TUGAS 2 : Analisis Dataset AirQuality menggunakan PCA dan FA
Lucky Surya Revansyah (24031554090), Nasliyatul Intani (24031554097)
2026-02-21
Load Dataset
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ purrr 1.2.1
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dlookr)## Registered S3 methods overwritten by 'dlookr':
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## print.transform scales
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## Attaching package: 'dlookr'
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## extract
##
## The following object is masked from 'package:base':
##
## transform# menentukan folder data
folderData <- "C:/Users/LUCKY SURYA/Documents/kuliah/semester 4/analisis multivariat/pertemuan 3 anmul"
# membaca data
data_udara <- read_delim(
paste0(folderData, "/AirQualityUCI.csv"),
delim = ";"
)## Rows: 9357 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ";"
## chr (4): CO(GT), C6H6(GT), T, AH
## dbl (8): PT08.S1(CO), NMHC(GT), PT08.S2(NMHC), NOx(GT), PT08.S3(NOx), NO2(GT...
## num (1): RH
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Mengubah koma menjadi titik
data_udara[] <- lapply(data_udara, function(x) as.numeric(gsub(",", ".", x)))
print((data_udara))## # A tibble: 9,357 × 13
## `CO(GT)` `PT08.S1(CO)` `NMHC(GT)` `C6H6(GT)` `PT08.S2(NMHC)` `NOx(GT)`
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2.6 1360 150 11.9 1046 166
## 2 2 1292 112 9.4 955 103
## 3 2.2 1402 88 9 939 131
## 4 2.2 1376 80 9.2 948 172
## 5 1.6 1272 51 6.5 836 131
## 6 1.2 1197 38 4.7 750 89
## 7 1.2 1185 31 3.6 690 62
## 8 1 1136 31 3.3 672 62
## 9 0.9 1094 24 2.3 609 45
## 10 0.6 1010 19 1.7 561 -200
## # ℹ 9,347 more rows
## # ℹ 7 more variables: `PT08.S3(NOx)` <dbl>, `NO2(GT)` <dbl>,
## # `PT08.S4(NO2)` <dbl>, `PT08.S5(O3)` <dbl>, T <dbl>, RH <dbl>, AH <dbl>glimpse(data_udara)## Rows: 9,357
## Columns: 13
## $ `CO(GT)` <dbl> 2.6, 2.0, 2.2, 2.2, 1.6, 1.2, 1.2, 1.0, 0.9, 0.6, -200…
## $ `PT08.S1(CO)` <dbl> 1360, 1292, 1402, 1376, 1272, 1197, 1185, 1136, 1094, …
## $ `NMHC(GT)` <dbl> 150, 112, 88, 80, 51, 38, 31, 31, 24, 19, 14, 8, 16, 2…
## $ `C6H6(GT)` <dbl> 11.9, 9.4, 9.0, 9.2, 6.5, 4.7, 3.6, 3.3, 2.3, 1.7, 1.3…
## $ `PT08.S2(NMHC)` <dbl> 1046, 955, 939, 948, 836, 750, 690, 672, 609, 561, 527…
## $ `NOx(GT)` <dbl> 166, 103, 131, 172, 131, 89, 62, 62, 45, -200, 21, 16,…
## $ `PT08.S3(NOx)` <dbl> 1056, 1174, 1140, 1092, 1205, 1337, 1462, 1453, 1579, …
## $ `NO2(GT)` <dbl> 113, 92, 114, 122, 116, 96, 77, 76, 60, -200, 34, 28, …
## $ `PT08.S4(NO2)` <dbl> 1692, 1559, 1555, 1584, 1490, 1393, 1333, 1333, 1276, …
## $ `PT08.S5(O3)` <dbl> 1268, 972, 1074, 1203, 1110, 949, 733, 730, 620, 501, …
## $ T <dbl> 13.6, 13.3, 11.9, 11.0, 11.2, 11.2, 11.3, 10.7, 10.7, …
## $ RH <dbl> 489, 477, 54, 60, 596, 592, 568, 60, 597, 602, 605, 56…
## $ AH <dbl> 0.7578, 0.7255, 0.7502, 0.7867, 0.7888, 0.7848, 0.7603…karena bahasa r menerima data float menggunakan titik
Boxplot
data_udara %>%
pivot_longer(cols = everything(),
names_to = "Variabel",
values_to = "Nilai") %>%
ggplot(aes(x = Variabel, y = Nilai)) +
geom_boxplot(fill = "orange") +
coord_flip() +
theme_minimal() +
labs(title = "Boxplot Setiap Variabel",
x = NULL,
y = "Nilai")
berdasarkan hasil plot terdapat banyak outlier yang tersebar pada setiap
fitur
Interpolasi
data_udara[data_udara == -200] <- NA
library(zoo)##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericdata_udara <- data_udara %>%
mutate(across(where(is.numeric), ~ na.approx(., na.rm = FALSE))) %>%
mutate(across(where(is.numeric), ~ na.locf(., na.rm = FALSE))) %>%
mutate(across(where(is.numeric), ~ na.locf(., fromLast = TRUE)))pada dataset air quality sebagai data time series nilai dari -200 dianggap sebagai missing value sehingga cara penangannya yaitu di interpolasikan. JIka langsung di drop satu baris hal itu akan sangat mengurangi data yang berpengaruh ke dalam model.
Statistika Deskriptif
stat_deskriptif <- data.frame(
Variabel = colnames(data_udara),
Minimum = apply(data_udara, 2, min, na.rm = TRUE),
Maksimum = apply(data_udara, 2, max, na.rm = TRUE),
Mean = round(apply(data_udara, 2, mean, na.rm = TRUE), 3),
Std_Dev = round(apply(data_udara, 2, sd, na.rm = TRUE), 3)
)
knitr::kable(
stat_deskriptif,
#caption = "Tabel 1. Statistika Deskriptif Variabel Penelitian"
)| Variabel | Minimum | Maksimum | Mean | Std_Dev | |
|---|---|---|---|---|---|
| CO(GT) | CO(GT) | 0.1000 | 11.900 | 2.131 | 1.432 |
| PT08.S1(CO) | PT08.S1(CO) | 647.0000 | 2040.000 | 1103.060 | 218.196 |
| NMHC(GT) | NMHC(GT) | 7.0000 | 1189.000 | 269.834 | 74.252 |
| C6H6(GT) | C6H6(GT) | 0.1000 | 63.700 | 10.179 | 7.504 |
| PT08.S2(NMHC) | PT08.S2(NMHC) | 383.0000 | 2214.000 | 942.143 | 267.867 |
| NOx(GT) | NOx(GT) | 2.0000 | 1479.000 | 241.922 | 204.315 |
| PT08.S3(NOx) | PT08.S3(NOx) | 322.0000 | 2683.000 | 832.759 | 255.710 |
| NO2(GT) | NO2(GT) | 2.0000 | 340.000 | 109.632 | 46.462 |
| PT08.S4(NO2) | PT08.S4(NO2) | 551.0000 | 2775.000 | 1453.299 | 343.206 |
| PT08.S5(O3) | PT08.S5(O3) | 221.0000 | 2523.000 | 1032.544 | 404.448 |
| T | T | -1.9000 | 44.600 | 18.233 | 8.782 |
| RH | RH | 10.0000 | 887.000 | 445.885 | 209.655 |
| AH | AH | 0.1847 | 2.231 | 1.020 | 0.402 |
Scaling (standardisasi data)
data_skala <- scale(data_udara)
head(data_skala)## CO(GT) PT08.S1(CO) NMHC(GT) C6H6(GT) PT08.S2(NMHC) NOx(GT)
## [1,] 0.32785179 1.1775644 -1.613887 0.2293295 0.38772051 -0.3715937
## [2,] -0.09121985 0.8659185 -2.125658 -0.1038345 0.04799919 -0.6799410
## [3,] 0.04847069 1.3700516 -2.448882 -0.1571408 -0.01173204 -0.5428978
## [4,] 0.04847069 1.2508929 -2.556623 -0.1304876 0.02186678 -0.3422273
## [5,] -0.37060094 0.7742580 -2.947185 -0.4903048 -0.39625178 -0.5428978
## [6,] -0.64998204 0.4305309 -3.122264 -0.7301829 -0.71730709 -0.7484626
## PT08.S3(NOx) NO2(GT) PT08.S4(NO2) PT08.S5(O3) T RH
## [1,] 0.8730251 0.07248685 0.6955039 0.5821661 -0.5276154 0.2056493
## [2,] 1.3344856 -0.37949239 0.3079816 -0.1496963 -0.5617770 0.1484123
## [3,] 1.2015224 0.09400967 0.2963268 0.1024996 -0.7211978 -1.8691921
## [4,] 1.0138097 0.26619223 0.3808242 0.4214531 -0.8236825 -1.8405736
## [5,] 1.4557168 0.13705531 0.1069363 0.1915098 -0.8009081 0.7160126
## [6,] 1.9719269 -0.29340111 -0.1756927 -0.2065640 -0.8009081 0.6969336
## AH
## [1,] -0.6509669
## [2,] -0.7312746
## [3,] -0.6698628
## [4,] -0.5791126
## [5,] -0.5738914
## [6,] -0.5838366knitr::kable(
round(head(data_skala, 5), 2)
)| CO(GT) | PT08.S1(CO) | NMHC(GT) | C6H6(GT) | PT08.S2(NMHC) | NOx(GT) | PT08.S3(NOx) | NO2(GT) | PT08.S4(NO2) | PT08.S5(O3) | T | RH | AH |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.33 | 1.18 | -1.61 | 0.23 | 0.39 | -0.37 | 0.87 | 0.07 | 0.70 | 0.58 | -0.53 | 0.21 | -0.65 |
| -0.09 | 0.87 | -2.13 | -0.10 | 0.05 | -0.68 | 1.33 | -0.38 | 0.31 | -0.15 | -0.56 | 0.15 | -0.73 |
| 0.05 | 1.37 | -2.45 | -0.16 | -0.01 | -0.54 | 1.20 | 0.09 | 0.30 | 0.10 | -0.72 | -1.87 | -0.67 |
| 0.05 | 1.25 | -2.56 | -0.13 | 0.02 | -0.34 | 1.01 | 0.27 | 0.38 | 0.42 | -0.82 | -1.84 | -0.58 |
| -0.37 | 0.77 | -2.95 | -0.49 | -0.40 | -0.54 | 1.46 | 0.14 | 0.11 | 0.19 | -0.80 | 0.72 | -0.57 |
Uji Korelasi Pearson
library(corrr)##
## Attaching package: 'corrr'## The following object is masked from 'package:dlookr':
##
## correlatelibrary(ggcorrplot)
matriks_korelasi <- cor(data_skala)
ggcorrplot(matriks_korelasi, lab = TRUE, lab_size = 3, type = "full")## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the ggcorrplot package.
## Please report the issue at <https://github.com/kassambara/ggcorrplot/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
dari heatmap tersebut bisa diambil kesimpulan bahwa matriks tersbut
masih cukup layak karena banyak korelasi cukup tinggi antar variabel
PCA (Principal Component Analysis)
pca_udara <- princomp(data_skala)
pca_summary <- data.frame(
Komponen = paste0("PC", 1:length(pca_udara$sdev)),
`Standard Deviation` = round(pca_udara$sdev, 4),
`Proportion Variance` = round(pca_udara$sdev^2 / sum(pca_udara$sdev^2), 4),
`Cumulative Variance` = round(cumsum(pca_udara$sdev^2 / sum(pca_udara$sdev^2)), 4)
)
knitr::kable(pca_summary)| Komponen | Standard.Deviation | Proportion.Variance | Cumulative.Variance | |
|---|---|---|---|---|
| Comp.1 | PC1 | 2.6037 | 0.5215 | 0.5215 |
| Comp.2 | PC2 | 1.5581 | 0.1868 | 0.7083 |
| Comp.3 | PC3 | 1.1117 | 0.0951 | 0.8034 |
| Comp.4 | PC4 | 0.9631 | 0.0714 | 0.8747 |
| Comp.5 | PC5 | 0.7014 | 0.0379 | 0.9126 |
| Comp.6 | PC6 | 0.6302 | 0.0306 | 0.9432 |
| Comp.7 | PC7 | 0.4683 | 0.0169 | 0.9600 |
| Comp.8 | PC8 | 0.3920 | 0.0118 | 0.9719 |
| Comp.9 | PC9 | 0.3627 | 0.0101 | 0.9820 |
| Comp.10 | PC10 | 0.3161 | 0.0077 | 0.9897 |
| Comp.11 | PC11 | 0.2857 | 0.0063 | 0.9959 |
| Comp.12 | PC12 | 0.2081 | 0.0033 | 0.9993 |
| Comp.13 | PC13 | 0.0972 | 0.0007 | 1.0000 |
pca_udara$loadings[, 1:3]## Comp.1 Comp.2 Comp.3
## CO(GT) 0.340975797 0.07399541 0.001324948
## PT08.S1(CO) 0.359092780 0.01586722 -0.066706932
## NMHC(GT) 0.112437912 -0.03756398 0.144250317
## C6H6(GT) 0.362874010 -0.06753631 0.047210868
## PT08.S2(NMHC) 0.368747904 -0.08129517 0.064517730
## NOx(GT) 0.297159899 0.27876215 -0.052904531
## PT08.S3(NOx) -0.326695996 0.01498722 0.036013305
## NO2(GT) 0.273683169 0.31184602 0.225861539
## PT08.S4(NO2) 0.267100529 -0.40869590 -0.118403515
## PT08.S5(O3) 0.357056411 0.08614507 -0.060690693
## T 0.045864701 -0.56836652 0.253997995
## RH 0.006864794 0.14714273 -0.844480377
## AH 0.059779663 -0.53606540 -0.343328921loadings_pca3 <- as.data.frame(unclass(pca_udara$loadings[,1:3]))
knitr::kable(
round(loadings_pca3, 3)
)| Comp.1 | Comp.2 | Comp.3 | |
|---|---|---|---|
| CO(GT) | 0.341 | 0.074 | 0.001 |
| PT08.S1(CO) | 0.359 | 0.016 | -0.067 |
| NMHC(GT) | 0.112 | -0.038 | 0.144 |
| C6H6(GT) | 0.363 | -0.068 | 0.047 |
| PT08.S2(NMHC) | 0.369 | -0.081 | 0.065 |
| NOx(GT) | 0.297 | 0.279 | -0.053 |
| PT08.S3(NOx) | -0.327 | 0.015 | 0.036 |
| NO2(GT) | 0.274 | 0.312 | 0.226 |
| PT08.S4(NO2) | 0.267 | -0.409 | -0.118 |
| PT08.S5(O3) | 0.357 | 0.086 | -0.061 |
| T | 0.046 | -0.568 | 0.254 |
| RH | 0.007 | 0.147 | -0.844 |
| AH | 0.060 | -0.536 | -0.343 |
Scree Plot
screeplot(pca_udara, type = "l", npcs = 12, main = "Screeplot Komponen Utama")
abline(h = 1, col="red", lty=5)
legend("topright", legend=c("Eigenvalue = 1"),
col=c("red"), lty=5, cex=0.6)
skor_pca <- as.data.frame(pca_udara$scores[,1:3])
names(skor_pca)[1] <- "PC1"
names(skor_pca)[2] <- "PC2"
names(skor_pca)[3] <- "PC3"
write_csv(skor_pca, "skor_pca_kualitas_udara.csv")Biplot PCA
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBafviz_pca_biplot(pca_udara, label = "var", axes = 1:2,addEllipses=TRUE, ellipse.level=0.95)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the ggpubr package.
## Please report the issue at <https://github.com/kassambara/ggpubr/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Kontribusi Variabel
library(factoextra)
# Kontribusi variabel terhadap PC1
fviz_contrib(pca_udara, choice = "var", axes = 1, top = 10)
# Kontribusi variabel terhadap PC2
fviz_contrib(pca_udara, choice = "var", axes = 2, top = 10)
# Kontribusi variabel terhadap PC3
fviz_contrib(pca_udara, choice = "var", axes = 3, top = 10)
Visualisasi Varaibel PCA
#| fig.width: 6
#| fig.height: 5
fviz_pca_var(pca_udara, col.var = "cos2",
gradient.cols = c("black", "orange", "green"),
repel = TRUE)
FA (Factor Analysis)
library(psych)##
## Attaching package: 'psych'## The following object is masked from 'package:dlookr':
##
## describe## The following objects are masked from 'package:ggplot2':
##
## %+%, alphadata_bersih <- data_skala[, colnames(data_skala) != "NMHC(GT)"]
R2 <- cor(data_bersih)
KMO(R2)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = R2)
## Overall MSA = 0.82
## MSA for each item =
## CO(GT) PT08.S1(CO) C6H6(GT) PT08.S2(NMHC) NOx(GT)
## 0.91 0.94 0.84 0.79 0.81
## PT08.S3(NOx) NO2(GT) PT08.S4(NO2) PT08.S5(O3) T
## 0.82 0.89 0.75 0.95 0.57
## RH AH
## 0.36 0.44fa_result <- fa(R2,
nfactors = 2,
fm = "pa",
rotate = "varimax")
kmo_result <- KMO(R2)
kmo_table <- data.frame(
Variabel = names(kmo_result$MSAi),
MSA = round(kmo_result$MSAi,3)
)
uji_barlets<-cortest.bartlett(R2, n = nrow(data_bersih))
knitr::kable(
kmo_table,
#caption = "Tabel 3. Nilai MSA Tiap Variabel"
)| Variabel | MSA | |
|---|---|---|
| CO(GT) | CO(GT) | 0.913 |
| PT08.S1(CO) | PT08.S1(CO) | 0.943 |
| C6H6(GT) | C6H6(GT) | 0.838 |
| PT08.S2(NMHC) | PT08.S2(NMHC) | 0.787 |
| NOx(GT) | NOx(GT) | 0.812 |
| PT08.S3(NOx) | PT08.S3(NOx) | 0.816 |
| NO2(GT) | NO2(GT) | 0.890 |
| PT08.S4(NO2) | PT08.S4(NO2) | 0.752 |
| PT08.S5(O3) | PT08.S5(O3) | 0.945 |
| T | T | 0.574 |
| RH | RH | 0.360 |
| AH | AH | 0.438 |
knitr::kable(
uji_barlets,
#caption = "Tabel 3. Nilai MSA Tiap Variabel"
)
|
|
|
Scree Plot FA
scree(data_skala, pc = FALSE)
Parallel Analysis
fa.parallel(data_skala)
## Parallel analysis suggests that the number of factors = 5 and the number of components = 3Ekstrasi Faktor
hasil_fa <- fa(r = data_skala,
nfactors = 2,
fm = "pa",
rotate = "varimax")
print(hasil_fa, sort = TRUE)## Factor Analysis using method = pa
## Call: fa(r = data_skala, nfactors = 2, rotate = "varimax", fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item PA1 PA2 h2 u2 com
## PT08.S2(NMHC) 5 0.95 0.22 0.950 0.050 1.1
## PT08.S5(O3) 10 0.93 -0.05 0.875 0.125 1.0
## C6H6(GT) 4 0.93 0.19 0.903 0.097 1.1
## PT08.S1(CO) 2 0.93 0.06 0.863 0.137 1.0
## CO(GT) 1 0.88 -0.04 0.768 0.232 1.0
## PT08.S3(NOx) 7 -0.82 -0.09 0.673 0.327 1.0
## NOx(GT) 6 0.79 -0.35 0.747 0.253 1.4
## NO2(GT) 8 0.73 -0.40 0.691 0.309 1.6
## NMHC(GT) 3 0.25 0.06 0.067 0.933 1.1
## T 11 0.04 0.81 0.660 0.340 1.0
## AH 13 0.08 0.76 0.585 0.415 1.0
## PT08.S4(NO2) 9 0.63 0.72 0.921 0.079 2.0
## RH 12 0.03 -0.14 0.021 0.979 1.1
##
## PA1 PA2
## SS loadings 6.56 2.16
## Proportion Var 0.50 0.17
## Cumulative Var 0.50 0.67
## Proportion Explained 0.75 0.25
## Cumulative Proportion 0.75 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 2 factors are sufficient.
##
## df null model = 78 with the objective function = 16.72 with Chi Square = 156367
## df of the model are 53 and the objective function was 3.75
##
## The root mean square of the residuals (RMSR) is 0.06
## The df corrected root mean square of the residuals is 0.08
##
## The harmonic n.obs is 9357 with the empirical chi square 2829.06 with prob < 0
## The total n.obs was 9357 with Likelihood Chi Square = 35025.16 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.671
## RMSEA index = 0.266 and the 90 % confidence intervals are 0.263 0.268
## BIC = 34540.54
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## PA1 PA2
## Correlation of (regression) scores with factors 0.99 0.96
## Multiple R square of scores with factors 0.98 0.91
## Minimum correlation of possible factor scores 0.96 0.83loading_fa <- as.data.frame(unclass(hasil_fa$loadings[,1:2]))
knitr::kable(
round(loading_fa,3),
#caption = "Tabel 4. Loading Faktor"
)| PA1 | PA2 | |
|---|---|---|
| CO(GT) | 0.876 | -0.036 |
| PT08.S1(CO) | 0.927 | 0.060 |
| NMHC(GT) | 0.251 | 0.058 |
| C6H6(GT) | 0.931 | 0.191 |
| PT08.S2(NMHC) | 0.951 | 0.216 |
| NOx(GT) | 0.790 | -0.350 |
| PT08.S3(NOx) | -0.815 | -0.090 |
| NO2(GT) | 0.729 | -0.400 |
| PT08.S4(NO2) | 0.633 | 0.721 |
| PT08.S5(O3) | 0.934 | -0.052 |
| T | 0.040 | 0.811 |
| RH | 0.029 | -0.143 |
| AH | 0.078 | 0.761 |
Diagram Faktor
fa.diagram(hasil_fa)
Alternatif dengan Factanal
hasil_factanal <- factanal(data_skala,
factors = 2,
scores = "Bartlett",
rotation = "varimax")
print(hasil_factanal, sort = TRUE)##
## Call:
## factanal(x = data_skala, factors = 2, scores = "Bartlett", rotation = "varimax")
##
## Uniquenesses:
## CO(GT) PT08.S1(CO) NMHC(GT) C6H6(GT) PT08.S2(NMHC)
## 0.277 0.168 0.943 0.031 0.012
## NOx(GT) PT08.S3(NOx) NO2(GT) PT08.S4(NO2) PT08.S5(O3)
## 0.284 0.360 0.296 0.087 0.160
## T RH AH
## 0.419 0.987 0.402
##
## Loadings:
## Factor1 Factor2
## CO(GT) 0.849
## PT08.S1(CO) 0.898 0.158
## C6H6(GT) 0.941 0.290
## PT08.S2(NMHC) 0.947 0.302
## NOx(GT) 0.790 -0.304
## PT08.S3(NOx) -0.792 -0.117
## NO2(GT) 0.751 -0.375
## PT08.S5(O3) 0.916
## PT08.S4(NO2) 0.567 0.769
## T 0.762
## AH 0.772
## NMHC(GT) 0.232
## RH -0.112
##
## Factor1 Factor2
## SS loadings 6.341 2.235
## Proportion Var 0.488 0.172
## Cumulative Var 0.488 0.660
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 31486.52 on 53 degrees of freedom.
## The p-value is 0Matriks Loading dalam bentuk Tidy
matriks_loading <- broom::tidy(hasil_factanal) %>%
pivot_longer(starts_with("fl"),
names_to = "faktor",
values_to = "loading")Heatmap Loading
fa_loading_plot <- ggplot(matriks_loading,
aes(x = faktor, y = variable, fill = loading)) +
geom_tile() +
labs(title = "Loading Faktor",
x = NULL,
y = NULL) +
scico::scale_fill_scico(palette = "cork", limits = c(-1,1)) +
coord_fixed(ratio = 1/2)
print(fa_loading_plot)
Korelasi Skor Faktor
hasil_factanal$scores %>%
cor() %>%
data.frame() %>%
rownames_to_column("faktor_x") %>%
pivot_longer(cols = -faktor_x,
names_to = "faktor_y",
values_to = "korelasi") %>%
ggplot(aes(x = faktor_x, y = faktor_y, fill = korelasi)) +
geom_tile() +
geom_text(aes(label = round(korelasi,4)), color = "white") +
labs(title = "Korelasi antar Skor Faktor",
x = NULL,
y = NULL) +
scico::scale_fill_scico(palette = "berlin", limits = c(-1,1)) +
coord_equal()