Implementasi Principal Component Analysis (PCA) dan Factor Analysis (FA) pada Global Data on Sustainable Energy (2000-2020)
Stella Dagatha (24031554080)
Na’illah Shania Rizqillah (24031554225)
Kelas 2024A
1. Import Data
Di tahap ini, dataset CSV dimuat ke dalam R untuk dianalisis.
data <- read.csv("C:/Users/ella/OneDrive/ドã‚ュメント/Semester 4/Tugas2_Anmul/global-data-on-sustainable-energy (1).csv")2. Cek struktur dan ringkasan
Memeriksa struktur dataset dan ringkasan statistik awal untuk mengetahui tipe data dan distribusi nilai.
str(data)## 'data.frame': 3649 obs. of 21 variables:
## $ Entity : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ Year : int 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 ...
## $ Access.to.electricity....of.population. : num 1.61 4.07 9.41 14.74 20.06 ...
## $ Access.to.clean.fuels.for.cooking : num 6.2 7.2 8.2 9.5 10.9 ...
## $ Renewable.electricity.generating.capacity.per.capita : num 9.22 8.86 8.47 8.09 7.75 7.51 7.4 7.25 7.49 7.5 ...
## $ Financial.flows.to.developing.countries..US... : num 20000 130000 3950000 25970000 NA ...
## $ Renewable.energy.share.in.the.total.final.energy.consumption....: num 45 45.6 37.8 36.7 44.2 ...
## $ Electricity.from.fossil.fuels..TWh. : num 0.16 0.09 0.13 0.31 0.33 0.34 0.2 0.2 0.19 0.16 ...
## $ Electricity.from.nuclear..TWh. : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Electricity.from.renewables..TWh. : num 0.31 0.5 0.56 0.63 0.56 0.59 0.64 0.75 0.54 0.78 ...
## $ Low.carbon.electricity....electricity. : num 66 84.7 81.2 67 62.9 ...
## $ Primary.energy.consumption.per.capita..kWh.person. : num 303 237 211 230 204 ...
## $ Energy.intensity.level.of.primary.energy..MJ..2017.PPP.GDP. : num 1.64 1.74 1.4 1.4 1.2 1.41 1.5 1.53 1.94 2.25 ...
## $ Value_co2_emissions_kt_by_country : num 760 730 1030 1220 1030 ...
## $ Renewables....equivalent.primary.energy. : num NA NA NA NA NA NA NA NA NA NA ...
## $ gdp_growth : num NA NA NA 8.83 1.41 ...
## $ gdp_per_capita : num NA NA 179 191 211 ...
## $ Density.n.P.Km2. : chr "60" "60" "60" "60" ...
## $ Land.Area.Km2. : int 652230 652230 652230 652230 652230 652230 652230 652230 652230 652230 ...
## $ Latitude : num 33.9 33.9 33.9 33.9 33.9 ...
## $ Longitude : num 67.7 67.7 67.7 67.7 67.7 ...summary(data)## Entity Year Access.to.electricity....of.population.
## Length:3649 Min. :2000 Min. : 1.252
## Class :character 1st Qu.:2005 1st Qu.: 59.801
## Mode :character Median :2010 Median : 98.362
## Mean :2010 Mean : 78.934
## 3rd Qu.:2015 3rd Qu.:100.000
## Max. :2020 Max. :100.000
## NA's :10
## Access.to.clean.fuels.for.cooking
## Min. : 0.00
## 1st Qu.: 23.18
## Median : 83.15
## Mean : 63.26
## 3rd Qu.:100.00
## Max. :100.00
## NA's :169
## Renewable.electricity.generating.capacity.per.capita
## Min. : 0.00
## 1st Qu.: 3.54
## Median : 32.91
## Mean : 113.14
## 3rd Qu.: 112.21
## Max. :3060.19
## NA's :931
## Financial.flows.to.developing.countries..US...
## Min. :0.000e+00
## 1st Qu.:2.600e+05
## Median :5.665e+06
## Mean :9.422e+07
## 3rd Qu.:5.535e+07
## Max. :5.202e+09
## NA's :2089
## Renewable.energy.share.in.the.total.final.energy.consumption....
## Min. : 0.000
## 1st Qu.: 6.515
## Median :23.300
## Mean :32.638
## 3rd Qu.:55.245
## Max. :96.040
## NA's :194
## Electricity.from.fossil.fuels..TWh. Electricity.from.nuclear..TWh.
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.29 1st Qu.: 0.00
## Median : 2.97 Median : 0.00
## Mean : 70.36 Mean : 13.45
## 3rd Qu.: 26.84 3rd Qu.: 0.00
## Max. :5184.13 Max. :809.41
## NA's :21 NA's :126
## Electricity.from.renewables..TWh. Low.carbon.electricity....electricity.
## Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.04 1st Qu.: 2.878
## Median : 1.47 Median : 27.865
## Mean : 23.97 Mean : 36.801
## 3rd Qu.: 9.60 3rd Qu.: 64.404
## Max. :2184.94 Max. :100.000
## NA's :21 NA's :42
## Primary.energy.consumption.per.capita..kWh.person.
## Min. : 0
## 1st Qu.: 3117
## Median : 13121
## Mean : 25744
## 3rd Qu.: 33893
## Max. :262586
##
## Energy.intensity.level.of.primary.energy..MJ..2017.PPP.GDP.
## Min. : 0.110
## 1st Qu.: 3.170
## Median : 4.300
## Mean : 5.307
## 3rd Qu.: 6.027
## Max. :32.570
## NA's :207
## Value_co2_emissions_kt_by_country Renewables....equivalent.primary.energy.
## Min. : 10 Min. : 0.000
## 1st Qu.: 2020 1st Qu.: 2.137
## Median : 10500 Median : 6.291
## Mean : 159866 Mean :11.987
## 3rd Qu.: 60580 3rd Qu.:16.842
## Max. :10707220 Max. :86.837
## NA's :428 NA's :2137
## gdp_growth gdp_per_capita Density.n.P.Km2. Land.Area.Km2.
## Min. :-62.076 Min. : 111.9 Length:3649 Min. : 21
## 1st Qu.: 1.383 1st Qu.: 1337.8 Class :character 1st Qu.: 25713
## Median : 3.560 Median : 4578.6 Mode :character Median : 117600
## Mean : 3.442 Mean : 13283.8 Mean : 633213
## 3rd Qu.: 5.830 3rd Qu.: 15768.6 3rd Qu.: 513120
## Max. :123.140 Max. :123514.2 Max. :9984670
## NA's :317 NA's :282 NA's :1
## Latitude Longitude
## Min. :-40.901 Min. :-175.20
## 1st Qu.: 3.203 1st Qu.: -11.78
## Median : 17.190 Median : 19.15
## Mean : 18.246 Mean : 14.82
## 3rd Qu.: 38.970 3rd Qu.: 46.20
## Max. : 64.963 Max. : 178.07
## NA's :1 NA's :13. Data Cleaning dan Preprocessing
Tahap ini bertujuan menyiapkan data agar siap untuk analisis PCA dan FA. ### 3.1 Ubah kolom karakter menjadi numeric
data$Density.n.P.Km2. <- as.numeric(data$Density.n.P.Km2.)## Warning: NAs introduced by coercion3.2 Pilih semua kolom numeric
data_numeric <- data[, sapply(data, is.numeric)]3.3 Hapus kolom yang tidak relevan
Kolom seperti Year, Latitude, Longitude, dan Electricity from nuclear dihapus karena tidak relevan atau sebagian besar bernilai 0.
sum(data$Electricity.from.nuclear..TWh. == 0, na.rm = TRUE)## [1] 2945data_numeric <- subset(data_numeric,
select = -c(Year, Latitude, Longitude, Land.Area.Km2.,
Electricity.from.nuclear..TWh.))3.4 Cek Persentase Missing Value per Variabel
Memeriksa seberapa banyak data hilang di tiap variabel.
na_percent <- colSums(is.na(data_numeric)) / nrow(data_numeric) * 100
tabel_na <- data.frame(
Variabel = names(na_percent),
Persen_NA = round(na_percent, 2)
)
kable(tabel_na,
caption = "Tabel Persentase Missing Value per Variabel",
row.names = FALSE)| Variabel | Persen_NA |
|---|---|
| Access.to.electricity….of.population. | 0.27 |
| Access.to.clean.fuels.for.cooking | 4.63 |
| Renewable.electricity.generating.capacity.per.capita | 25.51 |
| Financial.flows.to.developing.countries..US… | 57.25 |
| Renewable.energy.share.in.the.total.final.energy.consumption…. | 5.32 |
| Electricity.from.fossil.fuels..TWh. | 0.58 |
| Electricity.from.renewables..TWh. | 0.58 |
| Low.carbon.electricity….electricity. | 1.15 |
| Primary.energy.consumption.per.capita..kWh.person. | 0.00 |
| Energy.intensity.level.of.primary.energy..MJ..2017.PPP.GDP. | 5.67 |
| Value_co2_emissions_kt_by_country | 11.73 |
| Renewables….equivalent.primary.energy. | 58.56 |
| gdp_growth | 8.69 |
| gdp_per_capita | 7.73 |
| Density.n.P.Km2. | 2.90 |
3.5 Hapus variabel dengan NA > 30%
Variabel dengan missing value lebih dari 30% dihapus untuk menjaga kualitas data.
threshold <- 30
variabel_dihapus <- names(na_percent[na_percent > threshold])
data_numeric <- data_numeric[, na_percent <= threshold]
variabel_dihapus## [1] "Financial.flows.to.developing.countries..US..."
## [2] "Renewables....equivalent.primary.energy."3.6 Cek Skewness
Melihat distribusi data, apakah simetris atau skewed.
desc_stats <- describe(data_numeric)
tabel_skew <- data.frame(
Variabel = rownames(desc_stats),
Skewness = round(desc_stats$skew, 3)
)
kable(tabel_skew, caption = "Nilai Skewness Tiap Variabel")| Variabel | Skewness |
|---|---|
| Access.to.electricity….of.population. | -1.205 |
| Access.to.clean.fuels.for.cooking | -0.508 |
| Renewable.electricity.generating.capacity.per.capita | 5.361 |
| Renewable.energy.share.in.the.total.final.energy.consumption…. | 0.670 |
| Electricity.from.fossil.fuels..TWh. | 9.389 |
| Electricity.from.renewables..TWh. | 11.048 |
| Low.carbon.electricity….electricity. | 0.506 |
| Primary.energy.consumption.per.capita..kWh.person. | 2.649 |
| Energy.intensity.level.of.primary.energy..MJ..2017.PPP.GDP. | 2.587 |
| Value_co2_emissions_kt_by_country | 9.329 |
| gdp_growth | 2.540 |
| gdp_per_capita | 2.357 |
| Density.n.P.Km2. | 2.797 |
3.7 Tangani Missing Value dengan Median Imputation
Mengganti nilai hilang dengan median tiap kolom agar tidak mempengaruhi analisis.
for(i in 1:ncol(data_numeric)){
data_numeric[is.na(data_numeric[, i]), i] <-
median(data_numeric[, i], na.rm = TRUE)
}3.8 Cek kembali apakah masih ada Missing Value (NA)
colSums(is.na(data_numeric))## Access.to.electricity....of.population.
## 0
## Access.to.clean.fuels.for.cooking
## 0
## Renewable.electricity.generating.capacity.per.capita
## 0
## Renewable.energy.share.in.the.total.final.energy.consumption....
## 0
## Electricity.from.fossil.fuels..TWh.
## 0
## Electricity.from.renewables..TWh.
## 0
## Low.carbon.electricity....electricity.
## 0
## Primary.energy.consumption.per.capita..kWh.person.
## 0
## Energy.intensity.level.of.primary.energy..MJ..2017.PPP.GDP.
## 0
## Value_co2_emissions_kt_by_country
## 0
## gdp_growth
## 0
## gdp_per_capita
## 0
## Density.n.P.Km2.
## 03.9 Mengecek Outlier
3.9.1 Visualisasi Outlier dengan Boxplot
for(i in 1:ncol(data_numeric)){
boxplot(data_numeric[, i],
main = paste("Boxplot", colnames(data_numeric)[i]),
col = "lightblue",
ylab = colnames(data_numeric)[i],
cex.main = 1,
cex.lab = 0.8,
cex.axis = 0.8)
}
3.9.2 Analisis Outlier per Variabel
count_outliers <- function(x){
Q1 <- quantile(x, 0.25, na.rm = TRUE)
Q3 <- quantile(x, 0.75, na.rm = TRUE)
IQR <- Q3 - Q1
sum(x < (Q1 - 1.5*IQR) | x > (Q3 + 1.5*IQR), na.rm = TRUE)
}
outlier_counts <- sapply(data_numeric, count_outliers)
outlier_table <- data.frame(
Variabel = names(outlier_counts),
Jumlah_Outlier = outlier_counts
)
outlier_table <- outlier_table[order(-outlier_table$Jumlah_Outlier), ]
for(i in 1:nrow(outlier_table)){
cat(outlier_table$Variabel[i], "=", outlier_table$Jumlah_Outlier[i], "outlier\n")
}## Electricity.from.renewables..TWh. = 533 outlier
## Electricity.from.fossil.fuels..TWh. = 519 outlier
## Value_co2_emissions_kt_by_country = 509 outlier
## gdp_per_capita = 475 outlier
## Renewable.electricity.generating.capacity.per.capita = 469 outlier
## Energy.intensity.level.of.primary.energy..MJ..2017.PPP.GDP. = 316 outlier
## gdp_growth = 282 outlier
## Primary.energy.consumption.per.capita..kWh.person. = 237 outlier
## Density.n.P.Km2. = 231 outlier
## Access.to.electricity....of.population. = 0 outlier
## Access.to.clean.fuels.for.cooking = 0 outlier
## Renewable.energy.share.in.the.total.final.energy.consumption.... = 0 outlier
## Low.carbon.electricity....electricity. = 0 outlier3.10 Handle Outlier
Membatasi nilai ekstrem agar tidak mempengaruhi analisis.
winsorize <- function(x){
Q1 <- quantile(x, 0.25, na.rm = TRUE)
Q3 <- quantile(x, 0.75, na.rm = TRUE)
IQR <- Q3 - Q1
lower <- Q1 - 1.5*IQR
upper <- Q3 + 1.5*IQR
x[x < lower] <- lower
x[x > upper] <- upper
return(x)
}
data_numeric_clean <- as.data.frame(lapply(data_numeric, winsorize))
head(data_numeric_clean)## Access.to.electricity....of.population. Access.to.clean.fuels.for.cooking
## 1 1.613591 6.2
## 2 4.074574 7.2
## 3 9.409158 8.2
## 4 14.738506 9.5
## 5 20.064968 10.9
## 6 25.390894 12.2
## Renewable.electricity.generating.capacity.per.capita
## 1 9.22
## 2 8.86
## 3 8.47
## 4 8.09
## 5 7.75
## 6 7.51
## Renewable.energy.share.in.the.total.final.energy.consumption....
## 1 44.99
## 2 45.60
## 3 37.83
## 4 36.66
## 5 44.24
## 6 33.88
## Electricity.from.fossil.fuels..TWh. Electricity.from.renewables..TWh.
## 1 0.16 0.31
## 2 0.09 0.50
## 3 0.13 0.56
## 4 0.31 0.63
## 5 0.33 0.56
## 6 0.34 0.59
## Low.carbon.electricity....electricity.
## 1 65.95744
## 2 84.74577
## 3 81.15942
## 4 67.02128
## 5 62.92135
## 6 63.44086
## Primary.energy.consumption.per.capita..kWh.person.
## 1 302.5948
## 2 236.8919
## 3 210.8622
## 4 229.9682
## 5 204.2312
## 6 252.0691
## Energy.intensity.level.of.primary.energy..MJ..2017.PPP.GDP.
## 1 1.64
## 2 1.74
## 3 1.40
## 4 1.40
## 5 1.20
## 6 1.41
## Value_co2_emissions_kt_by_country gdp_growth gdp_per_capita Density.n.P.Km2.
## 1 760 3.559855 4578.6332 60
## 2 730 3.559855 4578.6332 60
## 3 1030 3.559855 179.4266 60
## 4 1220 8.832278 190.6838 60
## 5 1030 1.414118 211.3821 60
## 6 1550 11.229715 242.0313 603.11 Cek outlier setelah winsorizing
count_outliers <- function(x){
Q1 <- quantile(x, 0.25, na.rm = TRUE)
Q3 <- quantile(x, 0.75, na.rm = TRUE)
IQR <- Q3 - Q1
sum(x < (Q1 - 1.5*IQR) | x > (Q3 + 1.5*IQR), na.rm = TRUE)
}
outlier_counts_after <- sapply(data_numeric_clean, count_outliers)
for(i in 1:length(outlier_counts_after)){
cat(names(outlier_counts_after)[i], "=", outlier_counts_after[i], "outlier\n")
}## Access.to.electricity....of.population. = 0 outlier
## Access.to.clean.fuels.for.cooking = 0 outlier
## Renewable.electricity.generating.capacity.per.capita = 0 outlier
## Renewable.energy.share.in.the.total.final.energy.consumption.... = 0 outlier
## Electricity.from.fossil.fuels..TWh. = 0 outlier
## Electricity.from.renewables..TWh. = 0 outlier
## Low.carbon.electricity....electricity. = 0 outlier
## Primary.energy.consumption.per.capita..kWh.person. = 0 outlier
## Energy.intensity.level.of.primary.energy..MJ..2017.PPP.GDP. = 0 outlier
## Value_co2_emissions_kt_by_country = 0 outlier
## gdp_growth = 0 outlier
## gdp_per_capita = 0 outlier
## Density.n.P.Km2. = 0 outlier4. Standarisasi Data
Data dinormalisasi agar tiap variabel memiliki skala yang sama untuk PCA/FA.
data_scaled <- scale(data_numeric_clean)5. Karakteristik Data setelah preprocessing
5.1 Jumlah Observasi dan Variabel
n_obs <- nrow(data_numeric_clean)
n_var <- ncol(data_numeric_clean)
n_obs## [1] 3649n_var## [1] 135.2 Tampilan Data (Head)
kable(head(data_numeric_clean, 10),
caption = "Tabel 1. Sampel Data Setelah Preprocessing")| Access.to.electricity….of.population. | Access.to.clean.fuels.for.cooking | Renewable.electricity.generating.capacity.per.capita | Renewable.energy.share.in.the.total.final.energy.consumption…. | Electricity.from.fossil.fuels..TWh. | Electricity.from.renewables..TWh. | Low.carbon.electricity….electricity. | Primary.energy.consumption.per.capita..kWh.person. | Energy.intensity.level.of.primary.energy..MJ..2017.PPP.GDP. | Value_co2_emissions_kt_by_country | gdp_growth | gdp_per_capita | Density.n.P.Km2. |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.613591 | 6.20 | 9.22 | 44.99 | 0.16 | 0.31 | 65.95744 | 302.5948 | 1.64 | 760 | 3.559855 | 4578.6332 | 60 |
| 4.074574 | 7.20 | 8.86 | 45.60 | 0.09 | 0.50 | 84.74577 | 236.8919 | 1.74 | 730 | 3.559855 | 4578.6332 | 60 |
| 9.409158 | 8.20 | 8.47 | 37.83 | 0.13 | 0.56 | 81.15942 | 210.8622 | 1.40 | 1030 | 3.559855 | 179.4266 | 60 |
| 14.738506 | 9.50 | 8.09 | 36.66 | 0.31 | 0.63 | 67.02128 | 229.9682 | 1.40 | 1220 | 8.832278 | 190.6838 | 60 |
| 20.064968 | 10.90 | 7.75 | 44.24 | 0.33 | 0.56 | 62.92135 | 204.2312 | 1.20 | 1030 | 1.414118 | 211.3821 | 60 |
| 25.390894 | 12.20 | 7.51 | 33.88 | 0.34 | 0.59 | 63.44086 | 252.0691 | 1.41 | 1550 | 11.229715 | 242.0313 | 60 |
| 30.718690 | 13.85 | 7.40 | 31.89 | 0.20 | 0.64 | 76.19048 | 304.4209 | 1.50 | 1760 | 5.357403 | 263.7336 | 60 |
| 36.051010 | 15.30 | 7.25 | 28.78 | 0.20 | 0.75 | 78.94737 | 354.2799 | 1.53 | 1770 | 11.381768 | 359.6932 | 60 |
| 42.400000 | 16.70 | 7.49 | 21.17 | 0.19 | 0.54 | 73.97260 | 607.8335 | 1.94 | 3560 | 3.924984 | 364.6635 | 60 |
| 46.740050 | 18.40 | 7.50 | 16.53 | 0.16 | 0.78 | 82.97872 | 975.0482 | 2.25 | 4880 | 11.381768 | 437.2687 | 60 |
5.3 Statistik Deskriptif
desc_stats <- describe(data_numeric_clean)
tabel_deskriptif <- data.frame(
Variabel = rownames(desc_stats),
Mean = round(desc_stats$mean, 3),
SD = round(desc_stats$sd, 3),
Median = round(apply(data_numeric_clean, 2, median, na.rm = TRUE), 3),
Min = round(desc_stats$min, 3),
Max = round(desc_stats$max, 3),
row.names = NULL
)
kable(tabel_deskriptif,
caption = "Tabel Statistik Deskriptif Variabel",
align = 'c',
digits = 3)| Variabel | Mean | SD | Median | Min | Max |
|---|---|---|---|---|---|
| Access.to.electricity….of.population. | 78.987 | 30.251 | 98.362 | 1.252 | 100.000 |
| Access.to.clean.fuels.for.cooking | 64.177 | 38.357 | 83.150 | 0.000 | 100.000 |
| Renewable.electricity.generating.capacity.per.capita | 50.782 | 52.472 | 32.910 | 0.000 | 156.415 |
| Renewable.energy.share.in.the.total.final.energy.consumption…. | 32.142 | 29.165 | 23.300 | 0.000 | 96.040 |
| Electricity.from.fossil.fuels..TWh. | 17.102 | 23.945 | 2.970 | 0.000 | 65.850 |
| Electricity.from.renewables..TWh. | 6.368 | 8.673 | 1.470 | 0.000 | 23.825 |
| Low.carbon.electricity….electricity. | 36.698 | 34.130 | 27.865 | 0.000 | 100.000 |
| Primary.energy.consumption.per.capita..kWh.person. | 22351.646 | 23614.715 | 13120.570 | 0.000 | 80056.844 |
| Energy.intensity.level.of.primary.energy..MJ..2017.PPP.GDP. | 4.890 | 2.284 | 4.300 | 0.110 | 9.870 |
| Value_co2_emissions_kt_by_country | 33944.478 | 44063.705 | 10500.000 | 10.000 | 124560.005 |
| gdp_growth | 3.499 | 3.537 | 3.560 | -4.187 | 11.382 |
| gdp_per_capita | 9740.795 | 11178.232 | 4578.633 | 111.927 | 32784.472 |
| Density.n.P.Km2. | 129.109 | 128.535 | 83.000 | 2.000 | 456.500 |
5.4 Visualisasi Data
for(i in 1:ncol(data_numeric_clean)){
boxplot(data_numeric_clean[, i],
main = paste("Boxplot", colnames(data_numeric_clean)[i]),
col = "lightblue",
ylab = colnames(data_numeric_clean)[i],
cex.main = 1,
cex.lab = 0.8,
cex.axis = 0.8)
}
6. Uji Kelayakan PCA/FA
6.1 KMO Test
Menilai kecukupan sampel untuk PCA/FA.
KMO(data_scaled)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = data_scaled)
## Overall MSA = 0.73
## MSA for each item =
## Access.to.electricity....of.population.
## 0.83
## Access.to.clean.fuels.for.cooking
## 0.85
## Renewable.electricity.generating.capacity.per.capita
## 0.63
## Renewable.energy.share.in.the.total.final.energy.consumption....
## 0.83
## Electricity.from.fossil.fuels..TWh.
## 0.70
## Electricity.from.renewables..TWh.
## 0.73
## Low.carbon.electricity....electricity.
## 0.56
## Primary.energy.consumption.per.capita..kWh.person.
## 0.69
## Energy.intensity.level.of.primary.energy..MJ..2017.PPP.GDP.
## 0.46
## Value_co2_emissions_kt_by_country
## 0.74
## gdp_growth
## 0.74
## gdp_per_capita
## 0.68
## Density.n.P.Km2.
## 0.536.2 Bartlett Test
Mengukur apakah korelasi antar variabel signifikan.
cortest.bartlett(data_scaled)## R was not square, finding R from data## $chisq
## [1] 32067.45
##
## $p.value
## [1] 0
##
## $df
## [1] 786.3 Hapus variabel MSA rendah (<0.5)
data_refined <- subset(data_scaled,
select = -c( Energy.intensity.level.of.primary.energy..MJ..2017.PPP.GDP.))6.4 Cek KMO setelah penghapusan
KMO(data_refined)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = data_refined)
## Overall MSA = 0.75
## MSA for each item =
## Access.to.electricity....of.population.
## 0.82
## Access.to.clean.fuels.for.cooking
## 0.85
## Renewable.electricity.generating.capacity.per.capita
## 0.60
## Renewable.energy.share.in.the.total.final.energy.consumption....
## 0.85
## Electricity.from.fossil.fuels..TWh.
## 0.70
## Electricity.from.renewables..TWh.
## 0.71
## Low.carbon.electricity....electricity.
## 0.55
## Primary.energy.consumption.per.capita..kWh.person.
## 0.75
## Value_co2_emissions_kt_by_country
## 0.73
## gdp_growth
## 0.67
## gdp_per_capita
## 0.73
## Density.n.P.Km2.
## 0.456.3 Hapus variabel MSA rendah (<0.5)
data_refined2 <- subset(data_refined,
select = -c( Density.n.P.Km2.))6.4 Cek KMO setelah penghapusan
KMO(data_refined2)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = data_refined2)
## Overall MSA = 0.76
## MSA for each item =
## Access.to.electricity....of.population.
## 0.83
## Access.to.clean.fuels.for.cooking
## 0.85
## Renewable.electricity.generating.capacity.per.capita
## 0.60
## Renewable.energy.share.in.the.total.final.energy.consumption....
## 0.84
## Electricity.from.fossil.fuels..TWh.
## 0.70
## Electricity.from.renewables..TWh.
## 0.71
## Low.carbon.electricity....electricity.
## 0.52
## Primary.energy.consumption.per.capita..kWh.person.
## 0.77
## Value_co2_emissions_kt_by_country
## 0.73
## gdp_growth
## 0.65
## gdp_per_capita
## 0.767. PCA
7.1 Hitung Eigenvalue
ev <- eigen(cor(data_refined2))
print(ev$values)## [1] 4.59920773 1.81191555 1.40553939 1.12602605 0.90426826 0.36024830
## [7] 0.28017301 0.16797447 0.12766003 0.12097501 0.096012217.2 Tentukan Jumlah Komponen (Kaiser Rule)
sum(ev$values > 1)## [1] 47.3 Jalankan PCA
pca_result <- prcomp(data_refined2)
summary(pca_result)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.1446 1.3461 1.1856 1.0611 0.95093 0.60021 0.52931
## Proportion of Variance 0.4181 0.1647 0.1278 0.1024 0.08221 0.03275 0.02547
## Cumulative Proportion 0.4181 0.5828 0.7106 0.8130 0.89518 0.92793 0.95340
## PC8 PC9 PC10 PC11
## Standard deviation 0.40985 0.35730 0.3478 0.30986
## Proportion of Variance 0.01527 0.01161 0.0110 0.00873
## Cumulative Proportion 0.96867 0.98027 0.9913 1.000007.4 Scree plot
plot(ev$values,
type = "b",
main = "Scree Plot",
ylab = "Eigenvalue",
xlab = "Component Number")
abline(h = 1, col = "red", lty = 2)
7.6 Biplot PCA
biplot(pca_result, scale=0)
7.7 Loading PCA (koefisien komponen)
pca_loadings <- pca_result$rotation
head(pca_loadings)## PC1
## Access.to.electricity....of.population. 0.38737074
## Access.to.clean.fuels.for.cooking 0.40721079
## Renewable.electricity.generating.capacity.per.capita 0.06361808
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.36186018
## Electricity.from.fossil.fuels..TWh. 0.33467694
## Electricity.from.renewables..TWh. 0.23947689
## PC2
## Access.to.electricity....of.population. -0.04338688
## Access.to.clean.fuels.for.cooking -0.06954139
## Renewable.electricity.generating.capacity.per.capita 0.44830885
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.29185180
## Electricity.from.fossil.fuels..TWh. 0.15838370
## Electricity.from.renewables..TWh. 0.53847818
## PC3
## Access.to.electricity....of.population. 0.2760126
## Access.to.clean.fuels.for.cooking 0.2738385
## Renewable.electricity.generating.capacity.per.capita 0.3905276
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.1104015
## Electricity.from.fossil.fuels..TWh. -0.4882333
## Electricity.from.renewables..TWh. -0.1479972
## PC4
## Access.to.electricity....of.population. -0.26435596
## Access.to.clean.fuels.for.cooking -0.12945435
## Renewable.electricity.generating.capacity.per.capita -0.46963532
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.29642441
## Electricity.from.fossil.fuels..TWh. -0.08886986
## Electricity.from.renewables..TWh. 0.12729308
## PC5
## Access.to.electricity....of.population. -0.006445551
## Access.to.clean.fuels.for.cooking -0.065599702
## Renewable.electricity.generating.capacity.per.capita 0.102307679
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.080296291
## Electricity.from.fossil.fuels..TWh. 0.171258616
## Electricity.from.renewables..TWh. 0.028663753
## PC6
## Access.to.electricity....of.population. -0.3172267
## Access.to.clean.fuels.for.cooking -0.1924430
## Renewable.electricity.generating.capacity.per.capita 0.6293242
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.2264497
## Electricity.from.fossil.fuels..TWh. 0.1675911
## Electricity.from.renewables..TWh. -0.3166441
## PC7
## Access.to.electricity....of.population. -0.10929019
## Access.to.clean.fuels.for.cooking 0.10695794
## Renewable.electricity.generating.capacity.per.capita -0.04946749
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.12492291
## Electricity.from.fossil.fuels..TWh. 0.15213472
## Electricity.from.renewables..TWh. -0.62492530
## PC8
## Access.to.electricity....of.population. -0.46581245
## Access.to.clean.fuels.for.cooking -0.18593297
## Renewable.electricity.generating.capacity.per.capita 0.06970722
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.71994761
## Electricity.from.fossil.fuels..TWh. -0.01755566
## Electricity.from.renewables..TWh. 0.07488047
## PC9
## Access.to.electricity....of.population. 0.23862059
## Access.to.clean.fuels.for.cooking 0.14813038
## Renewable.electricity.generating.capacity.per.capita -0.04816832
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.26642428
## Electricity.from.fossil.fuels..TWh. 0.05607712
## Electricity.from.renewables..TWh. -0.32004850
## PC10
## Access.to.electricity....of.population. -0.53105742
## Access.to.clean.fuels.for.cooking 0.77399127
## Renewable.electricity.generating.capacity.per.capita -0.02758679
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.14479650
## Electricity.from.fossil.fuels..TWh. 0.17289135
## Electricity.from.renewables..TWh. 0.06208677
## PC11
## Access.to.electricity....of.population. -0.18284636
## Access.to.clean.fuels.for.cooking 0.17100759
## Renewable.electricity.generating.capacity.per.capita 0.07070772
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.01478733
## Electricity.from.fossil.fuels..TWh. -0.70903510
## Electricity.from.renewables..TWh. 0.105241417.8 Split Sample Validation
set.seed(123)
index <- sample(1:nrow(data_refined2), nrow(data_refined2)/2)
data_half1 <- data_refined2[index, ]
data_half2 <- data_refined2[-index, ]
prcomp(data_half1)## Standard deviations (1, .., p=11):
## [1] 2.1566661 1.3484568 1.1748694 1.0401165 0.9446660 0.6058358 0.5310176
## [8] 0.4065966 0.3645758 0.3499810 0.3166225
##
## Rotation (n x k) = (11 x 11):
## PC1
## Access.to.electricity....of.population. -0.38055051
## Access.to.clean.fuels.for.cooking -0.40222333
## Renewable.electricity.generating.capacity.per.capita -0.05767614
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.36047519
## Electricity.from.fossil.fuels..TWh. -0.33612388
## Electricity.from.renewables..TWh. -0.23303952
## Low.carbon.electricity....electricity. 0.09517157
## Primary.energy.consumption.per.capita..kWh.person. -0.37634589
## Value_co2_emissions_kt_by_country -0.32544812
## gdp_growth 0.09961994
## gdp_per_capita -0.35539788
## PC2
## Access.to.electricity....of.population. -0.028953102
## Access.to.clean.fuels.for.cooking -0.067681347
## Renewable.electricity.generating.capacity.per.capita 0.456007720
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.284565479
## Electricity.from.fossil.fuels..TWh. 0.155291719
## Electricity.from.renewables..TWh. 0.543076758
## Low.carbon.electricity....electricity. 0.575282205
## Primary.energy.consumption.per.capita..kWh.person. -0.137789373
## Value_co2_emissions_kt_by_country 0.185020534
## gdp_growth 0.003082471
## gdp_per_capita -0.049344618
## PC3
## Access.to.electricity....of.population. -0.30268295
## Access.to.clean.fuels.for.cooking -0.29284983
## Renewable.electricity.generating.capacity.per.capita -0.41984207
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.15919543
## Electricity.from.fossil.fuels..TWh. 0.47697051
## Electricity.from.renewables..TWh. 0.15778935
## Low.carbon.electricity....electricity. -0.25090096
## Primary.energy.consumption.per.capita..kWh.person. -0.05279836
## Value_co2_emissions_kt_by_country 0.49168997
## gdp_growth 0.23877973
## gdp_per_capita -0.06402635
## PC4
## Access.to.electricity....of.population. 0.24563327
## Access.to.clean.fuels.for.cooking 0.11052102
## Renewable.electricity.generating.capacity.per.capita 0.41784636
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.28628179
## Electricity.from.fossil.fuels..TWh. 0.15234917
## Electricity.from.renewables..TWh. -0.09085917
## Low.carbon.electricity....electricity. -0.28342634
## Primary.energy.consumption.per.capita..kWh.person. -0.36342806
## Value_co2_emissions_kt_by_country 0.09928730
## gdp_growth 0.41224889
## gdp_per_capita -0.49720553
## PC5
## Access.to.electricity....of.population. -0.007041847
## Access.to.clean.fuels.for.cooking -0.074370044
## Renewable.electricity.generating.capacity.per.capita 0.069524703
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.056520370
## Electricity.from.fossil.fuels..TWh. 0.175458700
## Electricity.from.renewables..TWh. 0.042054336
## Low.carbon.electricity....electricity. -0.238804667
## Primary.energy.consumption.per.capita..kWh.person. -0.316609873
## Value_co2_emissions_kt_by_country 0.080440954
## gdp_growth -0.862592137
## gdp_per_capita -0.214549593
## PC6
## Access.to.electricity....of.population. 0.30675544
## Access.to.clean.fuels.for.cooking 0.21052095
## Renewable.electricity.generating.capacity.per.capita -0.64641109
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.20350911
## Electricity.from.fossil.fuels..TWh. -0.17303112
## Electricity.from.renewables..TWh. 0.31435217
## Low.carbon.electricity....electricity. 0.31556270
## Primary.energy.consumption.per.capita..kWh.person. -0.28223083
## Value_co2_emissions_kt_by_country -0.01952281
## gdp_growth 0.01130239
## gdp_per_capita -0.30629850
## PC7
## Access.to.electricity....of.population. -0.10001790
## Access.to.clean.fuels.for.cooking 0.09349039
## Renewable.electricity.generating.capacity.per.capita -0.02532537
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.12137570
## Electricity.from.fossil.fuels..TWh. 0.15232803
## Electricity.from.renewables..TWh. -0.62785025
## Low.carbon.electricity....electricity. 0.53630574
## Primary.energy.consumption.per.capita..kWh.person. 0.17842796
## Value_co2_emissions_kt_by_country 0.37727011
## gdp_growth -0.11201360
## gdp_per_capita -0.27229138
## PC8
## Access.to.electricity....of.population. -0.48706824
## Access.to.clean.fuels.for.cooking -0.11269682
## Renewable.electricity.generating.capacity.per.capita 0.04791123
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.69825134
## Electricity.from.fossil.fuels..TWh. 0.02201760
## Electricity.from.renewables..TWh. 0.06320726
## Low.carbon.electricity....electricity. 0.17671980
## Primary.energy.consumption.per.capita..kWh.person. -0.32325725
## Value_co2_emissions_kt_by_country -0.06703863
## gdp_growth 0.05075278
## gdp_per_capita 0.33606315
## PC9
## Access.to.electricity....of.population. 0.28699883
## Access.to.clean.fuels.for.cooking 0.12853955
## Renewable.electricity.generating.capacity.per.capita -0.04799134
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.29201918
## Electricity.from.fossil.fuels..TWh. 0.05491605
## Electricity.from.renewables..TWh. -0.32756415
## Low.carbon.electricity....electricity. 0.05846982
## Primary.energy.consumption.per.capita..kWh.person. -0.61629875
## Value_co2_emissions_kt_by_country 0.17738664
## gdp_growth 0.05237556
## gdp_per_capita 0.53456968
## PC10
## Access.to.electricity....of.population. -0.49379124
## Access.to.clean.fuels.for.cooking 0.79103339
## Renewable.electricity.generating.capacity.per.capita -0.02603383
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.22272541
## Electricity.from.fossil.fuels..TWh. 0.16885145
## Electricity.from.renewables..TWh. 0.03684986
## Low.carbon.electricity....electricity. -0.07617220
## Primary.energy.consumption.per.capita..kWh.person. -0.09057330
## Value_co2_emissions_kt_by_country -0.16974003
## gdp_growth 0.01495129
## gdp_per_capita -0.08510196
## PC11
## Access.to.electricity....of.population. 0.1865601826
## Access.to.clean.fuels.for.cooking -0.1487026184
## Renewable.electricity.generating.capacity.per.capita -0.0962741113
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.0005973434
## Electricity.from.fossil.fuels..TWh. 0.7044525325
## Electricity.from.renewables..TWh. -0.1008672851
## Low.carbon.electricity....electricity. 0.1789659845
## Primary.energy.consumption.per.capita..kWh.person. 0.0159600140
## Value_co2_emissions_kt_by_country -0.6279151495
## gdp_growth 0.0286162731
## gdp_per_capita -0.0025133204prcomp(data_half2)## Standard deviations (1, .., p=11):
## [1] 2.1332246 1.3441329 1.1992419 1.0794830 0.9569964 0.5937709 0.5260289
## [8] 0.4126890 0.3494165 0.3440745 0.3016099
##
## Rotation (n x k) = (11 x 11):
## PC1
## Access.to.electricity....of.population. 0.39417584
## Access.to.clean.fuels.for.cooking 0.41214844
## Renewable.electricity.generating.capacity.per.capita 0.07006988
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.36314373
## Electricity.from.fossil.fuels..TWh. 0.33312970
## Electricity.from.renewables..TWh. 0.24607527
## Low.carbon.electricity....electricity. -0.09107558
## Primary.energy.consumption.per.capita..kWh.person. 0.35243601
## Value_co2_emissions_kt_by_country 0.32698797
## gdp_growth -0.09792645
## gdp_per_capita 0.34268186
## PC2
## Access.to.electricity....of.population. -0.05887670
## Access.to.clean.fuels.for.cooking -0.07267003
## Renewable.electricity.generating.capacity.per.capita 0.43893582
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.29903827
## Electricity.from.fossil.fuels..TWh. 0.16406179
## Electricity.from.renewables..TWh. 0.53397298
## Low.carbon.electricity....electricity. 0.57295924
## Primary.energy.consumption.per.capita..kWh.person. -0.13745631
## Value_co2_emissions_kt_by_country 0.20968026
## gdp_growth 0.05870978
## gdp_per_capita -0.05031380
## PC3
## Access.to.electricity....of.population. 0.24573343
## Access.to.clean.fuels.for.cooking 0.25298207
## Renewable.electricity.generating.capacity.per.capita 0.35324951
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.05718224
## Electricity.from.fossil.fuels..TWh. -0.49151349
## Electricity.from.renewables..TWh. -0.12891998
## Low.carbon.electricity....electricity. 0.33885316
## Primary.energy.consumption.per.capita..kWh.person. 0.15310405
## Value_co2_emissions_kt_by_country -0.49478848
## gdp_growth -0.27366288
## gdp_per_capita 0.17715792
## PC4
## Access.to.electricity....of.population. -0.28192209
## Access.to.clean.fuels.for.cooking -0.14706079
## Renewable.electricity.generating.capacity.per.capita -0.51815438
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.29904074
## Electricity.from.fossil.fuels..TWh. -0.02278863
## Electricity.from.renewables..TWh. 0.16339005
## Low.carbon.electricity....electricity. 0.20965799
## Primary.energy.consumption.per.capita..kWh.person. 0.32053768
## Value_co2_emissions_kt_by_country -0.01142679
## gdp_growth -0.40564915
## gdp_per_capita 0.44987267
## PC5
## Access.to.electricity....of.population. -0.006782817
## Access.to.clean.fuels.for.cooking -0.057338506
## Renewable.electricity.generating.capacity.per.capita 0.138603071
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.101086177
## Electricity.from.fossil.fuels..TWh. 0.164574774
## Electricity.from.renewables..TWh. 0.013048191
## Low.carbon.electricity....electricity. -0.164596423
## Primary.energy.consumption.per.capita..kWh.person. -0.337643930
## Value_co2_emissions_kt_by_country 0.079521090
## gdp_growth -0.855882271
## gdp_per_capita -0.245007100
## PC6
## Access.to.electricity....of.population. -0.32647988
## Access.to.clean.fuels.for.cooking -0.17121597
## Renewable.electricity.generating.capacity.per.capita 0.60993869
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.25196080
## Electricity.from.fossil.fuels..TWh. 0.16180195
## Electricity.from.renewables..TWh. -0.31163311
## Low.carbon.electricity....electricity. -0.33099614
## Primary.energy.consumption.per.capita..kWh.person. 0.31710418
## Value_co2_emissions_kt_by_country 0.07235279
## gdp_growth -0.03068595
## gdp_per_capita 0.29833104
## PC7
## Access.to.electricity....of.population. -0.11742398
## Access.to.clean.fuels.for.cooking 0.12016809
## Renewable.electricity.generating.capacity.per.capita -0.06804686
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.12010013
## Electricity.from.fossil.fuels..TWh. 0.15561227
## Electricity.from.renewables..TWh. -0.62547268
## Low.carbon.electricity....electricity. 0.55412375
## Primary.energy.consumption.per.capita..kWh.person. 0.19044872
## Value_co2_emissions_kt_by_country 0.36438179
## gdp_growth -0.06292869
## gdp_per_capita -0.23922294
## PC8
## Access.to.electricity....of.population. 0.44791898
## Access.to.clean.fuels.for.cooking 0.24225112
## Renewable.electricity.generating.capacity.per.capita -0.09008141
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.72806613
## Electricity.from.fossil.fuels..TWh. 0.04887394
## Electricity.from.renewables..TWh. -0.07455414
## Low.carbon.electricity....electricity. -0.16379875
## Primary.energy.consumption.per.capita..kWh.person. 0.21901102
## Value_co2_emissions_kt_by_country 0.04052949
## gdp_growth -0.06672406
## gdp_per_capita -0.33712083
## PC9
## Access.to.electricity....of.population. 0.20577338
## Access.to.clean.fuels.for.cooking 0.15483117
## Renewable.electricity.generating.capacity.per.capita -0.04777312
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.24570755
## Electricity.from.fossil.fuels..TWh. 0.05498784
## Electricity.from.renewables..TWh. -0.31377571
## Low.carbon.electricity....electricity. 0.05708082
## Primary.energy.consumption.per.capita..kWh.person. -0.64727686
## Value_co2_emissions_kt_by_country 0.15726722
## gdp_growth 0.05416583
## gdp_per_capita 0.56538379
## PC10
## Access.to.electricity....of.population. 0.55360531
## Access.to.clean.fuels.for.cooking -0.75714803
## Renewable.electricity.generating.capacity.per.capita 0.02836237
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.08240530
## Electricity.from.fossil.fuels..TWh. -0.17089744
## Electricity.from.renewables..TWh. -0.08279453
## Low.carbon.electricity....electricity. 0.06103569
## Primary.energy.consumption.per.capita..kWh.person. 0.11340806
## Value_co2_emissions_kt_by_country 0.23043127
## gdp_growth -0.03064285
## gdp_per_capita 0.07725155
## PC11
## Access.to.electricity....of.population. -0.177249874
## Access.to.clean.fuels.for.cooking 0.190398295
## Renewable.electricity.generating.capacity.per.capita 0.045701579
## Renewable.energy.share.in.the.total.final.energy.consumption.... 0.025253615
## Electricity.from.fossil.fuels..TWh. -0.712674777
## Electricity.from.renewables..TWh. 0.107857229
## Low.carbon.electricity....electricity. -0.164705342
## Primary.energy.consumption.per.capita..kWh.person. -0.009253537
## Value_co2_emissions_kt_by_country 0.616733360
## gdp_growth -0.041950367
## gdp_per_capita -0.0270688738. Factor Analysis (FA)
8.1 Tentukan jumlah faktor optimal (Parallel Analysis)
fa.parallel(data_refined2, fa="fa")
## Parallel analysis suggests that the number of factors = 4 and the number of components = NA8.2 Ekstraksi Faktor dengan Rotasi Varimax
Misal parallel analysis merekomendasikan 4 faktor.
fa_result <- fa(data_refined2,
nfactors = 4,
rotate = "varimax",
fm = "pa") 8.3 Lihat loading faktor
print(fa_result$loadings, cutoff=0.4)##
## Loadings:
## PA1 PA3
## Access.to.electricity....of.population. 0.821
## Access.to.clean.fuels.for.cooking 0.763
## Renewable.electricity.generating.capacity.per.capita
## Renewable.energy.share.in.the.total.final.energy.consumption.... -0.803
## Electricity.from.fossil.fuels..TWh. 0.925
## Electricity.from.renewables..TWh. 0.615
## Low.carbon.electricity....electricity.
## Primary.energy.consumption.per.capita..kWh.person.
## Value_co2_emissions_kt_by_country 0.896
## gdp_growth
## gdp_per_capita
## PA4 PA2
## Access.to.electricity....of.population.
## Access.to.clean.fuels.for.cooking 0.504
## Renewable.electricity.generating.capacity.per.capita 0.610
## Renewable.energy.share.in.the.total.final.energy.consumption....
## Electricity.from.fossil.fuels..TWh.
## Electricity.from.renewables..TWh. 0.523
## Low.carbon.electricity....electricity. 0.778
## Primary.energy.consumption.per.capita..kWh.person. 0.799
## Value_co2_emissions_kt_by_country
## gdp_growth
## gdp_per_capita 0.908
##
## PA1 PA3 PA4 PA2
## SS loadings 2.300 2.279 2.082 1.368
## Proportion Var 0.209 0.207 0.189 0.124
## Cumulative Var 0.209 0.416 0.606 0.7308.4 Menampilkan Communality (h2)
communalities <- fa_result$communality
kable(data.frame(Communality = round(communalities, 3)), caption = "Tabel Nilai Communality (h2)")| Communality | |
|---|---|
| Access.to.electricity….of.population. | 0.867 |
| Access.to.clean.fuels.for.cooking | 0.889 |
| Renewable.electricity.generating.capacity.per.capita | 0.482 |
| Renewable.energy.share.in.the.total.final.energy.consumption…. | 0.831 |
| Electricity.from.fossil.fuels..TWh. | 0.930 |
| Electricity.from.renewables..TWh. | 0.709 |
| Low.carbon.electricity….electricity. | 0.717 |
| Primary.energy.consumption.per.capita..kWh.person. | 0.790 |
| Value_co2_emissions_kt_by_country | 0.860 |
| gdp_growth | 0.049 |
| gdp_per_capita | 0.905 |
fa_uniqueness <- fa_result$uniquenesses8.5 Analisis Cross-Loading (Rasio)
loadings_matrix <- fa_result$loadings[,]
cross_loading <- apply(loadings_matrix, 1, function(x) {
sorted <- sort(abs(x), decreasing = TRUE)
if(length(sorted) > 1) {
ratio <- sorted[1] / sorted[2]
} else {
ratio <- NA
}
return(ratio)
})
cross_loading## Access.to.electricity....of.population.
## 2.283420
## Access.to.clean.fuels.for.cooking
## 1.512528
## Renewable.electricity.generating.capacity.per.capita
## 2.035374
## Renewable.energy.share.in.the.total.final.energy.consumption....
## 2.879764
## Electricity.from.fossil.fuels..TWh.
## 4.014862
## Electricity.from.renewables..TWh.
## 1.175362
## Low.carbon.electricity....electricity.
## 2.469767
## Primary.energy.consumption.per.capita..kWh.person.
## 2.699945
## Value_co2_emissions_kt_by_country
## 4.993234
## gdp_growth
## 2.050058
## gdp_per_capita
## 3.9124428.6 Total Variance Explained (Vaccounted)
fa_variance <- fa_result$Vaccounted
print(fa_variance)## PA1 PA3 PA4 PA2
## SS loadings 2.2996271 2.2792031 2.0821590 1.3682236
## Proportion Var 0.2090570 0.2072003 0.1892872 0.1243840
## Cumulative Var 0.2090570 0.4162573 0.6055445 0.7299284
## Proportion Explained 0.2864075 0.2838638 0.2593229 0.1704057
## Cumulative Proportion 0.2864075 0.5702714 0.8295943 1.00000008.7 Visualisasi FA
corrplot(fa_result$loadings[,], is.corr = FALSE, tl.cex = 0.7, main = "Factor Loadings Heatmap")