FALSE
## 1 subsets of each size up to 15
## Selection Algorithm: forward
## jml_km_buah jml_kt_buah jml_gar_buah lt_m2 lb_m2 lokasi_2 lokasi_3
## 1 ( 1 ) " " " " " " " " "*" " " " "
## 2 ( 1 ) " " " " " " "*" "*" " " " "
## 3 ( 1 ) " " " " " " "*" "*" " " " "
## 4 ( 1 ) " " " " "*" "*" "*" " " " "
## 5 ( 1 ) "*" " " "*" "*" "*" " " " "
## 6 ( 1 ) "*" " " "*" "*" "*" " " " "
## 7 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 8 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 9 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 10 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 11 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 12 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 13 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 14 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_8 lokasi_9 lokasi_10
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " "*" " " " " " " " "
## 7 ( 1 ) " " " " "*" " " " " " " " "
## 8 ( 1 ) " " " " "*" " " " " "*" " "
## 9 ( 1 ) " " " " "*" "*" " " "*" " "
## 10 ( 1 ) " " " " "*" "*" " " "*" " "
## 11 ( 1 ) "*" " " "*" "*" " " "*" " "
## 12 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 13 ( 1 ) "*" "*" "*" "*" "*" "*" " "
## 14 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## lokasi_11
## 1 ( 1 ) " "
## 2 ( 1 ) " "
## 3 ( 1 ) "*"
## 4 ( 1 ) "*"
## 5 ( 1 ) "*"
## 6 ( 1 ) "*"
## 7 ( 1 ) "*"
## 8 ( 1 ) "*"
## 9 ( 1 ) "*"
## 10 ( 1 ) "*"
## 11 ( 1 ) "*"
## 12 ( 1 ) "*"
## 13 ( 1 ) "*"
## 14 ( 1 ) "*"
## 15 ( 1 ) "*"length(summary.fw$adjr2)## [1] 15length(summary.fw$cp)## [1] 15length(summary.fw$bic)## [1] 15summary.fw = summary(fw.model)
# Buat tabel berisi Adjusted R², Cp, BIC
results = data.frame(
Jumlah_Variabel = 1:15,
Adj_R2 = summary.fw$adjr2,
Cp = summary.fw$cp,
BIC = summary.fw$bic
)
results## Jumlah_Variabel Adj_R2 Cp BIC
## 1 1 0.7053393 334.04327 -1239.616
## 2 2 0.7445768 154.45513 -1380.161
## 3 3 0.7548747 108.04321 -1416.413
## 4 4 0.7617038 77.62854 -1439.446
## 5 5 0.7659493 59.11012 -1451.946
## 6 6 0.7687739 47.13229 -1458.465
## 7 7 0.7710128 37.85715 -1462.513
## 8 8 0.7729737 29.87220 -1465.404
## 9 9 0.7744659 24.04330 -1466.240
## 10 10 0.7755346 20.15800 -1465.186
## 11 11 0.7768815 15.01567 -1465.434
## 12 12 0.7776365 12.57781 -1462.989
## 13 13 0.7776633 13.45628 -1457.193
## 14 14 0.7776419 14.55420 -1451.176
## 15 15 0.7775437 16.00000 -1444.807# Cari model terbaik berdasarkan masing-masing kriteria
best_adjr2 = which.max(results$Adj_R2) # indeks model dengan Adj R² terbesar
best_cp = which.min(results$Cp) # indeks model dengan Cp terkecil
best_bic = which.min(results$BIC) # indeks model dengan BIC terkecil# Visualisasi hasil seleksi
plot(fw.model, scale = "adjr2") # plot berdasarkan Adjusted R²
plot(fw.model, scale = "bic") # plot berdasarkan BIC
plot(fw.model, scale = "Cp") # plot berdasarkan Cp
# Model terbaik menurut Adj R²
best_adjr2 = which.max(results$Adj_R2)
cat("Model terbaik (Adj R²) adalah model ke-", best_adjr2,
"dengan", best_adjr2, "variabel:\n")## Model terbaik (Adj R²) adalah model ke- 13 dengan 13 variabel:print(coef(fw.model, best_adjr2))## (Intercept) jml_km_buah jml_gar_buah lt_m2 lb_m2 lokasi_2
## 291816672 -348858759 548018726 9874659 17315252 -663602572
## lokasi_3 lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_8
## -1120321342 -2309980205 -1704585505 -1791680736 -1826243671 -1384876063
## lokasi_9 lokasi_11
## -8635410307 -7260504250# Model terbaik menurut Cp
best_cp = which.min(results$Cp)
cat("\nModel terbaik (Cp) adalah model ke-", best_cp,
"dengan", best_cp, "variabel:\n")##
## Model terbaik (Cp) adalah model ke- 12 dengan 12 variabel:print(coef(fw.model, best_cp))## (Intercept) jml_km_buah jml_gar_buah lt_m2 lb_m2 lokasi_2
## 267743676 -347230229 552292132 9848803 17314322 -644029765
## lokasi_3 lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_9
## -1100884583 -2292192499 -1688046277 -1771373825 -1807443177 -8623285982
## lokasi_11
## -7236937130# Model terbaik menurut BIC
best_bic = which.min(results$BIC)
cat("\nModel terbaik (BIC) adalah model ke-", best_bic,
"dengan", best_bic, "variabel:\n")##
## Model terbaik (BIC) adalah model ke- 9 dengan 9 variabel:print(coef(fw.model, best_bic))## (Intercept) jml_km_buah jml_gar_buah lt_m2 lb_m2 lokasi_3
## -184990729 -341162178 559543578 9719290 17697131 -699142186
## lokasi_6 lokasi_7 lokasi_9 lokasi_11
## -1416377909 -1429298516 -8300556125 -6909912357# Ubah ke long format untuk ggplot (hasil forward stepwise)
results_long_fw = reshape2::melt(results, id.vars = "Jumlah_Variabel")
# Data titik model terbaik (Adj R², Cp, BIC) pada forward stepwise
best_points_fw = data.frame(
Jumlah_Variabel = c(best_adjr2, best_cp, best_bic),
value = c(results$Adj_R2[best_adjr2],
results$Cp[best_cp],
results$BIC[best_bic]),
variable = c("Adj_R2","Cp","BIC")
)
# Plot hasil forward stepwise dengan model terbaik ditandai
ggplot(results_long_fw, aes(x = Jumlah_Variabel, y = value, color = variable)) +
geom_line(size = 1) +
geom_point(size = 2) +
geom_point(data = best_points_fw,
aes(x = Jumlah_Variabel, y = value),
color = "red", size = 3, shape = 17) +
theme_minimal() +
labs(title = "Forward Stepwise Selection (Model Terbaik)",
y = "Nilai Kriteria",
x = "Jumlah Variabel",
color = "Kriteria")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Hasil model terbaik Forward Stepwise Selection
cat("Model terbaik (Adj R²):", best_adjr2, "variabel\n")## Model terbaik (Adj R²): 13 variabelcat("Model terbaik (Cp):", best_cp, "variabel\n")## Model terbaik (Cp): 12 variabelcat("Model terbaik (BIC):", best_bic, "variabel\n")## Model terbaik (BIC): 9 variabelModel terbaik berdasarkan \(R^2_{adj}\) adalah: model 11 dengan 11 variable
Model terbaik berdasarkan Cp. Mallow’s adalah : model 11 dengan 11 variable
Model terbaik berdasarkan BIC adalah : model 7 dengan 7 variable
B. Backward Stepwise Selection Model
# Backward Stepwise Selection
bw.model = regsubsets(harga_rp ~ .,
data = rumah.clean,
nbest = 1,
nvmax = NULL,
method = "backward")
# Ringkasan hasil backward stepwise
summary.bw = summary(bw.model)
# Buat tabel hasil
results.bw = data.frame(
Jumlah_Variabel = 1:length(summary.bw$adjr2),
Adj_R2 = summary.bw$adjr2,
Cp = summary.bw$cp,
BIC = summary.bw$bic
)
results.bw## Jumlah_Variabel Adj_R2 Cp BIC
## 1 1 0.7053393 334.04327 -1239.616
## 2 2 0.7445768 154.45513 -1380.161
## 3 3 0.7548747 108.04321 -1416.413
## 4 4 0.7617038 77.62854 -1439.446
## 5 5 0.7659493 59.11012 -1451.946
## 6 6 0.7687739 47.13229 -1458.465
## 7 7 0.7710128 37.85715 -1462.513
## 8 8 0.7729737 29.87220 -1465.404
## 9 9 0.7744659 24.04330 -1466.240
## 10 10 0.7755346 20.15800 -1465.186
## 11 11 0.7768815 15.01567 -1465.434
## 12 12 0.7776365 12.57781 -1462.989
## 13 13 0.7776633 13.45628 -1457.193
## 14 14 0.7776419 14.55420 -1451.176
## 15 15 0.7775437 16.00000 -1444.807# Cari model terbaik berdasarkan masing-masing kriteria
best_adjr2_bw = which.max(results.bw$Adj_R2)
best_cp_bw = which.min(results.bw$Cp)
best_bic_bw = which.min(results.bw$BIC)
# Tampilkan variabel yang terpilih
cat("\nVariabel pada model terbaik (Adj R²):\n")##
## Variabel pada model terbaik (Adj R²):print(coef(bw.model, best_adjr2_bw))## (Intercept) jml_km_buah jml_gar_buah lt_m2 lb_m2 lokasi_2
## 291816672 -348858759 548018726 9874659 17315252 -663602572
## lokasi_3 lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_8
## -1120321342 -2309980205 -1704585505 -1791680736 -1826243671 -1384876063
## lokasi_9 lokasi_11
## -8635410307 -7260504250cat("\nVariabel pada model terbaik (Cp):\n")##
## Variabel pada model terbaik (Cp):print(coef(bw.model, best_cp_bw))## (Intercept) jml_km_buah jml_gar_buah lt_m2 lb_m2 lokasi_2
## 267743676 -347230229 552292132 9848803 17314322 -644029765
## lokasi_3 lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_9
## -1100884583 -2292192499 -1688046277 -1771373825 -1807443177 -8623285982
## lokasi_11
## -7236937130cat("\nVariabel pada model terbaik (BIC):\n")##
## Variabel pada model terbaik (BIC):print(coef(bw.model, best_bic_bw))## (Intercept) jml_km_buah jml_gar_buah lt_m2 lb_m2 lokasi_3
## -184990729 -341162178 559543578 9719290 17697131 -699142186
## lokasi_6 lokasi_7 lokasi_9 lokasi_11
## -1416377909 -1429298516 -8300556125 -6909912357# Visualisasi hasil backward stepwise
plot(bw.model, scale = "adjr2") # pakai Adj R²
plot(bw.model, scale = "Cp") # pakai Mallows' Cp
plot(bw.model, scale = "bic") # pakai BIC
library(ggplot2)
library(reshape2)results_bw_long = reshape2::melt(results.bw, id.vars = "Jumlah_Variabel")
ggplot(results_bw_long, aes(x = Jumlah_Variabel, y = value, color = variable)) +
geom_line(size = 1) +
geom_point(size = 2) +
geom_point(data = data.frame(
Jumlah_Variabel = c(best_adjr2_bw, best_cp_bw, best_bic_bw),
value = c(results.bw$Adj_R2[best_adjr2_bw],
results.bw$Cp[best_cp_bw],
results.bw$BIC[best_bic_bw]),
variable = c("Adj_R2","Cp","BIC")
), aes(x = Jumlah_Variabel, y = value),
color = "red", size = 3, shape = 17) +
theme_minimal() +
labs(title = "Backward Stepwise Selection (Model Terbaik)",
y = "Nilai Kriteria",
x = "Jumlah Variabel",
color = "Kriteria")
# # Hasil model terbaik Backward Stepwise Selection
cat("Model terbaik (Backward - Adj R²):", best_adjr2_bw, "variabel\n")## Model terbaik (Backward - Adj R²): 13 variabelcat("Model terbaik (Backward - Cp):", best_cp_bw, "variabel\n")## Model terbaik (Backward - Cp): 12 variabelcat("Model terbaik (Backward - BIC):", best_bic_bw, "variabel\n")## Model terbaik (Backward - BIC): 9 variabelC. Hybrid Model
# Hybrid Stepwise Selection
hy.model = regsubsets(harga_rp ~ .,
data = rumah.clean,
nbest = 1,
nvmax = NULL,
method = "seqrep")
# Ringkasan hasil hybrid
summary.hy = summary(hy.model)
# Buat tabel hasil
results.hy = data.frame(
Jumlah_Variabel = 1:length(summary.hy$adjr2),
Adj_R2 = summary.hy$adjr2,
Cp = summary.hy$cp,
BIC = summary.hy$bic
)
results.hy## Jumlah_Variabel Adj_R2 Cp BIC
## 1 1 0.7053393 334.04327 -1239.616
## 2 2 0.7445768 154.45513 -1380.161
## 3 3 0.7548747 108.04321 -1416.413
## 4 4 0.7617038 77.62854 -1439.446
## 5 5 0.7559656 104.84207 -1409.130
## 6 6 0.7687739 47.13229 -1458.465
## 7 7 0.7710128 37.85715 -1462.513
## 8 8 0.7729737 29.87220 -1465.404
## 9 9 0.7744659 24.04330 -1466.240
## 10 10 0.7755346 20.15800 -1465.186
## 11 11 0.7768815 15.01567 -1465.434
## 12 12 0.7776365 12.57781 -1462.989
## 13 13 0.7776633 13.45628 -1457.193
## 14 14 0.7776419 14.55420 -1451.176
## 15 15 0.7775437 16.00000 -1444.807# Hasil model terbaik Hybrid Selection
best_adjr2_hy = which.max(results.hy$Adj_R2)
best_cp_hy = which.min(results.hy$Cp)
best_bic_hy = which.min(results.hy$BIC)
cat("Model terbaik (Hybrid - Adj R²):", best_adjr2_hy, "variabel\n")## Model terbaik (Hybrid - Adj R²): 13 variabelcat("Model terbaik (Hybrid - Cp):", best_cp_hy, "variabel\n")## Model terbaik (Hybrid - Cp): 12 variabelcat("Model terbaik (Hybrid - BIC):", best_bic_hy, "variabel\n")## Model terbaik (Hybrid - BIC): 9 variabel# Variabel yang terpilih
cat("\nVariabel pada model terbaik (Adj R²):\n")##
## Variabel pada model terbaik (Adj R²):print(coef(hy.model, best_adjr2_hy))## (Intercept) jml_km_buah jml_gar_buah lt_m2 lb_m2 lokasi_2
## 291816672 -348858759 548018726 9874659 17315252 -663602572
## lokasi_3 lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_8
## -1120321342 -2309980205 -1704585505 -1791680736 -1826243671 -1384876063
## lokasi_9 lokasi_11
## -8635410307 -7260504250cat("\nVariabel pada model terbaik (Cp):\n")##
## Variabel pada model terbaik (Cp):print(coef(hy.model, best_cp_hy))## (Intercept) jml_km_buah jml_gar_buah lt_m2 lb_m2 lokasi_2
## 267743676 -347230229 552292132 9848803 17314322 -644029765
## lokasi_3 lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_9
## -1100884583 -2292192499 -1688046277 -1771373825 -1807443177 -8623285982
## lokasi_11
## -7236937130cat("\nVariabel pada model terbaik (BIC):\n")##
## Variabel pada model terbaik (BIC):print(coef(hy.model, best_bic_hy))## (Intercept) jml_km_buah jml_gar_buah lt_m2 lb_m2 lokasi_3
## -184990729 -341162178 559543578 9719290 17697131 -699142186
## lokasi_6 lokasi_7 lokasi_9 lokasi_11
## -1416377909 -1429298516 -8300556125 -6909912357# Ubah ke long format untuk ggplot (hasil hybrid stepwise)
results_hy_long = reshape2::melt(results.hy, id.vars = "Jumlah_Variabel")
# Data titik model terbaik (Adj R², Cp, BIC) pada hybrid stepwise
best_points_hy = data.frame(
Jumlah_Variabel = c(best_adjr2_hy, best_cp_hy, best_bic_hy),
value = c(results.hy$Adj_R2[best_adjr2_hy],
results.hy$Cp[best_cp_hy],
results.hy$BIC[best_bic_hy]),
variable = c("Adj_R2","Cp","BIC")
)
# Plot hasil hybrid stepwise dengan model terbaik ditandai
ggplot(results_hy_long, aes(x = Jumlah_Variabel, y = value, color = variable)) +
geom_line(size = 1) +
geom_point(size = 2) +
geom_point(data = best_points_hy,
aes(x = Jumlah_Variabel, y = value),
color = "red", size = 3, shape = 17) +
theme_minimal() +
labs(title = "Hybrid Stepwise Selection (Model Terbaik)",
y = "Nilai Kriteria",
x = "Jumlah Variabel",
color = "Kriteria")
library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## select# model penuh
model_full = lm(harga_rp ~ ., data = rumah.clean)
# stepwise selection (AIC-based)
model_best = stepAIC(model_full, direction = "both")## Start: AIC=44452.32
## harga_rp ~ jml_km_buah + jml_kt_buah + jml_gar_buah + lt_m2 +
## lb_m2 + lokasi_2 + lokasi_3 + lokasi_4 + lokasi_5 + lokasi_6 +
## lokasi_7 + lokasi_8 + lokasi_9 + lokasi_10 + lokasi_11
##
## Df Sum of Sq RSS AIC
## - jml_kt_buah 1 3.7280e+18 6.7910e+21 44451
## - lokasi_10 1 6.2655e+18 6.7936e+21 44451
## - lokasi_8 1 8.0203e+18 6.7953e+21 44452
## <none> 6.7873e+21 44452
## - lokasi_5 1 3.1850e+19 6.8192e+21 44455
## - jml_km_buah 1 4.6261e+19 6.8336e+21 44457
## - lokasi_4 1 5.3549e+19 6.8409e+21 44458
## - lokasi_2 1 6.7044e+19 6.8544e+21 44460
## - lokasi_9 1 7.6255e+19 6.8636e+21 44462
## - lokasi_7 1 8.3361e+19 6.8707e+21 44463
## - lokasi_3 1 1.7479e+20 6.9621e+21 44476
## - lokasi_6 1 1.9268e+20 6.9800e+21 44479
## - jml_gar_buah 1 2.6995e+20 7.0573e+21 44490
## - lokasi_11 1 3.8483e+20 7.1722e+21 44507
## - lt_m2 1 9.8444e+20 7.7718e+21 44589
## - lb_m2 1 2.6838e+21 9.4712e+21 44792
##
## Step: AIC=44450.88
## harga_rp ~ jml_km_buah + jml_gar_buah + lt_m2 + lb_m2 + lokasi_2 +
## lokasi_3 + lokasi_4 + lokasi_5 + lokasi_6 + lokasi_7 + lokasi_8 +
## lokasi_9 + lokasi_10 + lokasi_11
##
## Df Sum of Sq RSS AIC
## - lokasi_10 1 6.0681e+18 6.7971e+21 44450
## - lokasi_8 1 7.6629e+18 6.7987e+21 44450
## <none> 6.7910e+21 44451
## + jml_kt_buah 1 3.7280e+18 6.7873e+21 44452
## - lokasi_5 1 3.0728e+19 6.8218e+21 44454
## - lokasi_4 1 5.1856e+19 6.8429e+21 44457
## - lokasi_2 1 6.5748e+19 6.8568e+21 44459
## - lokasi_9 1 7.3959e+19 6.8650e+21 44460
## - lokasi_7 1 8.2955e+19 6.8740e+21 44461
## - jml_km_buah 1 1.6168e+20 6.9527e+21 44473
## - lokasi_3 1 1.7213e+20 6.9632e+21 44475
## - lokasi_6 1 1.9281e+20 6.9839e+21 44478
## - jml_gar_buah 1 2.6674e+20 7.0578e+21 44488
## - lokasi_11 1 3.8742e+20 7.1785e+21 44506
## - lt_m2 1 1.0506e+21 7.8417e+21 44596
## - lb_m2 1 2.9552e+21 9.7463e+21 44819
##
## Step: AIC=44449.8
## harga_rp ~ jml_km_buah + jml_gar_buah + lt_m2 + lb_m2 + lokasi_2 +
## lokasi_3 + lokasi_4 + lokasi_5 + lokasi_6 + lokasi_7 + lokasi_8 +
## lokasi_9 + lokasi_11
##
## Df Sum of Sq RSS AIC
## - lokasi_8 1 7.5443e+18 6.8047e+21 44449
## <none> 6.7971e+21 44450
## + lokasi_10 1 6.0681e+18 6.7910e+21 44451
## + jml_kt_buah 1 3.5306e+18 6.7936e+21 44451
## - lokasi_5 1 3.0429e+19 6.8275e+21 44452
## - lokasi_4 1 5.1513e+19 6.8486e+21 44456
## - lokasi_2 1 6.3990e+19 6.8611e+21 44457
## - lokasi_9 1 7.3930e+19 6.8710e+21 44459
## - lokasi_7 1 8.2186e+19 6.8793e+21 44460
## - jml_km_buah 1 1.6009e+20 6.9572e+21 44472
## - lokasi_3 1 1.6952e+20 6.9666e+21 44473
## - lokasi_6 1 1.9099e+20 6.9881e+21 44476
## - jml_gar_buah 1 2.6877e+20 7.0659e+21 44488
## - lokasi_11 1 3.8665e+20 7.1838e+21 44505
## - lt_m2 1 1.0494e+21 7.8466e+21 44595
## - lb_m2 1 2.9547e+21 9.7518e+21 44818
##
## Step: AIC=44448.93
## harga_rp ~ jml_km_buah + jml_gar_buah + lt_m2 + lb_m2 + lokasi_2 +
## lokasi_3 + lokasi_4 + lokasi_5 + lokasi_6 + lokasi_7 + lokasi_9 +
## lokasi_11
##
## Df Sum of Sq RSS AIC
## <none> 6.8047e+21 44449
## + lokasi_8 1 7.5443e+18 6.7971e+21 44450
## + lokasi_10 1 5.9496e+18 6.7987e+21 44450
## + jml_kt_buah 1 3.1876e+18 6.8015e+21 44450
## - lokasi_5 1 2.9852e+19 6.8345e+21 44451
## - lokasi_4 1 5.0743e+19 6.8554e+21 44455
## - lokasi_2 1 6.0719e+19 6.8654e+21 44456
## - lokasi_9 1 7.3724e+19 6.8784e+21 44458
## - lokasi_7 1 8.0596e+19 6.8853e+21 44459
## - jml_km_buah 1 1.5867e+20 6.9633e+21 44471
## - lokasi_3 1 1.6481e+20 6.9695e+21 44471
## - lokasi_6 1 1.8730e+20 6.9920e+21 44475
## - jml_gar_buah 1 2.7357e+20 7.0782e+21 44487
## - lokasi_11 1 3.8435e+20 7.1890e+21 44503
## - lt_m2 1 1.0449e+21 7.8496e+21 44593
## - lb_m2 1 2.9544e+21 9.7590e+21 44817summary(model_best)##
## Call:
## lm(formula = harga_rp ~ jml_km_buah + jml_gar_buah + lt_m2 +
## lb_m2 + lokasi_2 + lokasi_3 + lokasi_4 + lokasi_5 + lokasi_6 +
## lokasi_7 + lokasi_9 + lokasi_11, data = rumah.clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.368e+10 -6.527e+08 1.552e+07 5.456e+08 2.057e+10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.677e+08 2.628e+08 1.019 0.308549
## jml_km_buah -3.472e+08 7.148e+07 -4.858 1.38e-06 ***
## jml_gar_buah 5.523e+08 8.659e+07 6.379 2.71e-10 ***
## lt_m2 9.849e+06 7.900e+05 12.466 < 2e-16 ***
## lb_m2 1.731e+07 8.260e+05 20.961 < 2e-16 ***
## lokasi_2 -6.440e+08 2.143e+08 -3.005 0.002721 **
## lokasi_3 -1.101e+09 2.224e+08 -4.951 8.65e-07 ***
## lokasi_4 -2.292e+09 8.344e+08 -2.747 0.006119 **
## lokasi_5 -1.688e+09 8.011e+08 -2.107 0.035358 *
## lokasi_6 -1.771e+09 3.356e+08 -5.278 1.60e-07 ***
## lokasi_7 -1.807e+09 5.221e+08 -3.462 0.000558 ***
## lokasi_9 -8.623e+09 2.604e+09 -3.311 0.000962 ***
## lokasi_11 -7.237e+09 9.572e+08 -7.560 8.98e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.593e+09 on 1012 degrees of freedom
## Multiple R-squared: 0.7802, Adjusted R-squared: 0.7776
## F-statistic: 299.4 on 12 and 1012 DF, p-value: < 2.2e-16Mencari Metode Optimal
library(tictoc)##
## Attaching package: 'tictoc'## The following object is masked from 'package:data.table':
##
## shiftlibrary(leaps)# Best subset
tic("Best Subset")
best.subset = regsubsets(
harga_rp ~ .,
data = rumah.clean,
nvmax = ncol(rumah.clean) - 1
)
toc()## Best Subset: 0 sec elapsed# FW Selection
tic("Forward Selection")
fw.model = regsubsets(
harga_rp ~ .,
data = rumah.clean,
nvmax = 15,
method = "forward"
)
toc()## Forward Selection: 0.02 sec elapsed# Backward Stepwise Selection
tic("Backward Selection")
bw.model = regsubsets(harga_rp ~ .,
data = rumah.clean,
nbest = 1,
nvmax = NULL,
method = "backward")
toc()## Backward Selection: 0.01 sec elapsed# Hybrid Selection
tic("Hybrid Selection")
hy.model = regsubsets(harga_rp ~ .,
data = rumah.clean,
nbest = 1,
nvmax = NULL,
method = "seqrep")
toc()## Hybrid Selection: 0 sec elapsedCek Simetris
# Install dan load package kalau belum
library(glmnet)## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack## Loaded glmnet 4.1-10# Siapkan X dan y
X = model.matrix(harga_rp ~ ., data = rumah.clean)[, -1] # matrix prediktor
y = rumah.clean[["harga_rp"]] # ambil kolom respon secara pasti
length(y) # harus 1025## [1] 1025nrow(X) # harus 1025## [1] 1025str(rumah.clean)## tibble [1,025 × 16] (S3: tbl_df/tbl/data.frame)
## $ harga_rp : num [1:1025] 2.00e+09 7.99e+08 2.30e+09 6.25e+09 6.00e+08 1.10e+09 5.50e+08 2.60e+09 4.50e+09 3.90e+09 ...
## $ jml_km_buah : num [1:1025] 4 2 11 5 2 3 3 3 4 4 ...
## $ jml_kt_buah : num [1:1025] 3 2 4 5 1 2 3 3 4 4 ...
## $ jml_gar_buah: num [1:1025] 2 1 1 2 1 1 0 2 2 0 ...
## $ lt_m2 : num [1:1025] 90 102 90 294 66 60 60 98 288 80 ...
## $ lb_m2 : num [1:1025] 130 90 150 350 50 80 112 90 400 200 ...
## $ lokasi_2 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_3 : num [1:1025] 0 1 0 0 1 0 0 0 0 0 ...
## $ lokasi_4 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_5 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_6 : num [1:1025] 0 0 0 0 0 0 1 0 0 0 ...
## $ lokasi_7 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_8 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_9 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_10 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_11 : num [1:1025] 0 0 0 0 0 0 0 0 1 0 ...# Histogram
hist(rumah.clean$harga_rp,
main = "Histogram Harga Rumah",
xlab = "harga_rp")
# Density
plot(density(rumah.clean$harga_rp),
main = "Density Harga Rumah")
# Boxplot
boxplot(rumah.clean$harga_rp,
main = "Boxplot Harga Rumah",
horizontal = TRUE)
# Skewness
library(moments)
skewness(rumah.clean$harga_rp)## [1] 4.683263# QQ plot
qqnorm(rumah.clean$harga_rp)
qqline(rumah.clean$harga_rp, col = "red")
Transformasi Log
# Transformasi Log
rumah.log = rumah.clean
rumah.log$log_harga = log(rumah.clean$harga_rp)
str(rumah.log)## tibble [1,025 × 17] (S3: tbl_df/tbl/data.frame)
## $ harga_rp : num [1:1025] 2.00e+09 7.99e+08 2.30e+09 6.25e+09 6.00e+08 1.10e+09 5.50e+08 2.60e+09 4.50e+09 3.90e+09 ...
## $ jml_km_buah : num [1:1025] 4 2 11 5 2 3 3 3 4 4 ...
## $ jml_kt_buah : num [1:1025] 3 2 4 5 1 2 3 3 4 4 ...
## $ jml_gar_buah: num [1:1025] 2 1 1 2 1 1 0 2 2 0 ...
## $ lt_m2 : num [1:1025] 90 102 90 294 66 60 60 98 288 80 ...
## $ lb_m2 : num [1:1025] 130 90 150 350 50 80 112 90 400 200 ...
## $ lokasi_2 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_3 : num [1:1025] 0 1 0 0 1 0 0 0 0 0 ...
## $ lokasi_4 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_5 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_6 : num [1:1025] 0 0 0 0 0 0 1 0 0 0 ...
## $ lokasi_7 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_8 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_9 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_10 : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ lokasi_11 : num [1:1025] 0 0 0 0 0 0 0 0 1 0 ...
## $ log_harga : num [1:1025] 21.4 20.5 21.6 22.6 20.2 ...# Reset layout ke 1x1 (supaya tidak konflik dengan par(mfrow))
par(mfrow = c(1,1), mar = c(4,4,2,1))# Histogram log_harga
hist(rumah.log$log_harga, breaks = 30,
main = "Histogram Harga Rumah (Log)",
xlab = "log(harga_rp)",
col = "skyblue", border = "white")
# Density plot log_harga
plot(density(rumah.log$log_harga),
main = "Density Harga Rumah (Log)",
xlab = "log(harga_rp)",
col = "blue", lwd = 2)
# Boxplot log_harga
boxplot(rumah.log$log_harga,
main = "Boxplot Harga Rumah (Log)",
horizontal = TRUE,
col = "orange")
# QQ plot log_harga
qqnorm(rumah.log$log_harga,
main = "QQ Plot Harga Rumah (Log)")
qqline(rumah.log$log_harga, col = "red", lwd = 2)
library(moments)
cat("Skewness harga asli :", skewness(rumah.clean$harga_rp), "\n")## Skewness harga asli : 4.683263cat("Skewness harga (log) :", skewness(rumah.log$log_harga), "\n")## Skewness harga (log) : 0.6709699# Standarisasi data
rumah.log.s = scale(rumah.log [, -1])
rumah.log.s## jml_km_buah jml_kt_buah jml_gar_buah lt_m2 lb_m2
## [1,] 0.5655616 0.3458367 0.90079381 -0.369829400 -0.187370149
## [2,] -0.8818807 -0.3293683 0.06897971 -0.304338001 -0.409648905
## [3,] 5.6316100 1.0210418 0.06897971 -0.369829400 -0.076230770
## [4,] 1.2892828 1.6962468 0.90079381 0.743524390 1.035163010
## [5,] -0.8818807 -1.0045733 0.06897971 -0.500812199 -0.631927661
## [6,] -0.1581596 -0.3293683 0.06897971 -0.533557899 -0.465218594
## [7,] -0.1581596 0.3458367 -0.76283439 -0.533557899 -0.287395589
## [8,] -0.1581596 0.3458367 0.90079381 -0.326168467 -0.409648905
## [9,] 0.5655616 1.0210418 0.90079381 0.710778690 1.313011455
## [10,] 0.5655616 1.0210418 -0.76283439 -0.424405566 0.201617675
## [11,] 0.5655616 1.0210418 2.56442201 4.083585760 1.313011455
## [12,] -0.1581596 0.3458367 0.06897971 -0.293422767 -0.354079216
## [13,] 0.5655616 1.0210418 2.56442201 1.851420563 0.868453943
## [14,] 1.2892828 1.6962468 0.90079381 0.350575993 0.073807390
## [15,] -0.1581596 0.3458367 0.06897971 -0.615422148 -0.476332531
## [16,] 1.2892828 1.0210418 0.90079381 0.454270709 1.868708345
## [17,] 0.5655616 0.3458367 0.06897971 0.252338894 0.290529177
## [18,] -0.8818807 -0.3293683 0.06897971 -0.686371164 -0.604142816
## [19,] 0.5655616 0.3458367 1.73260791 0.230508428 0.823998192
## [20,] 2.7367252 0.3458367 -0.76283439 1.376607918 0.646175187
## [21,] 1.2892828 0.3458367 1.73260791 1.638573515 0.979593321
## [22,] -0.1581596 0.3458367 0.06897971 0.694405840 0.823998192
## [23,] -0.1581596 0.3458367 0.06897971 1.867793413 1.313011455
## [24,] -0.1581596 0.3458367 -0.76283439 0.776270090 0.757314565
## [25,] -0.1581596 0.3458367 0.06897971 0.694405840 0.823998192
## [26,] -0.8818807 -0.3293683 0.06897971 0.257796511 -0.131800459
## [27,] 0.5655616 1.6962468 0.90079381 0.547050192 1.313011455
## [28,] -0.8818807 -0.3293683 0.06897971 -0.615422148 -0.609699785
## [29,] 0.5655616 1.6962468 0.90079381 0.841761489 1.301897517
## [30,] 0.5655616 1.0210418 0.90079381 -0.397117483 0.018237701
## [31,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.737510070
## [32,] 1.2892828 1.0210418 0.06897971 -0.118779036 0.284972208
## [33,] 0.5655616 -0.3293683 0.90079381 0.366948843 0.479466120
## [34,] 0.5655616 0.3458367 0.90079381 -0.326168467 -0.209598024
## [35,] 1.2892828 1.0210418 0.90079381 -0.075118103 0.229402519
## [36,] 0.5655616 1.0210418 0.90079381 1.431184084 2.424405236
## [37,] -0.1581596 -0.3293683 0.06897971 -0.042372403 -0.242939838
## [38,] -0.8818807 -1.0045733 0.06897971 -0.500812199 -0.659712505
## [39,] 0.5655616 -0.3293683 0.90079381 0.317830294 -0.354079216
## [40,] -0.1581596 1.0210418 0.90079381 -0.206100901 -0.131800459
## [41,] 2.0130040 0.3458367 2.56442201 0.776270090 1.590859900
## [42,] -0.1581596 0.3458367 0.06897971 -0.369829400 -0.559687065
## [43,] 1.2892828 -0.3293683 0.06897971 -0.184270435 0.007123763
## [44,] 1.2892828 -0.3293683 0.06897971 -0.184270435 0.007123763
## [45,] 2.0130040 0.3458367 0.90079381 0.279626977 0.201617675
## [46,] 0.5655616 0.3458367 1.73260791 3.505078398 0.757314565
## [47,] 0.5655616 1.0210418 0.06897971 0.219593195 0.479466120
## [48,] -0.1581596 0.3458367 0.90079381 -0.124236652 -0.298509527
## [49,] -0.1581596 -0.3293683 0.90079381 -0.440778416 -0.409648905
## [50,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.615256754
## [51,] 0.5655616 1.0210418 0.90079381 0.448813093 0.757314565
## [52,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.520788283
## [53,] -0.1581596 0.3458367 0.06897971 -0.135151885 -0.020661081
## [54,] -0.1581596 -0.3293683 0.06897971 -0.549930748 -0.509674345
## [55,] 0.5655616 1.0210418 0.06897971 -0.446236033 1.174087233
## [56,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.520788283
## [57,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.520788283
## [58,] 0.5655616 1.0210418 0.90079381 -0.446236033 1.174087233
## [59,] -0.1581596 -0.3293683 0.06897971 -0.369829400 -0.659712505
## [60,] -0.1581596 -0.3293683 0.06897971 -0.309795617 -0.409648905
## [61,] 1.2892828 1.6962468 0.06897971 0.776270090 0.896238787
## [62,] -0.1581596 -0.3293683 0.06897971 -0.517185049 -0.354079216
## [63,] -0.1581596 -0.3293683 0.06897971 -0.533557899 -0.604142816
## [64,] 2.7367252 2.3714518 2.56442201 0.503389259 1.701999278
## [65,] -0.1581596 -0.3293683 0.90079381 -0.506269815 -0.465218594
## [66,] 0.5655616 0.3458367 -0.76283439 -0.653625464 -0.559687065
## [67,] -0.8818807 0.3458367 -0.76283439 -0.424405566 0.423896431
## [68,] 0.5655616 1.6962468 0.90079381 -0.151524735 0.990707259
## [69,] -0.1581596 -0.3293683 0.06897971 -0.468066499 -0.493003438
## [70,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.520788283
## [71,] 0.5655616 1.6962468 4.22805021 4.913143486 1.035163010
## [72,] 1.2892828 0.3458367 0.06897971 1.704064915 0.201617675
## [73,] 0.5655616 -0.3293683 -0.76283439 -0.369829400 -0.020661081
## [74,] 1.2892828 1.6962468 2.56442201 0.994574754 1.590859900
## [75,] -0.1581596 -1.0045733 0.90079381 -0.293422767 -0.159585304
## [76,] 1.2892828 1.6962468 0.90079381 2.473588858 2.424405236
## [77,] -0.1581596 0.3458367 -0.76283439 0.306915061 -0.298509527
## [78,] 1.2892828 0.3458367 1.73260791 0.770812473 0.757314565
## [79,] -0.1581596 -0.3293683 -0.76283439 -0.025999553 -0.109572584
## [80,] -0.1581596 0.3458367 -0.76283439 -0.353456550 -0.226268931
## [81,] 2.0130040 1.6962468 1.73260791 7.521884229 2.702253681
## [82,] 0.5655616 0.3458367 0.06897971 -0.042372403 0.090478297
## [83,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.659712505
## [84,] 0.5655616 1.0210418 0.90079381 0.710778690 1.313011455
## [85,] -0.1581596 -0.3293683 -0.76283439 -0.440778416 -0.465218594
## [86,] -0.1581596 0.3458367 -0.76283439 -0.178812818 -0.187370149
## [87,] -0.1581596 -0.3293683 -0.76283439 -0.369829400 -0.298509527
## [88,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.576357972
## [89,] -0.1581596 -0.3293683 -0.76283439 -0.369829400 -0.437433749
## [90,] -0.1581596 0.3458367 0.06897971 0.121356096 0.073807390
## [91,] 1.2892828 3.0466569 3.39623611 6.233886707 5.058408495
## [92,] 0.5655616 0.3458367 0.90079381 -0.206100901 -0.003990175
## [93,] 1.2892828 -0.3293683 0.06897971 -0.184270435 0.007123763
## [94,] 1.2892828 1.6962468 2.56442201 2.937486270 0.896238787
## [95,] 1.2892828 -0.3293683 0.06897971 -0.184270435 0.007123763
## [96,] -0.8818807 -1.0045733 0.06897971 -0.468066499 -0.709725225
## [97,] 4.9078888 5.0722719 0.06897971 -0.451693649 -0.076230770
## [98,] -0.8818807 -1.0045733 -0.76283439 -0.468066499 -0.709725225
## [99,] 4.9078888 5.0722719 -0.76283439 -0.484439349 -0.242939838
## [100,] -0.1581596 0.3458367 0.06897971 -0.511727432 -0.509674345
## [101,] -0.1581596 0.3458367 0.06897971 -0.560845982 -0.481889500
## [102,] 0.5655616 1.0210418 0.90079381 0.503389259 2.224354355
## [103,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.520788283
## [104,] 1.2892828 1.0210418 0.90079381 0.230508428 0.757314565
## [105,] 2.7367252 1.0210418 0.90079381 1.185591336 0.073807390
## [106,] 0.5655616 1.0210418 0.06897971 -0.206100901 -0.081787739
## [107,] 0.5655616 1.0210418 0.06897971 -0.206100901 -0.081787739
## [108,] -0.1581596 0.3458367 0.06897971 -0.511727432 -0.509674345
## [109,] -0.1581596 0.3458367 0.06897971 -0.380744633 -0.298509527
## [110,] 0.5655616 0.3458367 0.90079381 0.798100556 0.340541897
## [111,] -0.1581596 1.0210418 0.90079381 -0.315253234 0.062693452
## [112,] -0.1581596 -0.3293683 0.90079381 0.274169361 -0.076230770
## [113,] 1.2892828 2.3714518 2.56442201 1.594912582 1.868708345
## [114,] 0.5655616 1.0210418 1.73260791 0.814473406 1.090732699
## [115,] -0.1581596 -0.3293683 0.90079381 0.317830294 -0.076230770
## [116,] -0.8818807 1.0210418 0.90079381 0.170474645 -0.215154993
## [117,] 0.5655616 0.3458367 0.06897971 -0.162439969 -0.431876780
## [118,] -0.1581596 0.3458367 0.90079381 -0.478981732 -0.437433749
## [119,] -0.1581596 0.3458367 0.06897971 -0.599049298 -0.465218594
## [120,] -0.1581596 -0.3293683 0.06897971 -0.533557899 -0.509674345
## [121,] -0.1581596 0.3458367 0.06897971 0.225050811 -0.242939838
## [122,] -0.1581596 1.0210418 0.90079381 -0.118779036 0.757314565
## [123,] -0.8818807 -0.3293683 0.06897971 -0.533557899 -0.715282194
## [124,] -0.8818807 -0.3293683 0.06897971 -0.206100901 -0.520788283
## [125,] 0.5655616 0.3458367 0.06897971 -0.271592301 0.479466120
## [126,] -0.1581596 -0.3293683 -0.76283439 -0.020541936 -0.520788283
## [127,] -0.8818807 -1.0045733 0.06897971 -0.697286397 -0.631927661
## [128,] 0.5655616 0.3458367 0.90079381 0.230508428 0.512807933
## [129,] -0.8818807 -1.0045733 0.06897971 -0.287965151 -0.481889500
## [130,] -0.1581596 -0.3293683 0.06897971 -0.206100901 -0.187370149
## [131,] -0.1581596 0.3458367 0.06897971 -0.042372403 0.507250964
## [132,] -0.8818807 -0.3293683 0.06897971 -0.533557899 -0.615256754
## [133,] -0.1581596 0.3458367 0.06897971 -0.326168467 -0.254053775
## [134,] 1.2892828 1.0210418 0.90079381 -0.206100901 0.090478297
## [135,] 1.2892828 1.0210418 0.90079381 0.285084594 0.423896431
## [136,] -0.1581596 -0.3293683 0.06897971 -0.064202869 -0.354079216
## [137,] 1.2892828 2.3714518 0.90079381 0.939998588 2.424405236
## [138,] 1.2892828 -0.3293683 -0.76283439 3.068469069 1.868708345
## [139,] -0.1581596 0.3458367 0.90079381 -0.435320799 -0.376307091
## [140,] 1.2892828 1.0210418 0.90079381 -0.042372403 0.479466120
## [141,] 1.2892828 1.6962468 0.90079381 0.743524390 1.035163010
## [142,] 0.5655616 1.0210418 0.90079381 0.230508428 0.340541897
## [143,] 2.0130040 1.0210418 0.90079381 2.004233829 1.313011455
## [144,] -0.1581596 0.3458367 0.90079381 -0.435320799 -0.376307091
## [145,] 1.2892828 -0.3293683 -0.76283439 3.068469069 1.868708345
## [146,] -0.1581596 0.3458367 0.90079381 -0.435320799 -0.376307091
## [147,] 1.2892828 1.0210418 0.90079381 -0.042372403 0.479466120
## [148,] 2.0130040 1.0210418 0.90079381 2.004233829 1.313011455
## [149,] 0.5655616 -0.3293683 0.06897971 -0.369829400 -0.409648905
## [150,] 0.5655616 0.3458367 0.90079381 0.563423042 0.423896431
## [151,] 0.5655616 0.3458367 0.90079381 2.413555075 1.313011455
## [152,] 0.5655616 1.0210418 0.06897971 -0.025999553 0.379440680
## [153,] 0.5655616 1.0210418 0.90079381 0.230508428 0.435010369
## [154,] 1.2892828 1.0210418 0.90079381 0.628914441 0.890681819
## [155,] -0.1581596 0.3458367 0.06897971 -0.533557899 -0.354079216
## [156,] -0.1581596 -0.3293683 0.06897971 -0.500812199 -0.548573127
## [157,] -0.8818807 0.3458367 0.06897971 -0.424405566 -0.437433749
## [158,] -0.1581596 -0.3293683 0.90079381 -0.478981732 -0.465218594
## [159,] -0.1581596 0.3458367 4.22805021 2.435385541 1.313011455
## [160,] 2.7367252 3.0466569 2.56442201 1.300201285 3.535799016
## [161,] 0.5655616 0.3458367 0.06897971 -0.206100901 0.201617675
## [162,] 0.5655616 -0.3293683 0.06897971 -0.206100901 0.034908608
## [163,] -0.1581596 -0.3293683 0.06897971 -0.249761834 -0.242939838
## [164,] -0.1581596 -0.3293683 0.06897971 -0.315253234 -0.465218594
## [165,] 0.5655616 0.3458367 0.90079381 -0.135151885 -0.437433749
## [166,] 0.5655616 1.0210418 0.90079381 2.462673624 7.425677248
## [167,] -0.1581596 -1.0045733 -0.76283439 -0.167897585 -0.631927661
## [168,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.743067039
## [169,] 0.5655616 1.0210418 0.90079381 -0.446236033 1.174087233
## [170,] -0.8818807 -1.0045733 0.90079381 -0.238846601 -0.631927661
## [171,] 2.0130040 3.0466569 2.56442201 2.653690206 3.535799016
## [172,] 0.5655616 0.3458367 0.90079381 0.137728945 0.479466120
## [173,] -0.1581596 0.3458367 0.06897971 0.012203763 -0.276281651
## [174,] 0.5655616 0.3458367 0.06897971 -0.577218831 -0.476332531
## [175,] 0.5655616 -0.3293683 4.22805021 2.440843158 0.201617675
## [176,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.726396132
## [177,] -0.1581596 -0.3293683 0.90079381 -0.402575100 -0.470775563
## [178,] 0.5655616 1.0210418 0.90079381 1.125557553 1.868708345
## [179,] -0.1581596 1.0210418 0.06897971 0.230508428 -0.159585304
## [180,] -0.8818807 -1.0045733 -0.76283439 -0.369829400 -0.681940381
## [181,] -0.1581596 0.3458367 -0.76283439 0.929083355 0.201617675
## [182,] 1.2892828 1.0210418 0.06897971 -0.178812818 0.201617675
## [183,] -0.1581596 -0.3293683 -0.76283439 -0.042372403 -0.076230770
## [184,] 1.2892828 2.3714518 1.73260791 1.267455585 3.424659638
## [185,] -0.8818807 -1.0045733 -0.76283439 -0.364371783 -0.587471909
## [186,] -0.1581596 -0.3293683 0.06897971 -0.517185049 -0.565244034
## [187,] -0.1581596 -0.3293683 0.06897971 0.858134339 -0.031775019
## [188,] -0.8818807 -1.0045733 0.06897971 -0.326168467 -0.365193153
## [189,] -0.8818807 -1.0045733 0.06897971 -0.287965151 -0.576357972
## [190,] -0.1581596 -1.0045733 0.06897971 -0.369829400 -0.659712505
## [191,] 1.2892828 0.3458367 0.90079381 0.421525010 -0.409648905
## [192,] -0.1581596 0.3458367 0.06897971 -0.140609502 -0.215154993
## [193,] 0.5655616 0.3458367 0.90079381 -0.309795617 -0.076230770
## [194,] -0.8818807 -1.0045733 0.06897971 -0.364371783 -0.465218594
## [195,] 0.5655616 0.3458367 0.06897971 -0.577218831 -0.476332531
## [196,] -0.8818807 -1.0045733 0.06897971 -0.249761834 -0.309623464
## [197,] 1.2892828 1.0210418 0.90079381 -0.015084320 -0.131800459
## [198,] 1.2892828 -0.3293683 0.06897971 -0.053287636 -0.187370149
## [199,] -0.8818807 -0.3293683 0.06897971 -0.315253234 -0.520788283
## [200,] 0.5655616 0.3458367 0.06897971 -0.151524735 -0.381864060
## [201,] 1.2892828 1.0210418 0.06897971 0.841761489 1.757568967
## [202,] -0.1581596 -0.3293683 0.06897971 -0.375287017 -0.437433749
## [203,] -0.8818807 -0.3293683 0.06897971 -0.468066499 -0.643041599
## [204,] 0.5655616 -0.3293683 0.06897971 0.290542211 -0.548573127
## [205,] 0.5655616 1.0210418 0.06897971 0.230508428 0.201617675
## [206,] 2.0130040 1.6962468 0.90079381 1.152845636 1.868708345
## [207,] 0.5655616 0.3458367 0.90079381 -0.069660486 0.146047986
## [208,] 0.5655616 0.3458367 0.90079381 1.474845017 1.590859900
## [209,] 1.2892828 1.0210418 1.73260791 2.413555075 2.980102126
## [210,] 0.5655616 1.6962468 1.73260791 1.933284813 1.868708345
## [211,] 0.5655616 -0.3293683 -0.76283439 -0.206100901 0.757314565
## [212,] 0.5655616 0.3458367 0.06897971 0.503389259 -0.076230770
## [213,] -0.1581596 -0.3293683 0.06897971 -0.517185049 -0.487446469
## [214,] -0.8818807 -1.0045733 0.06897971 -0.468066499 -0.687497350
## [215,] -0.1581596 -0.3293683 0.90079381 -0.369829400 -0.465218594
## [216,] 1.2892828 1.0210418 0.90079381 1.076439004 1.107403606
## [217,] -0.1581596 -0.3293683 -0.76283439 0.230508428 -0.076230770
## [218,] -0.1581596 0.3458367 1.73260791 0.121356096 0.423896431
## [219,] 4.1841676 5.7474770 1.73260791 9.917777924 12.288025037
## [220,] 0.5655616 1.0210418 0.90079381 -0.287965151 0.201617675
## [221,] -0.1581596 -0.3293683 0.06897971 -0.315253234 -0.354079216
## [222,] -0.8818807 -0.3293683 0.90079381 -0.178812818 -0.076230770
## [223,] 0.5655616 -0.3293683 -0.76283439 -0.358914167 -0.398534967
## [224,] -0.1581596 -0.3293683 -0.76283439 -0.468066499 -0.242939838
## [225,] -0.8818807 -0.3293683 -0.76283439 -0.533557899 -0.643041599
## [226,] -0.1581596 -1.0045733 -0.76283439 -0.468066499 -0.631927661
## [227,] 1.2892828 0.3458367 0.06897971 1.201964186 0.757314565
## [228,] 1.2892828 0.3458367 -0.76283439 0.776270090 0.757314565
## [229,] -0.8818807 -0.3293683 -0.76283439 -0.468066499 -0.576357972
## [230,] 0.5655616 -0.3293683 -0.76283439 1.147388020 0.146047986
## [231,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.643041599
## [232,] 8.5264947 8.4482971 -0.76283439 0.612541591 1.313011455
## [233,] 1.2892828 1.6962468 3.39623611 7.172596765 1.868708345
## [234,] 1.2892828 3.7218619 0.90079381 1.824132480 3.535799016
## [235,] 0.5655616 1.0210418 0.90079381 -0.173355202 0.479466120
## [236,] -0.1581596 -0.3293683 0.06897971 -0.533557899 -0.465218594
## [237,] -0.1581596 1.0210418 0.06897971 0.230508428 0.479466120
## [238,] 0.5655616 -0.3293683 0.90079381 -0.320710850 -0.354079216
## [239,] 0.5655616 -0.3293683 0.90079381 -0.206100901 -0.354079216
## [240,] -0.1581596 -0.3293683 0.90079381 -0.391659866 -0.493003438
## [241,] -0.8818807 -1.0045733 0.06897971 -0.293422767 -0.520788283
## [242,] -0.8818807 -0.3293683 0.06897971 -0.686371164 -0.687497350
## [243,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.709725225
## [244,] 0.5655616 0.3458367 2.56442201 0.017661380 0.034908608
## [245,] -0.1581596 0.3458367 0.06897971 -0.369829400 -0.176256211
## [246,] 0.5655616 1.0210418 4.22805021 3.505078398 1.868708345
## [247,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.715282194
## [248,] 0.5655616 0.3458367 0.06897971 -0.025999553 0.340541897
## [249,] -0.1581596 -0.3293683 0.90079381 -0.468066499 -0.354079216
## [250,] -0.1581596 0.3458367 0.06897971 -0.511727432 -0.509674345
## [251,] -0.1581596 -0.3293683 0.06897971 0.366948843 0.201617675
## [252,] 1.2892828 0.3458367 2.56442201 1.611285432 1.313011455
## [253,] 0.5655616 0.3458367 0.90079381 0.203220345 0.034908608
## [254,] 0.5655616 2.3714518 0.90079381 1.431184084 3.480229327
## [255,] 1.2892828 1.6962468 0.06897971 0.159559412 0.757314565
## [256,] -0.8818807 -1.0045733 -0.76283439 -0.468066499 -0.676383412
## [257,] 1.2892828 0.3458367 2.56442201 2.195250410 2.424405236
## [258,] -0.8818807 -1.0045733 0.06897971 -0.528100282 -0.709725225
## [259,] -0.8818807 -1.0045733 0.90079381 -0.167897585 -0.631927661
## [260,] 1.2892828 1.0210418 0.06897971 -0.162439969 -0.031775019
## [261,] -0.1581596 0.3458367 0.06897971 -0.080575719 -0.287395589
## [262,] -0.8818807 -1.0045733 0.06897971 -0.369829400 -0.631927661
## [263,] -0.1581596 -0.3293683 -0.76283439 -0.402575100 -0.242939838
## [264,] 0.5655616 1.0210418 0.90079381 0.023118996 -0.020661081
## [265,] -0.8818807 -1.0045733 0.06897971 -0.435320799 -0.548573127
## [266,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.709725225
## [267,] 1.2892828 0.3458367 1.73260791 1.867793413 1.313011455
## [268,] -0.1581596 -0.3293683 0.90079381 -0.609964531 -0.520788283
## [269,] -0.1581596 -0.3293683 0.06897971 -0.227931368 -0.076230770
## [270,] 3.4604464 3.7218619 0.06897971 -0.500812199 -0.242939838
## [271,] -0.8818807 -1.0045733 0.06897971 -0.451693649 -0.659712505
## [272,] -0.8818807 -1.0045733 0.06897971 -0.702744014 -0.687497350
## [273,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.698611288
## [274,] -0.1581596 -0.3293683 -0.76283439 0.121356096 -0.020661081
## [275,] -0.8818807 -1.0045733 -0.76283439 0.661660141 -0.520788283
## [276,] -0.8818807 -1.0045733 0.06897971 -0.287965151 -0.604142816
## [277,] -0.8818807 -1.0045733 0.06897971 -0.287965151 -0.409648905
## [278,] -0.8818807 -1.0045733 0.06897971 -0.478981732 -0.631927661
## [279,] -0.8818807 -0.3293683 0.06897971 -0.468066499 -0.576357972
## [280,] 0.5655616 1.0210418 0.90079381 0.230508428 0.757314565
## [281,] 3.4604464 1.0210418 1.73260791 1.103727087 1.479720522
## [282,] -0.8818807 -1.0045733 -0.76283439 -0.206100901 -0.242939838
## [283,] -0.1581596 -0.3293683 0.90079381 1.065523770 1.035163010
## [284,] -0.1581596 -0.3293683 0.06897971 -0.642710231 -0.609699785
## [285,] -0.8818807 -0.3293683 0.06897971 -0.468066499 -0.476332531
## [286,] -0.1581596 0.3458367 0.06897971 -0.468066499 -0.215154993
## [287,] -0.1581596 0.3458367 0.90079381 -0.402575100 -0.298509527
## [288,] -0.1581596 -1.0045733 0.06897971 -0.478981732 -0.631927661
## [289,] -0.1581596 0.3458367 0.06897971 -0.451693649 0.757314565
## [290,] -0.1581596 0.3458367 0.06897971 -0.244304218 -0.354079216
## [291,] -0.1581596 -0.3293683 0.90079381 0.470643559 0.446124306
## [292,] -0.8818807 -1.0045733 0.06897971 -0.424405566 -0.465218594
## [293,] 2.0130040 -0.3293683 1.73260791 0.366948843 0.757314565
## [294,] -0.8818807 -1.0045733 0.06897971 -0.500812199 -0.604142816
## [295,] -0.1581596 -0.3293683 0.90079381 -0.364371783 -0.465218594
## [296,] -0.8818807 -0.3293683 0.06897971 0.230508428 0.201617675
## [297,] 0.5655616 0.3458367 0.06897971 0.230508428 0.034908608
## [298,] -0.1581596 -0.3293683 0.06897971 -0.249761834 -0.404091936
## [299,] 0.5655616 0.3458367 0.06897971 -0.206100901 -0.020661081
## [300,] -0.1581596 -0.3293683 0.90079381 -0.533557899 -0.548573127
## [301,] 0.5655616 -0.3293683 -0.76283439 -0.222473751 -0.104015615
## [302,] -0.1581596 0.3458367 0.06897971 0.672575374 0.384997648
## [303,] -0.1581596 0.3458367 0.90079381 -0.528100282 -0.598585847
## [304,] -0.1581596 0.3458367 0.06897971 -0.533557899 -0.576357972
## [305,] 3.4604464 3.0466569 3.39623611 2.315317976 0.440567337
## [306,] 9.9739371 9.7987072 0.06897971 -0.042372403 3.257950571
## [307,] -0.1581596 -0.3293683 0.06897971 0.072237546 0.040465576
## [308,] -0.1581596 -0.3293683 -0.76283439 -0.337083700 -0.170699242
## [309,] -0.1581596 0.3458367 0.90079381 -0.413490333 -0.270724682
## [310,] 2.0130040 2.3714518 0.90079381 2.167962327 3.257950571
## [311,] 0.5655616 1.0210418 0.90079381 0.317830294 0.796213347
## [312,] 2.7367252 3.0466569 2.56442201 2.577283573 3.535799016
## [313,] 0.5655616 0.3458367 0.06897971 -0.135151885 -0.437433749
## [314,] -0.1581596 0.3458367 0.06897971 -0.260677068 0.146047986
## [315,] 0.5655616 0.3458367 0.06897971 -0.069660486 -0.187370149
## [316,] -0.1581596 -1.0045733 -0.76283439 -0.096948569 -0.465218594
## [317,] -0.8818807 -1.0045733 0.90079381 -0.238846601 -0.659712505
## [318,] -0.1581596 -0.3293683 -0.76283439 0.121356096 -0.209598024
## [319,] 1.2892828 1.0210418 -0.76283439 1.976945746 2.424405236
## [320,] -0.8818807 -1.0045733 0.06897971 -0.206100901 -0.409648905
## [321,] 0.5655616 -1.0045733 0.06897971 0.055864696 -0.187370149
## [322,] -0.1581596 -1.0045733 0.06897971 -0.244304218 0.201617675
## [323,] -0.1581596 -0.3293683 0.06897971 -0.287965151 -0.587471909
## [324,] 0.5655616 1.6962468 0.06897971 -0.064202869 0.479466120
## [325,] 2.0130040 -0.3293683 0.90079381 1.005489988 0.146047986
## [326,] -0.8818807 -1.0045733 0.06897971 -0.424405566 -0.520788283
## [327,] -0.1581596 0.3458367 0.06897971 -0.326168467 -0.076230770
## [328,] 0.5655616 0.3458367 0.90079381 0.148644179 0.590605498
## [329,] -0.8818807 -0.3293683 0.06897971 -0.315253234 -0.465218594
## [330,] -0.1581596 -0.3293683 -0.76283439 0.328745527 0.284972208
## [331,] -0.8818807 -1.0045733 0.06897971 -0.206100901 -0.659712505
## [332,] 0.5655616 1.0210418 1.73260791 1.818674864 1.651986558
## [333,] -0.1581596 0.3458367 0.06897971 -0.560845982 -0.242939838
## [334,] -0.8818807 -0.3293683 0.90079381 -0.402575100 -0.498560407
## [335,] -0.1581596 -0.3293683 0.06897971 -0.522642665 -0.520788283
## [336,] 0.5655616 1.0210418 0.90079381 1.545794033 3.591368705
## [337,] 2.0130040 0.3458367 1.73260791 1.322031751 0.885124850
## [338,] -0.1581596 -0.3293683 0.90079381 -0.156982352 -0.409648905
## [339,] 1.2892828 -0.3293683 0.06897971 0.448813093 0.201617675
## [340,] -0.8818807 -1.0045733 0.06897971 1.431184084 -0.354079216
## [341,] -1.6056019 -1.0045733 -0.76283439 2.004233829 2.007632568
## [342,] -1.6056019 -1.0045733 -0.76283439 0.639829674 0.618390342
## [343,] -1.6056019 -1.0045733 -0.76283439 2.637317356 2.652240961
## [344,] -1.6056019 -1.0045733 -0.76283439 -0.533557899 -0.576357972
## [345,] -1.6056019 -1.0045733 -0.76283439 0.121356096 0.090478297
## [346,] -1.6056019 -1.0045733 -0.76283439 -0.397117483 -0.437433749
## [347,] -1.6056019 -1.0045733 -0.76283439 -0.025999553 -0.059559864
## [348,] -1.6056019 -1.0045733 -0.76283439 -0.173355202 -0.209598024
## [349,] -1.6056019 -1.0045733 -0.76283439 0.066779929 0.034908608
## [350,] -1.6056019 -1.0045733 -0.76283439 2.489961708 2.502202800
## [351,] -1.6056019 -1.0045733 -0.76283439 0.841761489 0.823998192
## [352,] -1.6056019 -1.0045733 -0.76283439 0.694405840 0.673960031
## [353,] -1.6056019 -1.0045733 -0.76283439 -0.380744633 -0.420762842
## [354,] -1.6056019 -1.0045733 -0.76283439 -0.309795617 -0.348522247
## [355,] -1.6056019 -1.0045733 -0.76283439 -0.440778416 -0.481889500
## [356,] -1.6056019 -1.0045733 -0.76283439 -0.200643285 -0.237382869
## [357,] 1.2892828 1.0210418 0.06897971 0.083152779 0.396111586
## [358,] -0.8818807 -0.3293683 0.90079381 -0.402575100 -0.498560407
## [359,] -0.8818807 -1.0045733 0.06897971 -0.364371783 -0.659712505
## [360,] -0.1581596 -0.3293683 0.06897971 -0.637252614 -0.604142816
## [361,] 2.0130040 1.6962468 0.06897971 4.820364003 4.091495906
## [362,] -0.1581596 -1.0045733 -0.76283439 -0.429863183 -0.604142816
## [363,] -0.8818807 -1.0045733 0.06897971 -0.315253234 -0.687497350
## [364,] -0.8818807 -0.3293683 0.06897971 -0.533557899 -0.843092479
## [365,] -0.8818807 -0.3293683 0.90079381 -0.402575100 -0.498560407
## [366,] -0.8818807 -0.3293683 0.90079381 -0.402575100 -0.498560407
## [367,] -0.8818807 -0.3293683 0.90079381 -0.402575100 -0.498560407
## [368,] -0.8818807 -0.3293683 0.90079381 -0.402575100 -0.498560407
## [369,] -0.8818807 -0.3293683 0.90079381 -0.402575100 -0.498560407
## [370,] 0.5655616 1.6962468 0.90079381 0.416067393 0.757314565
## [371,] -0.1581596 -0.3293683 0.06897971 -0.167897585 -0.342965278
## [372,] -0.1581596 0.3458367 0.06897971 -0.620879764 -0.509674345
## [373,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.687497350
## [374,] -0.1581596 -0.3293683 0.06897971 -0.533557899 -0.576357972
## [375,] -0.1581596 -0.3293683 -0.76283439 0.023118996 0.868453943
## [376,] 0.5655616 1.0210418 0.90079381 0.885422422 1.590859900
## [377,] 0.5655616 0.3458367 -0.76283439 0.361491227 -0.159585304
## [378,] -0.8818807 -1.0045733 -0.76283439 -0.468066499 -0.709725225
## [379,] -0.8818807 -0.3293683 0.90079381 -0.206100901 -0.326294371
## [380,] -0.1581596 -0.3293683 -0.76283439 -0.451693649 -0.354079216
## [381,] 1.2892828 1.0210418 0.90079381 1.038235687 -0.076230770
## [382,] 0.5655616 1.0210418 3.39623611 0.776270090 0.912909694
## [383,] -0.1581596 -0.3293683 0.90079381 -0.478981732 -0.526345252
## [384,] 0.5655616 0.3458367 0.90079381 0.121356096 0.479466120
## [385,] -0.1581596 0.3458367 0.06897971 -0.369829400 -0.242939838
## [386,] 0.5655616 0.3458367 0.90079381 -0.369829400 -0.187370149
## [387,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.659712505
## [388,] -0.1581596 0.3458367 0.90079381 -0.326168467 -0.409648905
## [389,] -0.1581596 -0.3293683 0.06897971 -0.309795617 0.423896431
## [390,] 0.5655616 1.0210418 -0.76283439 -0.424405566 0.201617675
## [391,] 0.5655616 1.0210418 1.73260791 2.413555075 1.590859900
## [392,] 0.5655616 0.3458367 0.90079381 0.650744907 0.757314565
## [393,] 0.5655616 1.0210418 0.90079381 1.551251649 1.868708345
## [394,] -0.1581596 -0.3293683 0.90079381 -0.326168467 -0.487446469
## [395,] 2.0130040 1.6962468 1.73260791 7.521884229 2.702253681
## [396,] -0.1581596 -0.3293683 -0.76283439 -0.249761834 -0.131800459
## [397,] -0.1581596 1.0210418 0.90079381 -0.124236652 -0.042888957
## [398,] 8.5264947 8.4482971 4.22805021 0.317830294 1.868708345
## [399,] -0.1581596 0.3458367 0.06897971 -0.615422148 -0.476332531
## [400,] -0.1581596 0.3458367 0.06897971 -0.331626084 -0.476332531
## [401,] 0.5655616 1.0210418 0.06897971 -0.173355202 -0.209598024
## [402,] 0.5655616 1.0210418 0.90079381 0.012203763 0.146047986
## [403,] 1.2892828 1.0210418 0.90079381 0.667117757 1.313011455
## [404,] 0.5655616 1.0210418 0.90079381 2.413555075 0.757314565
## [405,] 0.5655616 0.3458367 0.90079381 -0.402575100 -0.348522247
## [406,] 0.5655616 1.0210418 -0.76283439 0.497931642 0.312757053
## [407,] -0.1581596 -0.3293683 0.06897971 -0.397117483 -0.298509527
## [408,] -0.1581596 0.3458367 0.90079381 -0.451693649 -0.315180433
## [409,] -0.1581596 0.3458367 0.06897971 -0.206100901 0.423896431
## [410,] -0.1581596 0.3458367 0.90079381 -0.424405566 -0.326294371
## [411,] -0.1581596 -0.3293683 0.06897971 -0.315253234 -0.631927661
## [412,] -0.8818807 -0.3293683 0.06897971 -0.555388365 -0.437433749
## [413,] 0.5655616 1.0210418 0.90079381 -0.206100901 0.107149203
## [414,] -0.1581596 -0.3293683 0.06897971 0.432440243 0.057136483
## [415,] 0.5655616 0.3458367 0.90079381 -0.140609502 0.201617675
## [416,] 0.5655616 0.3458367 0.90079381 -0.140609502 0.201617675
## [417,] 1.2892828 1.0210418 4.22805021 0.776270090 1.313011455
## [418,] -0.1581596 0.3458367 0.90079381 0.165017028 0.757314565
## [419,] 1.2892828 1.6962468 -0.76283439 -0.042372403 0.479466120
## [420,] 0.5655616 1.0210418 0.90079381 -0.369829400 0.340541897
## [421,] 0.5655616 0.3458367 0.06897971 0.459728326 1.035163010
## [422,] -0.1581596 -0.3293683 0.06897971 -0.369829400 0.201617675
## [423,] 0.5655616 1.0210418 0.90079381 -0.451693649 0.340541897
## [424,] -0.1581596 -0.3293683 2.56442201 -0.124236652 0.562820653
## [425,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.520788283
## [426,] -0.1581596 0.3458367 0.90079381 0.776270090 1.729784123
## [427,] -0.1581596 -0.3293683 0.06897971 -0.468066499 -0.442990718
## [428,] -0.1581596 -0.3293683 0.06897971 -0.468066499 -0.215154993
## [429,] 0.5655616 0.3458367 0.90079381 -0.337083700 -0.292952558
## [430,] -0.8818807 -0.3293683 0.06897971 -0.533557899 -0.520788283
## [431,] -0.1581596 0.3458367 0.90079381 -0.468066499 -0.459661625
## [432,] 0.5655616 0.3458367 0.90079381 -0.468066499 -0.448547687
## [433,] -0.1581596 0.3458367 0.06897971 -0.533557899 -0.476332531
## [434,] 1.2892828 0.3458367 0.06897971 -0.369829400 0.340541897
## [435,] 0.5655616 0.3458367 0.90079381 3.303146584 3.813647461
## [436,] 0.5655616 1.0210418 0.90079381 2.331690826 1.368581144
## [437,] -0.1581596 -0.3293683 0.06897971 -0.402575100 -0.442990718
## [438,] -0.1581596 0.3458367 0.90079381 -0.533557899 -0.520788283
## [439,] -0.1581596 1.0210418 0.06897971 -0.025999553 -0.020661081
## [440,] -0.1581596 0.3458367 0.90079381 -0.206100901 -0.026218050
## [441,] 0.5655616 1.0210418 0.90079381 -0.042372403 0.407225524
## [442,] -0.1581596 -0.3293683 0.06897971 -0.042372403 -0.242939838
## [443,] -0.1581596 -0.3293683 0.90079381 0.175932262 -0.076230770
## [444,] 2.0130040 1.6962468 2.56442201 1.758641081 1.879822283
## [445,] -0.1581596 1.0210418 0.90079381 -0.206100901 -0.131800459
## [446,] 0.5655616 0.3458367 0.06897971 -0.369829400 -0.204041055
## [447,] 1.2892828 1.0210418 0.06897971 0.437897859 1.035163010
## [448,] 0.5655616 0.3458367 0.06897971 -0.533557899 -0.331851340
## [449,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.520788283
## [450,] 1.2892828 1.6962468 3.39623611 1.878708646 1.268555704
## [451,] 1.2892828 1.0210418 0.90079381 1.878708646 1.224099953
## [452,] -0.1581596 1.0210418 0.90079381 -0.025999553 0.018237701
## [453,] -0.1581596 -0.3293683 -0.76283439 -0.337083700 -0.131800459
## [454,] -0.1581596 0.3458367 -0.76283439 -0.271592301 -0.326294371
## [455,] 0.5655616 1.0210418 0.90079381 -0.025999553 0.618390342
## [456,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.520788283
## [457,] -0.1581596 0.3458367 0.06897971 -0.468066499 -0.354079216
## [458,] 0.5655616 0.3458367 0.06897971 -0.249761834 -0.170699242
## [459,] 0.5655616 0.3458367 4.22805021 7.762019360 2.980102126
## [460,] -0.8818807 -0.3293683 0.06897971 -0.697286397 -0.587471909
## [461,] -0.1581596 1.0210418 0.06897971 -0.206100901 -0.187370149
## [462,] 0.5655616 0.3458367 0.06897971 0.121356096 -0.354079216
## [463,] -0.8818807 -0.3293683 0.90079381 -0.615422148 -0.659712505
## [464,] 2.0130040 1.0210418 0.90079381 1.185591336 1.868708345
## [465,] -0.1581596 0.3458367 0.06897971 -0.528100282 -0.270724682
## [466,] 0.5655616 1.6962468 0.90079381 1.987860979 6.703271290
## [467,] 0.5655616 1.6962468 0.90079381 0.154101795 0.757314565
## [468,] -0.8818807 -0.3293683 -0.76283439 -0.724574480 -0.698611288
## [469,] -0.1581596 -0.3293683 0.90079381 -0.402575100 -0.470775563
## [470,] -0.1581596 0.3458367 0.06897971 -0.364371783 -0.076230770
## [471,] -0.1581596 -0.3293683 0.90079381 -0.468066499 -0.409648905
## [472,] -0.1581596 -0.3293683 0.06897971 -0.533557899 -0.604142816
## [473,] -0.1581596 -0.3293683 0.06897971 -0.446236033 -0.465218594
## [474,] -0.1581596 -0.3293683 0.06897971 -0.506269815 -0.465218594
## [475,] 1.2892828 0.3458367 -0.76283439 -0.369829400 0.173832830
## [476,] 1.2892828 1.6962468 -0.76283439 0.476101176 0.535035809
## [477,] 1.2892828 1.0210418 -0.76283439 -0.206100901 0.157161923
## [478,] 0.5655616 0.3458367 -0.76283439 0.230508428 -0.076230770
## [479,] 1.2892828 1.0210418 -0.76283439 -0.075118103 0.246073426
## [480,] -0.8818807 -1.0045733 -0.76283439 -0.369829400 -0.465218594
## [481,] 2.0130040 1.6962468 -0.76283439 0.716236307 0.757314565
## [482,] 1.2892828 1.6962468 -0.76283439 0.159559412 0.618390342
## [483,] 1.2892828 1.0210418 -0.76283439 0.230508428 0.479466120
## [484,] -0.1581596 0.3458367 0.90079381 -0.435320799 -0.270724682
## [485,] 0.5655616 0.3458367 0.90079381 -0.468066499 -0.215154993
## [486,] -0.1581596 0.3458367 0.06897971 -0.468066499 -0.354079216
## [487,] -0.1581596 -0.3293683 0.06897971 -0.369829400 -0.615256754
## [488,] -0.1581596 0.3458367 0.90079381 -0.506269815 -0.354079216
## [489,] -0.8818807 -1.0045733 0.06897971 -0.506269815 -0.643041599
## [490,] -0.8818807 -1.0045733 0.06897971 -0.506269815 -0.659712505
## [491,] 0.5655616 0.3458367 0.90079381 -0.473524116 -0.215154993
## [492,] -0.1581596 -0.3293683 0.06897971 -0.517185049 -0.493003438
## [493,] 1.2892828 0.3458367 0.90079381 -0.402575100 -0.109572584
## [494,] -0.8818807 -0.3293683 0.06897971 -0.435320799 -0.454104656
## [495,] 0.5655616 0.3458367 0.06897971 0.230508428 1.007378165
## [496,] -0.8818807 -0.3293683 1.73260791 0.448813093 0.423896431
## [497,] -0.1581596 -0.3293683 -0.76283439 -0.506269815 -0.354079216
## [498,] 0.5655616 1.0210418 -0.76283439 -0.397117483 0.018237701
## [499,] 0.5655616 0.3458367 -0.76283439 -0.555388365 -0.309623464
## [500,] 0.5655616 0.3458367 -0.76283439 -0.451693649 -0.076230770
## [501,] 0.5655616 0.3458367 0.90079381 0.339660760 1.035163010
## [502,] -0.1581596 1.0210418 -0.76283439 -0.451693649 -0.076230770
## [503,] 0.5655616 1.0210418 -0.76283439 -0.233388985 1.090732699
## [504,] 1.2892828 0.3458367 0.90079381 0.197762728 0.735086689
## [505,] -0.1581596 -0.3293683 0.06897971 -0.124236652 -0.298509527
## [506,] -0.1581596 -0.3293683 1.73260791 0.525219725 0.868453943
## [507,] -0.1581596 -0.3293683 0.90079381 0.121356096 -0.076230770
## [508,] 0.5655616 0.3458367 0.90079381 0.214135578 0.073807390
## [509,] 0.5655616 0.3458367 0.90079381 -0.249761834 -0.215154993
## [510,] -0.1581596 0.3458367 0.90079381 -0.429863183 -0.420762842
## [511,] -0.1581596 0.3458367 0.90079381 -0.522642665 -0.554130096
## [512,] -0.8818807 0.3458367 0.06897971 -0.588134065 -0.404091936
## [513,] -0.1581596 -0.3293683 0.06897971 -0.588134065 -0.554130096
## [514,] -0.1581596 0.3458367 7.55530661 4.269144725 4.091495906
## [515,] 0.5655616 0.3458367 0.90079381 0.383321693 0.840669098
## [516,] 0.5655616 1.0210418 0.90079381 -0.162439969 0.157161923
## [517,] 1.2892828 1.6962468 0.90079381 0.099525629 0.968479383
## [518,] 0.5655616 1.0210418 0.90079381 0.121356096 0.435010369
## [519,] 0.5655616 0.3458367 0.06897971 -0.468066499 -0.215154993
## [520,] 2.0130040 1.6962468 0.06897971 0.541592575 1.035163010
## [521,] -0.1581596 0.3458367 0.90079381 -0.315253234 -0.076230770
## [522,] 0.5655616 1.0210418 0.90079381 0.230508428 0.340541897
## [523,] -0.8818807 -1.0045733 0.06897971 -0.468066499 -0.631927661
## [524,] 0.5655616 1.0210418 0.06897971 0.241423661 0.646175187
## [525,] -0.1581596 0.3458367 -0.76283439 -0.588134065 -0.220711962
## [526,] 0.5655616 0.3458367 -0.76283439 -0.506269815 -0.187370149
## [527,] -0.1581596 -0.3293683 -0.76283439 -0.560845982 -0.287395589
## [528,] -0.1581596 0.3458367 -0.76283439 -0.642710231 -0.409648905
## [529,] 1.2892828 0.3458367 0.06897971 1.212879419 1.035163010
## [530,] 1.2892828 1.0210418 -0.76283439 0.317830294 1.313011455
## [531,] 1.2892828 1.0210418 0.90079381 2.620944506 1.868708345
## [532,] -0.8818807 -0.3293683 0.06897971 -0.549930748 -0.437433749
## [533,] -0.1581596 0.3458367 0.06897971 -0.451693649 -0.315180433
## [534,] -0.1581596 -0.3293683 0.06897971 -0.369829400 -0.248496806
## [535,] 0.5655616 0.3458367 -0.76283439 0.230508428 0.368326742
## [536,] 0.5655616 1.0210418 0.90079381 1.185591336 1.868708345
## [537,] 2.0130040 1.6962468 0.90079381 -0.162439969 0.157161923
## [538,] 0.5655616 1.0210418 0.06897971 0.339660760 0.373883711
## [539,] -0.1581596 0.3458367 0.06897971 -0.615422148 -0.476332531
## [540,] -0.8818807 -0.3293683 0.06897971 -0.500812199 -0.565244034
## [541,] 4.1841676 1.0210418 6.72349251 1.698607298 0.762871534
## [542,] 0.5655616 1.0210418 0.90079381 1.185591336 0.857340005
## [543,] 1.2892828 1.0210418 0.90079381 0.230508428 0.229402519
## [544,] -0.1581596 -0.3293683 0.06897971 -0.162439969 -0.298509527
## [545,] -0.1581596 0.3458367 0.90079381 0.012203763 -0.131800459
## [546,] 0.5655616 0.3458367 0.90079381 -0.162439969 0.646175187
## [547,] 1.2892828 2.3714518 2.56442201 2.238911343 4.925041242
## [548,] 1.2892828 1.6962468 2.56442201 0.978201905 3.396874793
## [549,] 0.5655616 0.3458367 0.90079381 0.907252888 0.590605498
## [550,] 0.5655616 1.0210418 0.90079381 0.776270090 1.868708345
## [551,] 0.5655616 1.0210418 0.90079381 1.207421802 1.035163010
## [552,] 1.2892828 0.3458367 0.90079381 0.612541591 0.384997648
## [553,] 0.5655616 1.0210418 0.90079381 -0.206100901 0.190503737
## [554,] 0.5655616 0.3458367 0.90079381 0.317830294 0.334984928
## [555,] 0.5655616 1.0210418 0.90079381 -0.025999553 0.924023632
## [556,] -0.1581596 -0.3293683 -0.76283439 -0.369829400 -0.176256211
## [557,] 1.2892828 3.0466569 2.56442201 0.939998588 1.868708345
## [558,] -0.8818807 -0.3293683 -0.76283439 -0.746404947 -0.681940381
## [559,] 0.5655616 0.3458367 -0.76283439 -0.369829400 -0.204041055
## [560,] -0.8818807 -0.3293683 0.06897971 -0.686371164 -0.604142816
## [561,] 0.5655616 1.0210418 0.90079381 -0.446236033 1.174087233
## [562,] -0.1581596 0.3458367 0.06897971 -0.659083081 -0.404091936
## [563,] -0.1581596 0.3458367 0.06897971 -0.631794998 -0.481889500
## [564,] 0.5655616 1.0210418 0.90079381 -0.408032716 -0.292952558
## [565,] 0.5655616 1.0210418 0.90079381 0.912710505 0.957365445
## [566,] 0.5655616 0.3458367 0.90079381 0.557965425 0.612833373
## [567,] -0.1581596 0.3458367 0.06897971 -0.031457170 0.023794670
## [568,] -0.1581596 0.3458367 0.90079381 0.448813093 0.090478297
## [569,] -0.8818807 -0.3293683 0.06897971 -0.642710231 -0.626370692
## [570,] 0.5655616 0.3458367 0.90079381 0.121356096 0.312757053
## [571,] -0.1581596 0.3458367 0.06897971 -0.582676448 -0.498560407
## [572,] -0.1581596 1.0210418 0.06897971 -0.555388365 0.001566794
## [573,] -0.1581596 0.3458367 0.06897971 -0.582676448 -0.520788283
## [574,] -0.1581596 0.3458367 0.06897971 -0.615422148 -0.476332531
## [575,] -0.8818807 -0.3293683 -0.76283439 -0.740947330 -0.670826443
## [576,] -0.8818807 -0.3293683 0.06897971 -0.686371164 -0.570801003
## [577,] 1.2892828 1.0210418 7.55530661 8.067645891 5.202889687
## [578,] 0.5655616 0.3458367 0.90079381 -0.036914786 0.740643658
## [579,] -0.1581596 -0.3293683 0.90079381 -0.511727432 -0.448547687
## [580,] 0.5655616 0.3458367 0.90079381 0.448813093 0.757314565
## [581,] -0.8818807 -0.3293683 1.73260791 0.230508428 -0.409648905
## [582,] -0.1581596 0.3458367 0.90079381 -0.031457170 0.062693452
## [583,] -0.1581596 0.3458367 0.90079381 -0.031457170 0.201617675
## [584,] -0.1581596 -1.0045733 0.06897971 -0.206100901 0.090478297
## [585,] 0.5655616 0.3458367 0.90079381 -0.522642665 -0.426319811
## [586,] 0.5655616 1.0210418 1.73260791 0.034034230 1.335239331
## [587,] -0.1581596 0.3458367 0.90079381 -0.495354582 -0.476332531
## [588,] 1.2892828 1.0210418 0.90079381 0.869049572 0.418339462
## [589,] 0.5655616 1.6962468 -0.76283439 0.372406460 0.079364359
## [590,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.687497350
## [591,] -0.1581596 -0.3293683 -0.76283439 -0.435320799 -0.504117376
## [592,] -0.8818807 -0.3293683 -0.76283439 -0.287965151 -0.604142816
## [593,] -0.1581596 0.3458367 -0.76283439 -0.178812818 -0.287395589
## [594,] -0.1581596 -0.3293683 -0.76283439 -0.206100901 -0.242939838
## [595,] -0.1581596 -1.0045733 -0.76283439 -0.200643285 -0.442990718
## [596,] -0.1581596 0.3458367 0.90079381 -0.528100282 -0.270724682
## [597,] -0.1581596 -0.3293683 -0.76283439 -0.315253234 -0.459661625
## [598,] -0.1581596 -1.0045733 -0.76283439 -0.293422767 -0.342965278
## [599,] -0.1581596 -0.3293683 -0.76283439 -0.195185668 0.090478297
## [600,] -0.8818807 -1.0045733 -0.76283439 -0.468066499 -0.509674345
## [601,] -0.8818807 -1.0045733 -0.76283439 -0.566303598 -0.659712505
## [602,] -0.8818807 -1.0045733 -0.76283439 -0.304338001 -0.442990718
## [603,] -1.6056019 -1.0045733 -0.76283439 -0.369829400 -0.709725225
## [604,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.726396132
## [605,] -0.1581596 -0.3293683 0.06897971 -0.369829400 -0.159585304
## [606,] -0.8818807 -0.3293683 -0.76283439 -0.320710850 -0.476332531
## [607,] -0.1581596 -0.3293683 -0.76283439 -0.468066499 -0.570801003
## [608,] 0.5655616 0.3458367 -0.76283439 -0.255219451 0.090478297
## [609,] -0.1581596 -0.3293683 -0.76283439 -0.533557899 -0.493003438
## [610,] -0.1581596 0.3458367 0.90079381 -0.533557899 -0.431876780
## [611,] -0.1581596 0.3458367 -0.76283439 -0.424405566 -0.309623464
## [612,] -0.1581596 0.3458367 -0.76283439 0.121356096 -0.242939838
## [613,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.659712505
## [614,] -0.8818807 -1.0045733 0.06897971 -0.533557899 -0.576357972
## [615,] -0.1581596 -0.3293683 0.90079381 -0.369829400 -0.242939838
## [616,] -0.8818807 -1.0045733 -0.76283439 -0.435320799 -0.709725225
## [617,] -0.1581596 -0.3293683 -0.76283439 -0.468066499 -0.570801003
## [618,] -0.1581596 0.3458367 -0.76283439 -0.468066499 -0.242939838
## [619,] -0.1581596 -0.3293683 -0.76283439 -0.287965151 -0.354079216
## [620,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.676383412
## [621,] -0.1581596 -0.3293683 -0.76283439 -0.249761834 -0.242939838
## [622,] -0.1581596 -0.3293683 0.06897971 -0.315253234 -0.631927661
## [623,] -0.1581596 -0.3293683 -0.76283439 -0.533557899 -0.520788283
## [624,] 0.5655616 0.3458367 0.90079381 3.837993012 1.868708345
## [625,] -0.8818807 -1.0045733 -0.76283439 -0.167897585 -0.748624008
## [626,] 0.5655616 -0.3293683 -0.76283439 0.219593195 -0.131800459
## [627,] -0.1581596 -0.3293683 0.90079381 -0.331626084 -0.520788283
## [628,] -0.1581596 -0.3293683 -0.76283439 -0.402575100 -0.354079216
## [629,] -0.8818807 -1.0045733 -0.76283439 -0.446236033 -0.604142816
## [630,] 1.2892828 0.3458367 -0.76283439 -0.358914167 -0.576357972
## [631,] -0.8818807 -0.3293683 -0.76283439 -0.167897585 -0.520788283
## [632,] -0.8818807 -1.0045733 -0.76283439 -0.468066499 -0.709725225
## [633,] -0.8818807 -0.3293683 -0.76283439 -0.206100901 -0.409648905
## [634,] -0.8818807 -1.0045733 -0.76283439 -0.402575100 -0.576357972
## [635,] 0.5655616 1.0210418 0.90079381 1.594912582 2.424405236
## [636,] -0.8818807 -1.0045733 -0.76283439 -0.484439349 -0.659712505
## [637,] -0.8818807 -0.3293683 0.06897971 -0.697286397 -0.587471909
## [638,] -0.1581596 -0.3293683 -0.76283439 -0.533557899 -0.409648905
## [639,] -0.1581596 -0.3293683 0.06897971 -0.075118103 -0.242939838
## [640,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.659712505
## [641,] -0.8818807 -0.3293683 0.06897971 -0.659083081 -0.554130096
## [642,] -0.8818807 -1.0045733 -0.76283439 -0.451693649 -0.687497350
## [643,] -0.8818807 -1.0045733 -0.76283439 -0.451693649 -0.709725225
## [644,] -0.1581596 0.3458367 0.06897971 -0.615422148 -0.476332531
## [645,] -0.1581596 -0.3293683 -0.76283439 -0.533557899 -0.576357972
## [646,] -0.8818807 -1.0045733 -0.76283439 -0.435320799 -0.709725225
## [647,] 0.5655616 0.3458367 0.90079381 -0.277049918 0.340541897
## [648,] -0.8818807 -1.0045733 -0.76283439 -0.511727432 -0.659712505
## [649,] -0.1581596 -0.3293683 -0.76283439 -0.457151266 -0.659712505
## [650,] 2.0130040 2.3714518 1.73260791 1.960572896 3.313520260
## [651,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.687497350
## [652,] -0.8818807 -1.0045733 -0.76283439 -0.468066499 -0.659712505
## [653,] 0.5655616 0.3458367 0.90079381 0.121356096 0.479466120
## [654,] 1.2892828 0.3458367 0.06897971 0.285084594 0.868453943
## [655,] -0.1581596 0.3458367 0.90079381 0.503389259 0.840669098
## [656,] -0.8818807 -1.0045733 -0.76283439 -0.484439349 -0.659712505
## [657,] -0.8818807 -1.0045733 -0.76283439 -0.287965151 -0.326294371
## [658,] -0.1581596 0.3458367 2.56442201 1.867793413 3.307963291
## [659,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.687497350
## [660,] 0.5655616 1.0210418 0.90079381 0.285084594 1.090732699
## [661,] 0.5655616 1.0210418 0.90079381 1.758641081 0.757314565
## [662,] -0.8818807 -1.0045733 -0.76283439 -0.315253234 -0.626370692
## [663,] -1.6056019 -1.0045733 -0.76283439 -0.369829400 -0.709725225
## [664,] -0.1581596 -0.3293683 -0.76283439 -0.506269815 -0.354079216
## [665,] 2.7367252 1.6962468 0.90079381 1.682234448 1.868708345
## [666,] -0.1581596 -0.3293683 -0.76283439 -0.435320799 -0.354079216
## [667,] -0.1581596 0.3458367 -0.76283439 -0.315253234 -0.354079216
## [668,] 0.5655616 1.0210418 0.90079381 0.072237546 0.701744876
## [669,] -0.8818807 -1.0045733 -0.76283439 -0.435320799 -0.604142816
## [670,] -0.8818807 -1.0045733 -0.76283439 -0.320710850 -0.676383412
## [671,] -0.8818807 -1.0045733 -0.76283439 -0.468066499 -0.709725225
## [672,] -0.1581596 -1.0045733 -0.76283439 -0.402575100 -0.442990718
## [673,] -0.8818807 -1.0045733 -0.76283439 -0.440778416 -0.659712505
## [674,] 0.5655616 0.3458367 -0.76283439 -0.489896966 0.146047986
## [675,] -0.8818807 -1.0045733 -0.76283439 -0.451693649 -0.659712505
## [676,] -0.8818807 -0.3293683 -0.76283439 -0.337083700 -0.576357972
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## [738,] 0.5655616 0.3458367 -0.76283439 1.987860979 4.647192797
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## [881,] -0.1581596 -0.3293683 -0.76283439 -0.271592301 -0.520788283
## [882,] 0.5655616 0.3458367 -0.76283439 -0.167897585 -0.076230770
## [883,] -0.1581596 -0.3293683 -0.76283439 -0.424405566 -0.242939838
## [884,] -0.8818807 -1.0045733 -0.76283439 -0.304338001 -0.442990718
## [885,] -0.8818807 -1.0045733 -0.76283439 -0.468066499 -0.709725225
## [886,] -0.1581596 0.3458367 -0.76283439 -0.424405566 -0.309623464
## [887,] -0.8818807 -1.0045733 -0.76283439 -0.337083700 -0.631927661
## [888,] -0.1581596 -1.0045733 -0.76283439 -0.402575100 -0.509674345
## [889,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.576357972
## [890,] -0.8818807 -1.0045733 -0.76283439 -0.320710850 -0.548573127
## [891,] 0.5655616 -0.3293683 -0.76283439 -0.075118103 -0.354079216
## [892,] -0.8818807 -0.3293683 -0.76283439 -0.533557899 -0.437433749
## [893,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.687497350
## [894,] -0.8818807 -1.0045733 -0.76283439 -0.369829400 -0.493003438
## [895,] -1.6056019 -1.0045733 -0.76283439 -0.369829400 -0.709725225
## [896,] -0.8818807 -0.3293683 -0.76283439 -0.337083700 -0.576357972
## [897,] 1.2892828 1.0210418 0.90079381 -0.206100901 0.107149203
## [898,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.726396132
## [899,] 0.5655616 -0.3293683 -0.76283439 -0.217016135 -0.354079216
## [900,] 0.5655616 -0.3293683 -0.76283439 -0.533557899 -0.520788283
## [901,] -0.1581596 0.3458367 -0.76283439 -0.069660486 -0.298509527
## [902,] -0.1581596 -0.3293683 0.90079381 -0.402575100 -0.148471366
## [903,] 0.5655616 -0.3293683 -0.76283439 -0.326168467 -0.170699242
## [904,] -0.1581596 -0.3293683 -0.76283439 -0.528100282 -0.326294371
## [905,] -0.1581596 0.3458367 -0.76283439 -0.249761834 -0.409648905
## [906,] -0.1581596 -0.3293683 -0.76283439 -0.249761834 -0.409648905
## [907,] -0.8818807 -1.0045733 -0.76283439 -0.549930748 -0.593028878
## [908,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.687497350
## [909,] -0.1581596 -0.3293683 -0.76283439 -0.337083700 -0.409648905
## [910,] -0.8818807 -1.0045733 -0.76283439 -0.468066499 -0.576357972
## [911,] -0.8818807 -1.0045733 -0.76283439 -0.451693649 -0.709725225
## [912,] -0.8818807 -1.0045733 -0.76283439 -0.402575100 -0.576357972
## [913,] -0.1581596 -0.3293683 -0.76283439 -0.468066499 -0.570801003
## [914,] -0.1581596 -0.3293683 -0.76283439 -0.255219451 -0.654155536
## [915,] 0.5655616 0.3458367 -0.76283439 -0.255219451 0.090478297
## [916,] -0.1581596 0.3458367 -0.76283439 -0.315253234 -0.354079216
## [917,] -0.1581596 -0.3293683 -0.76283439 -0.287965151 -0.354079216
## [918,] -0.1581596 -0.3293683 -0.76283439 -0.468066499 -0.187370149
## [919,] -0.1581596 -0.3293683 -0.76283439 -0.025999553 -0.109572584
## [920,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.659712505
## [921,] -0.8818807 -1.0045733 -0.76283439 -0.468066499 -0.576357972
## [922,] 0.5655616 0.3458367 -0.76283439 0.623456824 -0.048445926
## [923,] -0.8818807 -1.0045733 -0.76283439 -0.320710850 -0.676383412
## [924,] 0.5655616 -0.3293683 -0.76283439 -0.369829400 -0.020661081
## [925,] -0.8818807 -1.0045733 -0.76283439 -0.435320799 -0.604142816
## [926,] 0.5655616 0.3458367 -0.76283439 -0.167897585 -0.076230770
## [927,] -0.8818807 -1.0045733 -0.76283439 -0.468066499 -0.709725225
## [928,] -0.1581596 0.3458367 -0.76283439 -0.424405566 -0.309623464
## [929,] -0.8818807 -1.0045733 -0.76283439 -0.364371783 -0.587471909
## [930,] -0.1581596 -1.0045733 -0.76283439 -0.402575100 -0.509674345
## [931,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.659712505
## [932,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.687497350
## [933,] -1.6056019 -1.0045733 -0.76283439 -0.369829400 -0.709725225
## [934,] -0.8818807 -0.3293683 -0.76283439 -0.337083700 -0.576357972
## [935,] -0.1581596 -1.0045733 -0.76283439 -0.468066499 -0.709725225
## [936,] 0.5655616 0.3458367 0.06897971 -0.227931368 0.479466120
## [937,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.726396132
## [938,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.659712505
## [939,] -0.1581596 -0.3293683 -0.76283439 -0.206100901 -0.415205874
## [940,] -0.8818807 -1.0045733 -0.76283439 -0.484439349 -0.659712505
## [941,] 0.5655616 1.0210418 4.22805021 4.924058719 1.868708345
## [942,] -0.1581596 0.3458367 -0.76283439 -0.304338001 -0.131800459
## [943,] -0.1581596 -0.3293683 -0.76283439 -0.418947950 -0.242939838
## [944,] -0.8818807 -1.0045733 -0.76283439 -0.549930748 -0.593028878
## [945,] -0.8818807 -1.0045733 -0.76283439 -0.451693649 -0.709725225
## [946,] -0.8818807 -0.3293683 -0.76283439 -0.320710850 -0.476332531
## [947,] -0.8818807 -1.0045733 -0.76283439 -0.287965151 -0.643041599
## [948,] -0.1581596 -1.0045733 -0.76283439 -0.369829400 -0.631927661
## [949,] -0.1581596 0.3458367 -0.76283439 0.121356096 -0.242939838
## [950,] -0.1581596 -0.3293683 -0.76283439 -0.255219451 -0.654155536
## [951,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.659712505
## [952,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.631927661
## [953,] -0.8818807 -1.0045733 -0.76283439 -0.435320799 -0.604142816
## [954,] -0.1581596 -1.0045733 -0.76283439 -0.468066499 -0.576357972
## [955,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.659712505
## [956,] -0.8818807 -1.0045733 -0.76283439 -0.435320799 -0.709725225
## [957,] -0.1581596 -0.3293683 -0.76283439 -0.206100901 -0.415205874
## [958,] -0.8818807 -1.0045733 -0.76283439 -0.484439349 -0.659712505
## [959,] 0.5655616 -0.3293683 -0.76283439 -0.326168467 -0.170699242
## [960,] -0.1581596 0.3458367 0.90079381 -0.326168467 -0.254053775
## [961,] -0.8818807 -0.3293683 -0.76283439 0.006746147 -0.520788283
## [962,] 1.2892828 1.0210418 -0.76283439 -0.326168467 -0.076230770
## [963,] -0.1581596 -0.3293683 -0.76283439 -0.326168467 -0.242939838
## [964,] -0.8818807 -0.3293683 -0.76283439 -0.320710850 -0.476332531
## [965,] -0.1581596 0.3458367 -0.76283439 -0.533557899 -0.576357972
## [966,] -0.1581596 -0.3293683 -0.76283439 -0.533557899 -0.442990718
## [967,] -0.1581596 -0.3293683 -0.76283439 -0.440778416 -0.465218594
## [968,] -0.8818807 -0.3293683 -0.76283439 -0.402575100 -0.581914941
## [969,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.709725225
## [970,] -0.8818807 -0.3293683 -0.76283439 -0.124236652 -0.465218594
## [971,] -0.8818807 -1.0045733 -0.76283439 0.034034230 -0.676383412
## [972,] -0.1581596 0.3458367 -0.76283439 -0.424405566 -0.498560407
## [973,] 1.2892828 1.6962468 -0.76283439 0.121356096 -0.076230770
## [974,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.709725225
## [975,] -0.1581596 0.3458367 -0.76283439 -0.571761215 -0.420762842
## [976,] -0.1581596 -0.3293683 -0.76283439 -0.124236652 -0.020661081
## [977,] -0.8818807 -1.0045733 -0.76283439 -0.539015515 -0.709725225
## [978,] -0.1581596 -0.3293683 -0.76283439 -0.511727432 -0.487446469
## [979,] -0.1581596 -0.3293683 -0.76283439 -0.533557899 -0.426319811
## [980,] -0.1581596 0.3458367 -0.76283439 -0.178812818 -0.187370149
## [981,] 0.5655616 -0.3293683 -0.76283439 0.006746147 -0.187370149
## [982,] -0.8818807 -1.0045733 -0.76283439 -0.402575100 -0.609699785
## [983,] -0.1581596 -0.3293683 -0.76283439 -0.206100901 -0.242939838
## [984,] -0.1581596 -0.3293683 -0.76283439 -0.364371783 -0.409648905
## [985,] -0.8818807 -1.0045733 -0.76283439 -0.435320799 -0.704168256
## [986,] 0.5655616 0.3458367 -0.76283439 -0.042372403 -0.131800459
## [987,] 0.5655616 -0.3293683 -0.76283439 -0.369829400 -0.076230770
## [988,] -0.1581596 -0.3293683 -0.76283439 -0.146067119 -0.154028335
## [989,] 0.5655616 0.3458367 -0.76283439 -0.206100901 -0.409648905
## [990,] -0.8818807 0.3458367 1.73260791 -0.173355202 0.201617675
## [991,] -0.1581596 -0.3293683 -0.76283439 -0.424405566 -0.354079216
## [992,] -0.1581596 -0.3293683 -0.76283439 -0.533557899 -0.576357972
## [993,] -0.8818807 -0.3293683 -0.76283439 -0.402575100 -0.581914941
## [994,] -0.8818807 -1.0045733 -0.76283439 -0.293422767 -0.509674345
## [995,] 0.5655616 0.3458367 0.90079381 -0.206100901 -0.215154993
## [996,] -0.8818807 -0.3293683 -0.76283439 -0.533557899 -0.465218594
## [997,] -0.8818807 -1.0045733 -0.76283439 -0.468066499 -0.676383412
## [998,] -0.1581596 -0.3293683 -0.76283439 0.170474645 -0.242939838
## [999,] 0.5655616 1.0210418 0.90079381 3.445044616 4.647192797
## [1000,] -0.1581596 -0.3293683 -0.76283439 -0.096948569 -0.020661081
## [1001,] 0.5655616 0.3458367 -0.76283439 -0.326168467 -0.465218594
## [1002,] -0.8818807 -1.0045733 -0.76283439 -0.342541317 -0.493003438
## [1003,] 0.5655616 -0.3293683 -0.76283439 -0.206100901 -0.187370149
## [1004,] 1.2892828 1.0210418 0.06897971 0.197762728 0.023794670
## [1005,] -0.8818807 -1.0045733 -0.76283439 -0.533557899 -0.631927661
## [1006,] -0.1581596 0.3458367 -0.76283439 -0.298880384 -0.187370149
## [1007,] -0.8818807 -1.0045733 -0.76283439 -0.468066499 -0.659712505
## [1008,] -0.8818807 -1.0045733 -0.76283439 -0.468066499 -0.609699785
## [1009,] -0.8818807 -1.0045733 -0.76283439 -0.337083700 -0.576357972
## [1010,] -0.8818807 -0.3293683 -0.76283439 -0.599049298 -0.587471909
## [1011,] -0.1581596 0.3458367 -0.76283439 -0.571761215 -0.420762842
## [1012,] -0.8818807 -1.0045733 -0.76283439 -0.369829400 -0.409648905
## [1013,] 0.5655616 -0.3293683 -0.76283439 -0.440778416 -0.159585304
## [1014,] -0.8818807 -0.3293683 -0.76283439 -0.642710231 -0.643041599
## [1015,] -0.1581596 -0.3293683 -0.76283439 -0.369829400 -0.298509527
## [1016,] 0.5655616 -0.3293683 -0.76283439 0.006746147 -0.187370149
## [1017,] -0.8818807 -1.0045733 -0.76283439 0.503389259 -0.554130096
## [1018,] -0.8818807 -1.0045733 -0.76283439 -0.347998934 -0.554130096
## [1019,] -0.1581596 0.3458367 -0.76283439 -0.408032716 -0.054002895
## [1020,] 1.2892828 1.0210418 -0.76283439 -0.391659866 -0.031775019
## [1021,] -0.1581596 -0.3293683 -0.76283439 -0.364371783 -0.409648905
## [1022,] -0.1581596 -0.3293683 -0.76283439 -0.533557899 -0.520788283
## [1023,] -0.1581596 -0.3293683 -0.76283439 -0.533557899 -0.242939838
## [1024,] -0.1581596 -0.3293683 -0.76283439 -0.364371783 -0.604142816
## [1025,] -0.1581596 0.3458367 -0.76283439 -0.506269815 -0.354079216
## lokasi_2 lokasi_3 lokasi_4 lokasi_5 lokasi_6 lokasi_7
## [1,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [2,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [3,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [4,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [5,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [6,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [7,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
## [8,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [9,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [10,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [11,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [12,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [13,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [14,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [15,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [16,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [17,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [18,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [19,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [20,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [21,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [22,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
## [23,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
## [24,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
## [25,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
## [26,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [27,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [28,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [29,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [30,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [31,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [32,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [33,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
## [34,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [35,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [36,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [37,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [38,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [39,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [40,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [41,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
## [42,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [43,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [44,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [45,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [46,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [47,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
## [48,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [49,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [50,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [51,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
## [52,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [53,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [54,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [55,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [176,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
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## [198,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
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## [221,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
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## [237,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
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## [255,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
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## [259,] -0.6642294 -0.6037308 -0.0992099 9.5964517 -0.2828536 -0.1644011
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## [261,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [262,] -0.6642294 -0.6037308 -0.0992099 9.5964517 -0.2828536 -0.1644011
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## [265,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
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## [268,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
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## [270,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [271,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
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## [273,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
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## [288,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
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## [296,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [297,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
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## [301,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [302,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
## [303,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [304,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [305,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [307,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
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## [309,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
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## [313,] -0.6642294 -0.6037308 -0.0992099 9.5964517 -0.2828536 -0.1644011
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## [329,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
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## [334,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [335,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
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## [358,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [359,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
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## [361,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
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## [363,] -0.6642294 -0.6037308 -0.0992099 9.5964517 -0.2828536 -0.1644011
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## [371,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
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## [374,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
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## [403,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [404,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
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## [411,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
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## [443,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [447,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
## [448,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [449,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [452,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [454,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [457,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [458,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [459,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [460,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [461,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [462,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
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## [464,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [470,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [494,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [510,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [511,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [512,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [513,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [514,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [515,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [518,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [520,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [522,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [523,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [540,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [549,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [553,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [554,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
## [555,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
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## [601,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [602,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [603,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [604,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [605,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
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## [615,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [616,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [617,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [618,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [619,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [620,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [621,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [622,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
## [623,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [624,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [625,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [626,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [627,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [628,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [629,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [630,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [631,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [632,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [633,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [634,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [635,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [636,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [637,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [638,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [639,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
## [640,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [641,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [642,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [643,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [644,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [645,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [646,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [647,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [648,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [649,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [650,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [651,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [652,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [653,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [654,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
## [655,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [656,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [657,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [658,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 6.0767509
## [659,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [660,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
## [661,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
## [662,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [663,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [664,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [665,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [666,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [667,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [668,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [669,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [670,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [671,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [672,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [673,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [674,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [675,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [676,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [677,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [678,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [679,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [680,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [681,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [682,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [683,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [684,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [685,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [686,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [687,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [688,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [689,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [690,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [691,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [692,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [693,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [694,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [695,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [696,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [697,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [698,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [699,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [700,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [701,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [702,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [703,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [704,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [705,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [706,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [707,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [708,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [709,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [710,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [711,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [712,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [713,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [714,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [715,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [716,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [717,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [718,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [719,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [720,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [722,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [724,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [725,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [726,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [727,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [728,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [729,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [730,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [731,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [734,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [735,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [739,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [750,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [759,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [760,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [762,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [763,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [764,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [768,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [775,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [783,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [799,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [800,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
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## [803,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [804,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [805,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [806,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [807,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [808,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [809,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [810,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [811,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
## [812,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [813,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [814,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [815,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [816,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [817,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [818,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [819,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [820,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [821,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [822,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [823,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [824,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [825,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [826,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [827,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [828,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [829,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [830,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [831,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [832,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [833,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [834,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [835,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [836,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [837,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [838,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [839,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [840,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [841,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [842,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [843,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [844,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [845,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [846,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [847,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [848,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [849,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [850,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [851,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [852,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [853,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [854,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [855,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [856,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [857,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [858,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [859,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [860,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [861,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [862,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [863,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [864,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [865,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [866,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [867,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [868,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [869,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [870,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [871,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [872,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [873,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [874,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [875,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [876,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [877,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [878,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [879,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [880,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [881,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [882,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [883,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [884,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [885,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [886,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [887,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [888,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [889,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [890,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [891,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [892,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [893,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [894,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [895,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [896,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [897,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [898,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [899,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [900,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [901,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [902,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [903,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [904,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [905,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [906,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [907,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [908,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [909,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [910,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [911,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [912,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [913,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [914,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [915,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [916,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [917,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [918,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [919,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [920,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [921,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [922,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [923,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [924,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [925,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [926,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [927,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [928,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [929,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [930,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [931,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [932,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [933,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [934,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [935,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [936,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [937,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [938,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [939,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [940,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [941,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [942,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [943,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [944,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [945,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [946,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [947,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [948,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [949,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [950,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [951,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [952,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [953,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [954,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [955,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [956,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [957,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [958,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [959,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [960,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [961,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [962,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [963,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [964,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [965,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [966,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [967,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [968,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [969,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [970,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [971,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [972,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [973,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [974,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [975,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [976,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [977,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [978,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [979,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [980,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [981,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [982,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [983,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [984,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [985,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [986,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [987,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [988,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [989,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [990,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
## [991,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [992,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [993,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [994,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [995,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
## [996,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [997,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [998,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [999,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 3.5319484 -0.1644011
## [1000,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1001,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1002,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1003,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1004,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1005,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1006,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1007,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1008,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1009,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1010,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1011,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1012,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1013,] -0.6642294 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1014,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1015,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1016,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1017,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1018,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1019,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1020,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1021,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1022,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1023,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1024,] 1.5040353 -0.6037308 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## [1025,] -0.6642294 1.6547513 -0.0992099 -0.1041035 -0.2828536 -0.1644011
## lokasi_8 lokasi_9 lokasi_10 lokasi_11 log_harga
## [1,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.142478730
## [2,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.751753174
## [3,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.278690101
## [4,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.252966346
## [5,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.030907844
## [6,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.440170569
## [7,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.115708748
## [8,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.398177783
## [9,] -0.06256121 -0.03123475 -0.03123475 11.26947096 0.932807735
## [10,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.793342285
## [11,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.781732487
## [12,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.398177783
## [13,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.169093266
## [14,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.456304862
## [15,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.087255215
## [16,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.335164857
## [17,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.092280248
## [18,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.921909579
## [19,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.430656130
## [20,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.830142571
## [21,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.171702999
## [22,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.995706692
## [23,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.363415982
## [24,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.818016909
## [25,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.995706692
## [26,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.083042306
## [27,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.370352635
## [28,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.880673273
## [29,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.204972002
## [30,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.521263089
## [31,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.750534168
## [32,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.932807735
## [33,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.627574309
## [34,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.316099215
## [35,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.995706692
## [36,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.552639576
## [37,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.278690101
## [38,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.208597628
## [39,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.205135094
## [40,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.600542190
## [41,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.359953449
## [42,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.205135094
## [43,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.315584730
## [44,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.331304354
## [45,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.755117951
## [46,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.493555087
## [47,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.205135094
## [48,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.537643232
## [49,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.226246293
## [50,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.726135492
## [51,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.291190680
## [52,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.277360396
## [53,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.039117253
## [54,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.240578839
## [55,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.349392782
## [56,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.277360396
## [57,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.277360396
## [58,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.349392782
## [59,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.952898575
## [60,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.152176278
## [61,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.985501410
## [62,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.545812265
## [63,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.833122594
## [64,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.865567587
## [65,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.533059449
## [66,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.240578839
## [67,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.630532112
## [68,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.818016909
## [69,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.117804106
## [70,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.277360396
## [71,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.199083189
## [72,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.349392782
## [73,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.775208792
## [74,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.681344397
## [75,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.212962127
## [76,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.349392782
## [77,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.336277898
## [78,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.335164857
## [79,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.775208792
## [80,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.093443144
## [81,] -0.06256121 -0.03123475 -0.03123475 11.26947096 1.928504525
## [82,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.398177783
## [83,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.115708748
## [84,] -0.06256121 -0.03123475 -0.03123475 11.26947096 0.932807735
## [85,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.908924398
## [86,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.405645984
## [87,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.593362944
## [88,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.098147992
## [89,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.485508770
## [90,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.359953449
## [91,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.434118663
## [92,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.687877804
## [93,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.315584730
## [94,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.003510416
## [95,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.331304354
## [96,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -2.204294418
## [97,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.557734073
## [98,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -2.204294418
## [99,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.557734073
## [100,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.440170569
## [101,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.074995989
## [102,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.376772972
## [103,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.277360396
## [104,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.291190680
## [105,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.985501410
## [106,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.659626678
## [107,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.659626678
## [108,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.440170569
## [109,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.691449679
## [110,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.196801268
## [111,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.359953449
## [112,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.715333016
## [113,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.803918686
## [114,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.106194309
## [115,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.569600049
## [116,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.015802158
## [117,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.117804106
## [118,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.347281689
## [119,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.292470708
## [120,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.258587846
## [121,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.166544041
## [122,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.842082220
## [123,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.814733453
## [124,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.015911501
## [125,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.656755997
## [126,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.117804106
## [127,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -2.484668095
## [128,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.865567587
## [129,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.034365321
## [130,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.127749045
## [131,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.278690101
## [132,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.440170569
## [133,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.537643232
## [134,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.015802158
## [135,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.768026691
## [136,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.292470708
## [137,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.017842962
## [138,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.604042704
## [139,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.396848077
## [140,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.768026691
## [141,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.252966346
## [142,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.394923573
## [143,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.711029806
## [144,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.396848077
## [145,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.604042704
## [146,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.396848077
## [147,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.768026691
## [148,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.711029806
## [149,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.045006066
## [150,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.585193911
## [151,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.765352344
## [152,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.117804106
## [153,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.569600049
## [154,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.521263089
## [155,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.731234615
## [156,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.066497756
## [157,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.315584730
## [158,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.440170569
## [159,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.642267038
## [160,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.552639576
## [161,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.074995989
## [162,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.066497756
## [163,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.107756640
## [164,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.674400925
## [165,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.212121472
## [166,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 3.037431540
## [167,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.378521668
## [168,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -2.381984202
## [169,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.349392782
## [170,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.326561047
## [171,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.310587011
## [172,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.600542190
## [173,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.569600049
## [174,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.706470418
## [175,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.659626678
## [176,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.619481754
## [177,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.278690101
## [178,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.552639576
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## [834,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.768026691
## [835,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.775208792
## [836,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.336277898
## [837,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.098147992
## [838,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.039117253
## [839,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.584462577
## [840,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.537643232
## [841,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.738427235
## [842,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.450746970
## [843,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.212121472
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## [845,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.023414694
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## [847,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.834924815
## [848,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.458053558
## [849,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.593362944
## [850,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.137894946
## [851,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.533059449
## [852,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.292470708
## [853,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.431350639
## [854,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.645187961
## [855,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.413946684
## [856,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.614322796
## [857,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.775208792
## [858,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.485508770
## [859,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.842082220
## [860,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.775208792
## [861,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.431350639
## [862,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.880673273
## [863,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.190029408
## [864,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.128380508
## [865,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.853218061
## [866,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.023414694
## [867,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.212121472
## [868,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.014798459
## [869,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.105938130
## [870,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.339260281
## [871,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.908924398
## [872,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.291190680
## [873,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.504251571
## [874,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.584462577
## [875,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.023414694
## [876,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.842082220
## [877,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.339260281
## [878,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.853218061
## [879,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.093443144
## [880,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.968008887
## [881,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.467119629
## [882,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.166544041
## [883,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.164236971
## [884,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.485508770
## [885,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.450746970
## [886,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.191310812
## [887,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.726468857
## [888,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.714655432
## [889,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.208597628
## [890,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.968008887
## [891,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.039117253
## [892,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.014798459
## [893,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.047287987
## [894,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.646632791
## [895,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.646632791
## [896,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.624974220
## [897,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.808221896
## [898,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.485508770
## [899,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.166544041
## [900,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.339260281
## [901,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.004512592
## [902,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.615652501
## [903,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.039117253
## [904,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.431350639
## [905,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.039117253
## [906,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.336277898
## [907,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.133591737
## [908,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.115708748
## [909,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.646632791
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## [911,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.853218061
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## [915,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.137894946
## [916,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.952898575
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## [918,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.137894946
## [919,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.775208792
## [920,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.378521668
## [921,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.208597628
## [922,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 1.128380508
## [923,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.923363115
## [924,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.212121472
## [925,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.923363115
## [926,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.166544041
## [927,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.450746970
## [928,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.191310812
## [929,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.542854462
## [930,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.714655432
## [931,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.115708748
## [932,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.080898062
## [933,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.646632791
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## [935,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.923363115
## [936,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.865567587
## [937,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.485508770
## [938,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.333183466
## [939,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.068923723
## [940,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.311281520
## [941,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.748692185
## [942,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.300114634
## [943,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.212121472
## [944,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.133591737
## [945,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.853218061
## [946,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.177679882
## [947,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.074995989
## [948,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.853218061
## [949,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.023414694
## [950,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.431350639
## [951,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.378521668
## [952,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.584462577
## [953,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.923363115
## [954,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.691449679
## [955,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.014798459
## [956,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.098147992
## [957,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.068923723
## [958,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.311281520
## [959,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.039117253
## [960,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.537643232
## [961,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.680050771
## [962,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.383067471
## [963,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.023414694
## [964,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.177679882
## [965,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.714655432
## [966,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.431350639
## [967,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.908924398
## [968,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.593362944
## [969,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.208597628
## [970,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.646632791
## [971,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.646632791
## [972,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.284886268
## [973,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.534389154
## [974,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.952898575
## [975,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.371749809
## [976,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.593362944
## [977,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.426072346
## [978,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.039117253
## [979,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.533059449
## [980,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.405645984
## [981,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.212121472
## [982,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.839775150
## [983,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.340263980
## [984,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.255125312
## [985,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.228287097
## [986,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.217484620
## [987,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.339260281
## [988,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.102693794
## [989,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.646632791
## [990,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.687877804
## [991,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.603786525
## [992,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.542854462
## [993,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.593362944
## [994,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.787784393
## [995,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.487653014
## [996,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.292470708
## [997,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.646632791
## [998,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.092488512
## [999,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 2.604042704
## [1000,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.177679882
## [1001,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.775208792
## [1002,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.714655432
## [1003,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.575867566
## [1004,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.954228280
## [1005,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.333183466
## [1006,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.034365321
## [1007,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.968008887
## [1008,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.968008887
## [1009,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.449071047
## [1010,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.240578839
## [1011,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.371749809
## [1012,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -1.030907844
## [1013,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.092488512
## [1014,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.533059449
## [1015,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.593362944
## [1016,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.212121472
## [1017,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.212121472
## [1018,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.714655432
## [1019,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.177679882
## [1020,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 0.034365321
## [1021,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.255125312
## [1022,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.646632791
## [1023,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.775208792
## [1024,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.431350639
## [1025,] -0.06256121 -0.03123475 -0.03123475 -0.08864874 -0.164236971
## attr(,"scaled:center")
## jml_km_buah jml_kt_buah jml_gar_buah lt_m2 lb_m2 lokasi_2
## 3.218537e+00 2.487805e+00 9.170732e-01 1.577639e+02 1.637180e+02 3.063415e-01
## lokasi_3 lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_8
## 2.673171e-01 9.756098e-03 1.073171e-02 7.414634e-02 2.634146e-02 3.902439e-03
## lokasi_9 lokasi_10 lokasi_11 log_harga
## 9.756098e-04 9.756098e-04 7.804878e-03 2.127022e+01
## attr(,"scaled:scale")
## jml_km_buah jml_kt_buah jml_gar_buah lt_m2 lb_m2 lokasi_2
## 1.38174758 1.48103162 1.20219169 183.23016622 179.95421921 0.46119832
## lokasi_3 lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_8
## 0.44277525 0.09833794 0.10308688 0.26213680 0.16022683 0.06237793
## lokasi_9 lokasi_10 lokasi_11 log_harga
## 0.03123475 0.03123475 0.08804274 1.02606663Lasso dan Shrinkage
A. Lasso
# --- Buat X dan y ---
X = model.matrix(log_harga ~ . - harga_rp, data = rumah.log)[, -1] # prediktor
y = rumah.log$log_harga # respon
# Cek konsistensi ukuran
cat("nrow(X):", nrow(X), "\n")## nrow(X): 1025cat("length(y):", length(y), "\n")## length(y): 1025# LASSO dengan 10-fold CV
set.seed(123)
lasso_reg = cv.glmnet(
X, y,
alpha = 1,# LASSO
standardize = TRUE,
nfolds = 10
)
# --- Tampilkan lambda hasil CV ---
cat("Lambda min :", lasso_reg$lambda.min, "\n")## Lambda min : 0.003900763cat("Lambda 1se :", lasso_reg$lambda.1se, "\n")## Lambda 1se : 0.06977098# Koefisien model pada lambda.min
coef(lasso_reg, s = "lambda.min")## 16 x 1 sparse Matrix of class "dgCMatrix"
## lambda.min
## (Intercept) 20.397539101
## jml_km_buah 0.035646058
## jml_kt_buah 0.139888492
## jml_gar_buah 0.172007914
## lt_m2 0.001032860
## lb_m2 0.001913687
## lokasi_2 -0.138435014
## lokasi_3 -0.453788322
## lokasi_4 -0.556529643
## lokasi_5 -0.797982632
## lokasi_6 -0.479630923
## lokasi_7 -0.100891281
## lokasi_8 -0.116692463
## lokasi_9 -2.017960454
## lokasi_10 -1.030505415
## lokasi_11 -0.602461731# Koefisien model pada lambda.1se
coef(lasso_reg, s = "lambda.1se")## 16 x 1 sparse Matrix of class "dgCMatrix"
## lambda.1se
## (Intercept) 20.3565903091
## jml_km_buah 0.0068907049
## jml_kt_buah 0.1494723976
## jml_gar_buah 0.1470696759
## lt_m2 0.0007175711
## lb_m2 0.0019942181
## lokasi_2 .
## lokasi_3 -0.1914504737
## lokasi_4 .
## lokasi_5 .
## lokasi_6 -0.0512242908
## lokasi_7 .
## lokasi_8 .
## lokasi_9 .
## lokasi_10 .
## lokasi_11 .# Plot kurva CV
plot(lasso_reg)
B. Ridge
library(glmnet)
# Gunakan X dan y yang sama
X = model.matrix(log_harga ~ . - harga_rp, data = rumah.log)[, -1]
y = rumah.log$log_harga
# Ridge regression dengan 10-fold CV
set.seed(123)
ridge_reg = cv.glmnet(
X, y,
alpha = 0, # alpha=0 → Ridge
standardize = TRUE,
nfolds = 10
)
# Tampilkan lambda hasil CV
cat("Lambda min :", ridge_reg$lambda.min, "\n")## Lambda min : 0.07837542cat("Lambda 1se :", ridge_reg$lambda.1se, "\n")## Lambda 1se : 0.6659978# Koefisien model pada lambda.min
coef(ridge_reg, s = "lambda.min")## 16 x 1 sparse Matrix of class "dgCMatrix"
## lambda.min
## (Intercept) 20.370142934
## jml_km_buah 0.054350391
## jml_kt_buah 0.129860932
## jml_gar_buah 0.167938965
## lt_m2 0.001098662
## lb_m2 0.001740819
## lokasi_2 -0.127566913
## lokasi_3 -0.428559698
## lokasi_4 -0.550121803
## lokasi_5 -0.774379777
## lokasi_6 -0.434727219
## lokasi_7 -0.100041675
## lokasi_8 -0.151462665
## lokasi_9 -2.002897456
## lokasi_10 -1.061432297
## lokasi_11 -0.576290511# Koefisien model pada lambda.1se
coef(ridge_reg, s = "lambda.1se")## 16 x 1 sparse Matrix of class "dgCMatrix"
## lambda.1se
## (Intercept) 20.3579294251
## jml_km_buah 0.0802934653
## jml_kt_buah 0.1081363692
## jml_gar_buah 0.1398308302
## lt_m2 0.0009888103
## lb_m2 0.0012656785
## lokasi_2 -0.0420083942
## lokasi_3 -0.2598017377
## lokasi_4 -0.2959998524
## lokasi_5 -0.4647771342
## lokasi_6 -0.1821943737
## lokasi_7 0.0156891763
## lokasi_8 -0.0270357435
## lokasi_9 -1.1841288260
## lokasi_10 -0.6612333366
## lokasi_11 -0.1808075896# Plot kurva CV
plot(ridge_reg)
library(glmnet)
# LASSO dengan standardisasi
fit_std = glmnet(X, y, alpha = 1, standardize = TRUE)
# LASSO tanpa standardisasi
fit_raw = glmnet(X, y, alpha = 1, standardize = FALSE)
# Plot dengan standardisasi
plot(fit_std, xvar = "lambda", label = TRUE, main = "LASSO - Standarisasi (TRUE)")
# Plot tanpa standardisasi
plot(fit_raw, xvar = "lambda", label = TRUE, main = "LASSO - Standarisasi (FALSE)")
library(glmnet)
# Ridge dengan standardisasi
fit_ridge_std = glmnet(X, y, alpha = 0, standardize = TRUE)
# Ridge tanpa standardisasi
fit_ridge_raw = glmnet(X, y, alpha = 0, standardize = FALSE)
# Plot dengan standardisasi
plot(fit_ridge_std, xvar = "lambda", label = TRUE, main = "Ridge - Standarisasi (TRUE)")
# Plot tanpa standardisasi
plot(fit_ridge_raw, xvar = "lambda", label = TRUE, main = "Ridge - Standarisasi (FALSE)")
summary.best## Subset selection object
## Call: regsubsets.formula(harga_rp ~ ., data = rumah.clean, nvmax = ncol(rumah.clean) -
## 1)
## 15 Variables (and intercept)
## Forced in Forced out
## jml_km_buah FALSE FALSE
## jml_kt_buah FALSE FALSE
## jml_gar_buah FALSE FALSE
## lt_m2 FALSE FALSE
## lb_m2 FALSE FALSE
## lokasi_2 FALSE FALSE
## lokasi_3 FALSE FALSE
## lokasi_4 FALSE FALSE
## lokasi_5 FALSE FALSE
## lokasi_6 FALSE FALSE
## lokasi_7 FALSE FALSE
## lokasi_8 FALSE FALSE
## lokasi_9 FALSE FALSE
## lokasi_10 FALSE FALSE
## lokasi_11 FALSE FALSE
## 1 subsets of each size up to 15
## Selection Algorithm: exhaustive
## jml_km_buah jml_kt_buah jml_gar_buah lt_m2 lb_m2 lokasi_2 lokasi_3
## 1 ( 1 ) " " " " " " " " "*" " " " "
## 2 ( 1 ) " " " " " " "*" "*" " " " "
## 3 ( 1 ) " " " " " " "*" "*" " " " "
## 4 ( 1 ) " " " " "*" "*" "*" " " " "
## 5 ( 1 ) "*" " " "*" "*" "*" " " " "
## 6 ( 1 ) "*" " " "*" "*" "*" " " " "
## 7 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 8 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 9 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 10 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 11 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 12 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 13 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 14 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_8 lokasi_9 lokasi_10
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " "*" " " " " " " " "
## 7 ( 1 ) " " " " "*" " " " " " " " "
## 8 ( 1 ) " " " " "*" " " " " "*" " "
## 9 ( 1 ) " " " " "*" "*" " " "*" " "
## 10 ( 1 ) " " " " "*" "*" " " "*" " "
## 11 ( 1 ) "*" " " "*" "*" " " "*" " "
## 12 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 13 ( 1 ) "*" "*" "*" "*" "*" "*" " "
## 14 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## lokasi_11
## 1 ( 1 ) " "
## 2 ( 1 ) " "
## 3 ( 1 ) "*"
## 4 ( 1 ) "*"
## 5 ( 1 ) "*"
## 6 ( 1 ) "*"
## 7 ( 1 ) "*"
## 8 ( 1 ) "*"
## 9 ( 1 ) "*"
## 10 ( 1 ) "*"
## 11 ( 1 ) "*"
## 12 ( 1 ) "*"
## 13 ( 1 ) "*"
## 14 ( 1 ) "*"
## 15 ( 1 ) "*"summary.bw## Subset selection object
## Call: regsubsets.formula(harga_rp ~ ., data = rumah.clean, nbest = 1,
## nvmax = NULL, method = "backward")
## 15 Variables (and intercept)
## Forced in Forced out
## jml_km_buah FALSE FALSE
## jml_kt_buah FALSE FALSE
## jml_gar_buah FALSE FALSE
## lt_m2 FALSE FALSE
## lb_m2 FALSE FALSE
## lokasi_2 FALSE FALSE
## lokasi_3 FALSE FALSE
## lokasi_4 FALSE FALSE
## lokasi_5 FALSE FALSE
## lokasi_6 FALSE FALSE
## lokasi_7 FALSE FALSE
## lokasi_8 FALSE FALSE
## lokasi_9 FALSE FALSE
## lokasi_10 FALSE FALSE
## lokasi_11 FALSE FALSE
## 1 subsets of each size up to 15
## Selection Algorithm: backward
## jml_km_buah jml_kt_buah jml_gar_buah lt_m2 lb_m2 lokasi_2 lokasi_3
## 1 ( 1 ) " " " " " " " " "*" " " " "
## 2 ( 1 ) " " " " " " "*" "*" " " " "
## 3 ( 1 ) " " " " " " "*" "*" " " " "
## 4 ( 1 ) " " " " "*" "*" "*" " " " "
## 5 ( 1 ) "*" " " "*" "*" "*" " " " "
## 6 ( 1 ) "*" " " "*" "*" "*" " " " "
## 7 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 8 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 9 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 10 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 11 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 12 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 13 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 14 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_8 lokasi_9 lokasi_10
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " "*" " " " " " " " "
## 7 ( 1 ) " " " " "*" " " " " " " " "
## 8 ( 1 ) " " " " "*" " " " " "*" " "
## 9 ( 1 ) " " " " "*" "*" " " "*" " "
## 10 ( 1 ) " " " " "*" "*" " " "*" " "
## 11 ( 1 ) "*" " " "*" "*" " " "*" " "
## 12 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 13 ( 1 ) "*" "*" "*" "*" "*" "*" " "
## 14 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## lokasi_11
## 1 ( 1 ) " "
## 2 ( 1 ) " "
## 3 ( 1 ) "*"
## 4 ( 1 ) "*"
## 5 ( 1 ) "*"
## 6 ( 1 ) "*"
## 7 ( 1 ) "*"
## 8 ( 1 ) "*"
## 9 ( 1 ) "*"
## 10 ( 1 ) "*"
## 11 ( 1 ) "*"
## 12 ( 1 ) "*"
## 13 ( 1 ) "*"
## 14 ( 1 ) "*"
## 15 ( 1 ) "*"summary.fw## Subset selection object
## Call: regsubsets.formula(harga_rp ~ ., data = rumah.clean, nvmax = 15,
## method = "forward")
## 15 Variables (and intercept)
## Forced in Forced out
## jml_km_buah FALSE FALSE
## jml_kt_buah FALSE FALSE
## jml_gar_buah FALSE FALSE
## lt_m2 FALSE FALSE
## lb_m2 FALSE FALSE
## lokasi_2 FALSE FALSE
## lokasi_3 FALSE FALSE
## lokasi_4 FALSE FALSE
## lokasi_5 FALSE FALSE
## lokasi_6 FALSE FALSE
## lokasi_7 FALSE FALSE
## lokasi_8 FALSE FALSE
## lokasi_9 FALSE FALSE
## lokasi_10 FALSE FALSE
## lokasi_11 FALSE FALSE
## 1 subsets of each size up to 15
## Selection Algorithm: forward
## jml_km_buah jml_kt_buah jml_gar_buah lt_m2 lb_m2 lokasi_2 lokasi_3
## 1 ( 1 ) " " " " " " " " "*" " " " "
## 2 ( 1 ) " " " " " " "*" "*" " " " "
## 3 ( 1 ) " " " " " " "*" "*" " " " "
## 4 ( 1 ) " " " " "*" "*" "*" " " " "
## 5 ( 1 ) "*" " " "*" "*" "*" " " " "
## 6 ( 1 ) "*" " " "*" "*" "*" " " " "
## 7 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 8 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 9 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 10 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 11 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 12 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 13 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 14 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_8 lokasi_9 lokasi_10
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " "*" " " " " " " " "
## 7 ( 1 ) " " " " "*" " " " " " " " "
## 8 ( 1 ) " " " " "*" " " " " "*" " "
## 9 ( 1 ) " " " " "*" "*" " " "*" " "
## 10 ( 1 ) " " " " "*" "*" " " "*" " "
## 11 ( 1 ) "*" " " "*" "*" " " "*" " "
## 12 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 13 ( 1 ) "*" "*" "*" "*" "*" "*" " "
## 14 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## lokasi_11
## 1 ( 1 ) " "
## 2 ( 1 ) " "
## 3 ( 1 ) "*"
## 4 ( 1 ) "*"
## 5 ( 1 ) "*"
## 6 ( 1 ) "*"
## 7 ( 1 ) "*"
## 8 ( 1 ) "*"
## 9 ( 1 ) "*"
## 10 ( 1 ) "*"
## 11 ( 1 ) "*"
## 12 ( 1 ) "*"
## 13 ( 1 ) "*"
## 14 ( 1 ) "*"
## 15 ( 1 ) "*"summary.hy## Subset selection object
## Call: regsubsets.formula(harga_rp ~ ., data = rumah.clean, nbest = 1,
## nvmax = NULL, method = "seqrep")
## 15 Variables (and intercept)
## Forced in Forced out
## jml_km_buah FALSE FALSE
## jml_kt_buah FALSE FALSE
## jml_gar_buah FALSE FALSE
## lt_m2 FALSE FALSE
## lb_m2 FALSE FALSE
## lokasi_2 FALSE FALSE
## lokasi_3 FALSE FALSE
## lokasi_4 FALSE FALSE
## lokasi_5 FALSE FALSE
## lokasi_6 FALSE FALSE
## lokasi_7 FALSE FALSE
## lokasi_8 FALSE FALSE
## lokasi_9 FALSE FALSE
## lokasi_10 FALSE FALSE
## lokasi_11 FALSE FALSE
## 1 subsets of each size up to 15
## Selection Algorithm: 'sequential replacement'
## jml_km_buah jml_kt_buah jml_gar_buah lt_m2 lb_m2 lokasi_2 lokasi_3
## 1 ( 1 ) " " " " " " " " "*" " " " "
## 2 ( 1 ) " " " " " " "*" "*" " " " "
## 3 ( 1 ) " " " " " " "*" "*" " " " "
## 4 ( 1 ) " " " " "*" "*" "*" " " " "
## 5 ( 1 ) "*" "*" "*" "*" "*" " " " "
## 6 ( 1 ) "*" " " "*" "*" "*" " " " "
## 7 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 8 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 9 ( 1 ) "*" " " "*" "*" "*" " " "*"
## 10 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 11 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 12 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 13 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 14 ( 1 ) "*" " " "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## lokasi_4 lokasi_5 lokasi_6 lokasi_7 lokasi_8 lokasi_9 lokasi_10
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " "*" " " " " " " " "
## 7 ( 1 ) " " " " "*" " " " " " " " "
## 8 ( 1 ) " " " " "*" " " " " "*" " "
## 9 ( 1 ) " " " " "*" "*" " " "*" " "
## 10 ( 1 ) " " " " "*" "*" " " "*" " "
## 11 ( 1 ) "*" " " "*" "*" " " "*" " "
## 12 ( 1 ) "*" "*" "*" "*" " " "*" " "
## 13 ( 1 ) "*" "*" "*" "*" "*" "*" " "
## 14 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## 15 ( 1 ) "*" "*" "*" "*" "*" "*" "*"
## lokasi_11
## 1 ( 1 ) " "
## 2 ( 1 ) " "
## 3 ( 1 ) "*"
## 4 ( 1 ) "*"
## 5 ( 1 ) " "
## 6 ( 1 ) "*"
## 7 ( 1 ) "*"
## 8 ( 1 ) "*"
## 9 ( 1 ) "*"
## 10 ( 1 ) "*"
## 11 ( 1 ) "*"
## 12 ( 1 ) "*"
## 13 ( 1 ) "*"
## 14 ( 1 ) "*"
## 15 ( 1 ) "*"# Data
X = model.matrix(log_harga ~ . - harga_rp, data = rumah.log)[, -1]
y = rumah.log$log_harga
dat = data.frame(y, X)
# sigma^2 untuk Cp
ols_full = lm(y ~ ., data = dat)
sigma2 = sum(resid(ols_full)^2) / ols_full$df.residual
n = nrow(dat)
# Fungsi metrik
get_metrics = function(fit, s, name) {
vars = rownames(coef(fit, s))[which(coef(fit, s) != 0)[-1]]
if (length(vars) == 0) {
return(data.frame(Model=name, Adj_R2=NA, AIC=NA, Cp=NA, Num_Vars=0, Vars=""))
}
lm_fit = lm(reformulate(vars, "y"), data = dat)
RSS = sum(resid(lm_fit)^2); p = length(coef(lm_fit))
data.frame(Model=name,
Adj_R2=summary(lm_fit)$adj.r.squared,
AIC=AIC(lm_fit),
Cp=RSS/sigma2 - (n-2*p),
Num_Vars=p-1,
Vars=paste(vars, collapse=", "))
}
# LASSO & RIDGE
set.seed(123)
lasso = cv.glmnet(X, y, alpha=1)
ridge = cv.glmnet(X, y, alpha=0)
# Hasil akhir
results = rbind(
get_metrics(lasso, "lambda.min", "Lasso_min"),
get_metrics(lasso, "lambda.1se", "Lasso_1se"),
get_metrics(ridge, "lambda.min", "Ridge_min"),
get_metrics(ridge, "lambda.1se", "Ridge_1se")
)
print(results, row.names = FALSE)## Model Adj_R2 AIC Cp Num_Vars
## Lasso_min 0.7292676 1640.160 16.00000 15
## Lasso_1se 0.7119898 1695.666 72.90382 7
## Ridge_min 0.7292676 1640.160 16.00000 15
## Ridge_1se 0.7292676 1640.160 16.00000 15
## Vars
## jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_8, lokasi_9, lokasi_10, lokasi_11
## jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_3, lokasi_6
## jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_8, lokasi_9, lokasi_10, lokasi_11
## jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_8, lokasi_9, lokasi_10, lokasi_11# Model Terbaik Regresi Linear Berganda
# Fungsi untuk menghitung Adjusted R², AIC, dan Mallows' Cp
get_lm_metrics = function(model, full_model) {
n = length(model$fitted.values)
k = length(coef(model)) - 1
# Residual Sum of Squares
rss = sum(resid(model)^2)
# Total Sum of Squares
tss = sum((model$model[[1]] - mean(model$model[[1]]))^2)
# R² dan Adj R²
r2 = 1 - rss/tss
adj_r2 = 1 - (1-r2)*(n-1)/(n-k-1)
# AIC
aic = AIC(model)
# Mallows' Cp
sigma2_full = sum(resid(full_model)^2) / (n - length(coef(full_model)))
cp = rss/sigma2_full - (n - 2*(k+1))
return(c(Adj_R2 = adj_r2, AIC = aic, Cp = cp))
}
reg_metrics = get_lm_metrics(reg, full_model = reg)
reg_metrics## Adj_R2 AIC Cp
## 5.574054e-01 4.986161e+04 1.600000e+01# Jalankan best subset selection
library(leaps)
best = regsubsets(log_harga ~ . - harga_rp, data = rumah.log, nvmax = 15)
summary.best = summary(best)
# Ambil metrik
adjr2 = summary.best$adjr2
cp = summary.best$cp
rss = summary.best$rss
n = nrow(rumah.log)
p = 1:(ncol(rumah.log)-1)
aic = n*log(rss/n) + 2*(p+1)## Warning in n * log(rss/n) + 2 * (p + 1): longer object length is not a multiple
## of shorter object length# Skor gabungan
norm = function(x) (x - min(x)) / (max(x) - min(x))
adjr2_n = norm(adjr2)
aic_n = 1 - norm(aic)
cp_n = 1 - norm(abs(cp - (p+1)))## Warning in cp - (p + 1): longer object length is not a multiple of shorter
## object lengthscore = (adjr2_n + aic_n + cp_n) / 3## Warning in adjr2_n + aic_n: longer object length is not a multiple of shorter
## object lengthbest_general = which.max(score)
# Variabel di model terbaik
which_mat = summary.best$which
vars_in_best = colnames(which_mat)[which_mat[best_general, ] & colnames(which_mat) != "(Intercept)"]
best_subset_table = data.frame(
Model = paste0("Best Subset (M", best_general, ")"),
Num_Variables = length(vars_in_best),
Adj_R2 = adjr2[best_general],
AIC = aic[best_general],
Cp = cp[best_general],
Vars = paste(vars_in_best, collapse = ", ")
)
best_subset_table## Model Num_Variables Adj_R2 AIC Cp
## 1 Best Subset (M14) 14 0.7293833 -1272.087 14.56843
## Vars
## 1 jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_9, lokasi_10, lokasi_11# Model Terbaik Regresi Linear Berganda (model penuh)
n = nrow(rumah2_dummy)
k = length(coef(reg)) - 1
rss = sum(resid(reg)^2)
tss = sum((reg$model[[1]] - mean(reg$model[[1]]))^2)
# Adjusted R²
r2 = 1 - rss/tss
adjr2 = 1 - (1 - r2) * (n - 1) / (n - k - 1)
# AIC
aic = AIC(reg)
# Mallows' Cp (pakai model penuh)
sigma2_full = rss / (n - (k + 1))
cp = rss / sigma2_full - (n - 2 * (k + 1))
# Ambil nama variabel dari model
vars = names(coef(reg))[-1]
# Satukan dalam data frame
reg_metrics = data.frame(
Model = "Regresi Linear Penuh",
Num_Variables = k,
Adj_R2 = adjr2,
Cp = cp,
AIC = aic,
Vars = paste(vars, collapse = ", ")
)
reg_metrics## Model Num_Variables Adj_R2 Cp AIC
## 1 Regresi Linear Penuh 15 0.5574054 16 49861.61
## Vars
## 1 jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_8, lokasi_9, lokasi_10, lokasi_11Perbandingan Model Terbaik
# Model Regresi Linear (tambah nama variabel juga)
vars_reg = names(coef(reg))[-1] # semua variabel kecuali intercept
reg_table = data.frame(
Model = "Regresi Linear",
Adj_R2 = reg_metrics$Adj_R2,
AIC = reg_metrics$AIC,
Cp = reg_metrics$Cp,
Num_Vars = length(vars_reg),
Vars = paste(vars_reg, collapse = ", ")
)
# Ambil dari Lasso dan Ridge (sudah ada di results_df)
lasso_ridge_table = results[, c("Model","Adj_R2","AIC","Cp","Num_Vars","Vars")]
# Ambil dari Best Subset
best_table = data.frame(
Model = paste0("Best Subset (model ", best_subset_table$Model, ")"),
Adj_R2 = best_subset_table$Adj_R2,
AIC = best_subset_table$AIC,
Cp = best_subset_table$Cp,
Num_Vars = length(vars_in_best),
Vars = best_subset_table$Vars
)
# Gabungkan semua
hasil_perbandingan = rbind(lasso_ridge_table, reg_table, best_table)
print(hasil_perbandingan, row.names = FALSE)## Model Adj_R2 AIC Cp Num_Vars
## Lasso_min 0.7292676 1640.160 16.00000 15
## Lasso_1se 0.7119898 1695.666 72.90382 7
## Ridge_min 0.7292676 1640.160 16.00000 15
## Ridge_1se 0.7292676 1640.160 16.00000 15
## Regresi Linear 0.5574054 49861.606 16.00000 15
## Best Subset (model Best Subset (M14)) 0.7293833 -1272.087 14.56843 14
## Vars
## jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_8, lokasi_9, lokasi_10, lokasi_11
## jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_3, lokasi_6
## jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_8, lokasi_9, lokasi_10, lokasi_11
## jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_8, lokasi_9, lokasi_10, lokasi_11
## jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_8, lokasi_9, lokasi_10, lokasi_11
## jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_9, lokasi_10, lokasi_11Hasil Model Terbaik
library(gt)
hasil_perbandingan %>% gt()| Model | Adj_R2 | AIC | Cp | Num_Vars | Vars |
|---|---|---|---|---|---|
| Lasso_min | 0.7292676 | 1640.160 | 16.00000 | 15 | jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_8, lokasi_9, lokasi_10, lokasi_11 |
| Lasso_1se | 0.7119898 | 1695.666 | 72.90382 | 7 | jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_3, lokasi_6 |
| Ridge_min | 0.7292676 | 1640.160 | 16.00000 | 15 | jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_8, lokasi_9, lokasi_10, lokasi_11 |
| Ridge_1se | 0.7292676 | 1640.160 | 16.00000 | 15 | jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_8, lokasi_9, lokasi_10, lokasi_11 |
| Regresi Linear | 0.5574054 | 49861.606 | 16.00000 | 15 | jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_8, lokasi_9, lokasi_10, lokasi_11 |
| Best Subset (model Best Subset (M14)) | 0.7293833 | -1272.087 | 14.56843 | 14 | jml_km_buah, jml_kt_buah, jml_gar_buah, lt_m2, lb_m2, lokasi_2, lokasi_3, lokasi_4, lokasi_5, lokasi_6, lokasi_7, lokasi_9, lokasi_10, lokasi_11 |