# Memuat library yang dibutuhkan
library(readxl)
library(car)
## Loading required package: carData
library(stats)
library(psych)
##
## Attaching package: 'psych'
## The following object is masked from 'package:car':
##
## logit
library(ggplot2)
##
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
##
## %+%, alpha
library(dplyr)
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:car':
##
## recode
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(reshape2)
library(MASS)
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
library(lmtest)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(pROC)
## Type 'citation("pROC")' for a citation.
##
## Attaching package: 'pROC'
## The following objects are masked from 'package:stats':
##
## cov, smooth, var
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:MASS':
##
## select
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
# Memuat data dari file excel
file_path <- "/home/taufiq/Documents/freelancer/kak qardawi/project baru/data/data.xlsx"
data <- read_excel(file_path, sheet = "Sheet1")
# Mengganti tanda kurung dalam nama kolom
colnames(data) <- gsub("\\(|\\)", "_", colnames(data))
# Memisahkan data berdasarkan jenis kelamin
data_perempuan <- data %>% filter(Jenis_Kelamin == "Perempuan")
data_laki_laki <- data %>% filter(Jenis_Kelamin == "Laki-laki")
# Tentukan variabel dependen dan independen
dependent_var <- "Usia" # Ganti dengan variabel dependen yang sesuai
# Fungsi untuk menghapus outlier
remove_outliers <- function(df, var) {
df <- df %>% mutate_at(vars(var), as.numeric)
Q1 <- quantile(df[[var]], 0.25, na.rm = TRUE)
Q3 <- quantile(df[[var]], 0.75, na.rm = TRUE)
IQR <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR
upper_bound <- Q3 + 1.5 * IQR
df <- df %>% filter(df[[var]] >= lower_bound & df[[var]] <= upper_bound)
return(df)
}
# Menghapus outlier dari data
clean_data_perempuan <- data_perempuan
clean_data_laki_laki <- data_laki_laki
desc_vars_perempuan <- colnames(data_perempuan)[3:ncol(data_perempuan)]
desc_vars_laki_laki <- colnames(data_laki_laki)[3:ncol(data_laki_laki)]
for (var in desc_vars_perempuan) {
clean_data_perempuan <- remove_outliers(clean_data_perempuan, var)
}
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(var)
##
## # Now:
## data %>% select(all_of(var))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
for (var in desc_vars_laki_laki) {
clean_data_laki_laki <- remove_outliers(clean_data_laki_laki, var)
}
# Memastikan semua variabel independen adalah numerik
numeric_data_perempuan <- clean_data_perempuan %>% mutate_at(vars(desc_vars_perempuan), as.numeric)
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(desc_vars_perempuan)
##
## # Now:
## data %>% select(all_of(desc_vars_perempuan))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
numeric_data_laki_laki <- clean_data_laki_laki %>% mutate_at(vars(desc_vars_laki_laki), as.numeric)
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(desc_vars_laki_laki)
##
## # Now:
## data %>% select(all_of(desc_vars_laki_laki))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Melakukan uji ANOVA dan Kruskal-Wallis
perform_tests <- function(dependent_var, independent_var, data) {
shapiro_test <- shapiro.test(data[[dependent_var]])
cat("\nShapiro-Wilk normality test for", dependent_var, "\n")
print(shapiro_test)
if (shapiro_test$p.value > 0.05) {
homogeneity_test <- bartlett.test(as.formula(paste(dependent_var, "~", independent_var)), data = data)
cat("\nBartlett test for homogeneity of variances for", dependent_var, "and", independent_var, "\n")
} else {
# Menambahkan pengecekan untuk menghindari error
if (length(unique(data[[independent_var]])) > 1) {
homogeneity_test <- fligner.test(as.formula(paste(dependent_var, "~", independent_var)), data = data)
cat("\nFligner-Killeen test for homogeneity of variances for", dependent_var, "and", independent_var, "\n")
} else {
cat("\nFligner-Killeen test cannot be performed because all observations are in the same group for", independent_var, "\n")
homogeneity_test <- list(p.value = NA)
}
}
print(homogeneity_test)
result <- list()
if (shapiro_test$p.value > 0.05 & homogeneity_test$p.value > 0.05) {
anova_result <- aov(as.formula(paste(dependent_var, "~", independent_var)), data = data)
result$anova <- summary(anova_result)
cat("\nANOVA result for", dependent_var, "and", independent_var, "\n")
print(result$anova)
} else {
# Menambahkan pengecekan untuk menghindari error
if (length(unique(data[[independent_var]])) > 1) {
kruskal_test <- kruskal.test(as.formula(paste(dependent_var, "~", independent_var)), data = data)
result$kruskal <- kruskal_test
cat("\nKruskal-Wallis test result for", dependent_var, "and", independent_var, "\n")
print(result$kruskal)
} else {
cat("\nKruskal-Wallis test cannot be performed because all observations are in the same group for", independent_var, "\n")
result$kruskal <- list(p.value = NA)
}
}
return(result)
}
results_perempuan <- list()
for (independent_var in colnames(numeric_data_perempuan[, -1])) {
results_perempuan[[independent_var]] <- perform_tests(dependent_var, independent_var, numeric_data_perempuan)
}
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test cannot be performed because all observations are in the same group for Jenis_Kelamin
## $p.value
## [1] NA
##
##
## Kruskal-Wallis test cannot be performed because all observations are in the same group for Jenis_Kelamin
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and BB1
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by BB1
## Fligner-Killeen:med chi-squared = 55.229, df = 55, p-value = 0.466
##
##
## Kruskal-Wallis test result for Usia and BB1
##
## Kruskal-Wallis rank sum test
##
## data: Usia by BB1
## Kruskal-Wallis chi-squared = 58.673, df = 55, p-value = 0.3424
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and BB2
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by BB2
## Fligner-Killeen:med chi-squared = 59.908, df = 56, p-value = 0.3359
##
##
## Kruskal-Wallis test result for Usia and BB2
##
## Kruskal-Wallis rank sum test
##
## data: Usia by BB2
## Kruskal-Wallis chi-squared = 58.781, df = 56, p-value = 0.374
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and BB3
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by BB3
## Fligner-Killeen:med chi-squared = 61.837, df = 55, p-value = 0.2452
##
##
## Kruskal-Wallis test result for Usia and BB3
##
## Kruskal-Wallis rank sum test
##
## data: Usia by BB3
## Kruskal-Wallis chi-squared = 59.158, df = 55, p-value = 0.3263
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MA_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MA_D_
## Fligner-Killeen:med chi-squared = 42.405, df = 57, p-value = 0.9252
##
##
## Kruskal-Wallis test result for Usia and MA_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MA_D_
## Kruskal-Wallis chi-squared = 59.061, df = 57, p-value = 0.4001
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MA_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MA_S_
## Fligner-Killeen:med chi-squared = 65.491, df = 54, p-value = 0.1359
##
##
## Kruskal-Wallis test result for Usia and MA_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MA_S_
## Kruskal-Wallis chi-squared = 58.426, df = 54, p-value = 0.3161
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and RL_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by RL_D_
## Fligner-Killeen:med chi-squared = 50.201, df = 53, p-value = 0.5838
##
##
## Kruskal-Wallis test result for Usia and RL_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RL_D_
## Kruskal-Wallis chi-squared = 56.283, df = 53, p-value = 0.3531
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and RL_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by RL_S_
## Fligner-Killeen:med chi-squared = 52.624, df = 52, p-value = 0.4498
##
##
## Kruskal-Wallis test result for Usia and RL_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RL_S_
## Kruskal-Wallis chi-squared = 56.177, df = 52, p-value = 0.3213
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and CRH_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by CRH_D_
## Fligner-Killeen:med chi-squared = 66, df = 51, p-value = 0.07708
##
##
## Kruskal-Wallis test result for Usia and CRH_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CRH_D_
## Kruskal-Wallis chi-squared = 61.399, df = 51, p-value = 0.151
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and CRH_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by CRH_S_
## Fligner-Killeen:med chi-squared = 45.637, df = 50, p-value = 0.649
##
##
## Kruskal-Wallis test result for Usia and CRH_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CRH_S_
## Kruskal-Wallis chi-squared = 57.118, df = 50, p-value = 0.2277
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and CL_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by CL_D_
## Fligner-Killeen:med chi-squared = 51.237, df = 51, p-value = 0.4644
##
##
## Kruskal-Wallis test result for Usia and CL_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CL_D_
## Kruskal-Wallis chi-squared = 51.742, df = 51, p-value = 0.4447
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and CL_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by CL_S_
## Fligner-Killeen:med chi-squared = 41.689, df = 52, p-value = 0.8462
##
##
## Kruskal-Wallis test result for Usia and CL_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CL_S_
## Kruskal-Wallis chi-squared = 57.59, df = 52, p-value = 0.2761
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and RH_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by RH_D_
## Fligner-Killeen:med chi-squared = 49.936, df = 51, p-value = 0.5159
##
##
## Kruskal-Wallis test result for Usia and RH_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RH_D_
## Kruskal-Wallis chi-squared = 62.379, df = 51, p-value = 0.1319
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and RH_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by RH_S_
## Fligner-Killeen:med chi-squared = 62.066, df = 54, p-value = 0.2107
##
##
## Kruskal-Wallis test result for Usia and RH_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RH_S_
## Kruskal-Wallis chi-squared = 53.59, df = 54, p-value = 0.4902
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and ML_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by ML_D_
## Fligner-Killeen:med chi-squared = 66, df = 52, p-value = 0.09175
##
##
## Kruskal-Wallis test result for Usia and ML_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by ML_D_
## Kruskal-Wallis chi-squared = 54.373, df = 52, p-value = 0.3842
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and ML_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by ML_S_
## Fligner-Killeen:med chi-squared = 51.942, df = 46, p-value = 0.2535
##
##
## Kruskal-Wallis test result for Usia and ML_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by ML_S_
## Kruskal-Wallis chi-squared = 60.082, df = 46, p-value = 0.0795
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MRH_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MRH_D_
## Fligner-Killeen:med chi-squared = 55.483, df = 55, p-value = 0.4564
##
##
## Kruskal-Wallis test result for Usia and MRH_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRH_D_
## Kruskal-Wallis chi-squared = 56.181, df = 55, p-value = 0.4304
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MRH_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MRH_S_
## Fligner-Killeen:med chi-squared = 48.331, df = 50, p-value = 0.5406
##
##
## Kruskal-Wallis test result for Usia and MRH_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRH_S_
## Kruskal-Wallis chi-squared = 46.985, df = 50, p-value = 0.5951
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MRB_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MRB_D_
## Fligner-Killeen:med chi-squared = 53.316, df = 47, p-value = 0.2443
##
##
## Kruskal-Wallis test result for Usia and MRB_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRB_D_
## Kruskal-Wallis chi-squared = 47.713, df = 47, p-value = 0.4436
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MRB_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MRB_S_
## Fligner-Killeen:med chi-squared = 36.404, df = 46, p-value = 0.8435
##
##
## Kruskal-Wallis test result for Usia and MRB_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRB_S_
## Kruskal-Wallis chi-squared = 45.049, df = 46, p-value = 0.512
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MrB_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MrB_D_
## Fligner-Killeen:med chi-squared = 47.313, df = 42, p-value = 0.2647
##
##
## Kruskal-Wallis test result for Usia and MrB_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MrB_D_
## Kruskal-Wallis chi-squared = 43.414, df = 42, p-value = 0.4109
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MrB_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MrB_S_
## Fligner-Killeen:med chi-squared = 53.494, df = 44, p-value = 0.1545
##
##
## Kruskal-Wallis test result for Usia and MrB_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MrB_S_
## Kruskal-Wallis chi-squared = 47.809, df = 44, p-value = 0.3208
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MBH_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MBH_D_
## Fligner-Killeen:med chi-squared = 47.246, df = 51, p-value = 0.6235
##
##
## Kruskal-Wallis test result for Usia and MBH_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MBH_D_
## Kruskal-Wallis chi-squared = 56.842, df = 51, p-value = 0.2666
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.88676, p-value = 1.777e-05
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MBH_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MBH_S_
## Fligner-Killeen:med chi-squared = 47.662, df = 48, p-value = 0.4866
##
##
## Kruskal-Wallis test result for Usia and MBH_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MBH_S_
## Kruskal-Wallis chi-squared = 56.854, df = 48, p-value = 0.1786
for (independent_var in names(results_perempuan)) {
cat("\nHasil uji untuk variabel independen (Perempuan):", independent_var, "\n")
print(results_perempuan[[independent_var]])
}
##
## Hasil uji untuk variabel independen (Perempuan): Jenis_Kelamin
## $kruskal
## $kruskal$p.value
## [1] NA
##
##
##
## Hasil uji untuk variabel independen (Perempuan): BB1
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by BB1
## Kruskal-Wallis chi-squared = 58.673, df = 55, p-value = 0.3424
##
##
##
## Hasil uji untuk variabel independen (Perempuan): BB2
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by BB2
## Kruskal-Wallis chi-squared = 58.781, df = 56, p-value = 0.374
##
##
##
## Hasil uji untuk variabel independen (Perempuan): BB3
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by BB3
## Kruskal-Wallis chi-squared = 59.158, df = 55, p-value = 0.3263
##
##
##
## Hasil uji untuk variabel independen (Perempuan): MA_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MA_D_
## Kruskal-Wallis chi-squared = 59.061, df = 57, p-value = 0.4001
##
##
##
## Hasil uji untuk variabel independen (Perempuan): MA_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MA_S_
## Kruskal-Wallis chi-squared = 58.426, df = 54, p-value = 0.3161
##
##
##
## Hasil uji untuk variabel independen (Perempuan): RL_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RL_D_
## Kruskal-Wallis chi-squared = 56.283, df = 53, p-value = 0.3531
##
##
##
## Hasil uji untuk variabel independen (Perempuan): RL_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RL_S_
## Kruskal-Wallis chi-squared = 56.177, df = 52, p-value = 0.3213
##
##
##
## Hasil uji untuk variabel independen (Perempuan): CRH_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CRH_D_
## Kruskal-Wallis chi-squared = 61.399, df = 51, p-value = 0.151
##
##
##
## Hasil uji untuk variabel independen (Perempuan): CRH_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CRH_S_
## Kruskal-Wallis chi-squared = 57.118, df = 50, p-value = 0.2277
##
##
##
## Hasil uji untuk variabel independen (Perempuan): CL_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CL_D_
## Kruskal-Wallis chi-squared = 51.742, df = 51, p-value = 0.4447
##
##
##
## Hasil uji untuk variabel independen (Perempuan): CL_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CL_S_
## Kruskal-Wallis chi-squared = 57.59, df = 52, p-value = 0.2761
##
##
##
## Hasil uji untuk variabel independen (Perempuan): RH_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RH_D_
## Kruskal-Wallis chi-squared = 62.379, df = 51, p-value = 0.1319
##
##
##
## Hasil uji untuk variabel independen (Perempuan): RH_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RH_S_
## Kruskal-Wallis chi-squared = 53.59, df = 54, p-value = 0.4902
##
##
##
## Hasil uji untuk variabel independen (Perempuan): ML_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by ML_D_
## Kruskal-Wallis chi-squared = 54.373, df = 52, p-value = 0.3842
##
##
##
## Hasil uji untuk variabel independen (Perempuan): ML_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by ML_S_
## Kruskal-Wallis chi-squared = 60.082, df = 46, p-value = 0.0795
##
##
##
## Hasil uji untuk variabel independen (Perempuan): MRH_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRH_D_
## Kruskal-Wallis chi-squared = 56.181, df = 55, p-value = 0.4304
##
##
##
## Hasil uji untuk variabel independen (Perempuan): MRH_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRH_S_
## Kruskal-Wallis chi-squared = 46.985, df = 50, p-value = 0.5951
##
##
##
## Hasil uji untuk variabel independen (Perempuan): MRB_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRB_D_
## Kruskal-Wallis chi-squared = 47.713, df = 47, p-value = 0.4436
##
##
##
## Hasil uji untuk variabel independen (Perempuan): MRB_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRB_S_
## Kruskal-Wallis chi-squared = 45.049, df = 46, p-value = 0.512
##
##
##
## Hasil uji untuk variabel independen (Perempuan): MrB_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MrB_D_
## Kruskal-Wallis chi-squared = 43.414, df = 42, p-value = 0.4109
##
##
##
## Hasil uji untuk variabel independen (Perempuan): MrB_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MrB_S_
## Kruskal-Wallis chi-squared = 47.809, df = 44, p-value = 0.3208
##
##
##
## Hasil uji untuk variabel independen (Perempuan): MBH_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MBH_D_
## Kruskal-Wallis chi-squared = 56.842, df = 51, p-value = 0.2666
##
##
##
## Hasil uji untuk variabel independen (Perempuan): MBH_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MBH_S_
## Kruskal-Wallis chi-squared = 56.854, df = 48, p-value = 0.1786
results_laki_laki <- list()
for (independent_var in colnames(numeric_data_laki_laki[, -1])) {
results_laki_laki[[independent_var]] <- perform_tests(dependent_var, independent_var, numeric_data_laki_laki)
}
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test cannot be performed because all observations are in the same group for Jenis_Kelamin
## $p.value
## [1] NA
##
##
## Kruskal-Wallis test cannot be performed because all observations are in the same group for Jenis_Kelamin
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and BB1
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by BB1
## Fligner-Killeen:med chi-squared = 89.101, df = 80, p-value = 0.2278
##
##
## Kruskal-Wallis test result for Usia and BB1
##
## Kruskal-Wallis rank sum test
##
## data: Usia by BB1
## Kruskal-Wallis chi-squared = 83.912, df = 80, p-value = 0.3606
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and BB2
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by BB2
## Fligner-Killeen:med chi-squared = 76.408, df = 73, p-value = 0.3697
##
##
## Kruskal-Wallis test result for Usia and BB2
##
## Kruskal-Wallis rank sum test
##
## data: Usia by BB2
## Kruskal-Wallis chi-squared = 84.75, df = 73, p-value = 0.1638
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and BB3
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by BB3
## Fligner-Killeen:med chi-squared = 69.929, df = 70, p-value = 0.4799
##
##
## Kruskal-Wallis test result for Usia and BB3
##
## Kruskal-Wallis rank sum test
##
## data: Usia by BB3
## Kruskal-Wallis chi-squared = 70.269, df = 70, p-value = 0.4685
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MA_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MA_D_
## Fligner-Killeen:med chi-squared = 84.836, df = 77, p-value = 0.2532
##
##
## Kruskal-Wallis test result for Usia and MA_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MA_D_
## Kruskal-Wallis chi-squared = 91.152, df = 77, p-value = 0.1292
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MA_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MA_S_
## Fligner-Killeen:med chi-squared = 79.333, df = 82, p-value = 0.5629
##
##
## Kruskal-Wallis test result for Usia and MA_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MA_S_
## Kruskal-Wallis chi-squared = 82.097, df = 82, p-value = 0.4762
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and RL_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by RL_D_
## Fligner-Killeen:med chi-squared = 91.3, df = 78, p-value = 0.144
##
##
## Kruskal-Wallis test result for Usia and RL_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RL_D_
## Kruskal-Wallis chi-squared = 81.639, df = 78, p-value = 0.3669
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and RL_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by RL_S_
## Fligner-Killeen:med chi-squared = 74.594, df = 72, p-value = 0.394
##
##
## Kruskal-Wallis test result for Usia and RL_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RL_S_
## Kruskal-Wallis chi-squared = 65.796, df = 72, p-value = 0.6832
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and CRH_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by CRH_D_
## Fligner-Killeen:med chi-squared = 72.147, df = 69, p-value = 0.3743
##
##
## Kruskal-Wallis test result for Usia and CRH_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CRH_D_
## Kruskal-Wallis chi-squared = 76.493, df = 69, p-value = 0.2507
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and CRH_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by CRH_S_
## Fligner-Killeen:med chi-squared = 71.979, df = 74, p-value = 0.5449
##
##
## Kruskal-Wallis test result for Usia and CRH_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CRH_S_
## Kruskal-Wallis chi-squared = 86.173, df = 74, p-value = 0.1575
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and CL_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by CL_D_
## Fligner-Killeen:med chi-squared = 80.079, df = 76, p-value = 0.3524
##
##
## Kruskal-Wallis test result for Usia and CL_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CL_D_
## Kruskal-Wallis chi-squared = 76.319, df = 76, p-value = 0.4682
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and CL_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by CL_S_
## Fligner-Killeen:med chi-squared = 68.992, df = 66, p-value = 0.3766
##
##
## Kruskal-Wallis test result for Usia and CL_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CL_S_
## Kruskal-Wallis chi-squared = 75.061, df = 66, p-value = 0.2082
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and RH_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by RH_D_
## Fligner-Killeen:med chi-squared = 85.541, df = 81, p-value = 0.3437
##
##
## Kruskal-Wallis test result for Usia and RH_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RH_D_
## Kruskal-Wallis chi-squared = 84.008, df = 81, p-value = 0.3876
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and RH_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by RH_S_
## Fligner-Killeen:med chi-squared = 89.846, df = 81, p-value = 0.2348
##
##
## Kruskal-Wallis test result for Usia and RH_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RH_S_
## Kruskal-Wallis chi-squared = 82.967, df = 81, p-value = 0.4186
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and ML_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by ML_D_
## Fligner-Killeen:med chi-squared = 69.161, df = 71, p-value = 0.5397
##
##
## Kruskal-Wallis test result for Usia and ML_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by ML_D_
## Kruskal-Wallis chi-squared = 73.871, df = 71, p-value = 0.3845
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and ML_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by ML_S_
## Fligner-Killeen:med chi-squared = 74.519, df = 72, p-value = 0.3963
##
##
## Kruskal-Wallis test result for Usia and ML_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by ML_S_
## Kruskal-Wallis chi-squared = 72.455, df = 72, p-value = 0.4628
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MRH_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MRH_D_
## Fligner-Killeen:med chi-squared = 96.085, df = 77, p-value = 0.06954
##
##
## Kruskal-Wallis test result for Usia and MRH_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRH_D_
## Kruskal-Wallis chi-squared = 85.441, df = 77, p-value = 0.2388
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MRH_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MRH_S_
## Fligner-Killeen:med chi-squared = 85.516, df = 76, p-value = 0.2132
##
##
## Kruskal-Wallis test result for Usia and MRH_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRH_S_
## Kruskal-Wallis chi-squared = 86.374, df = 76, p-value = 0.195
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MRB_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MRB_D_
## Fligner-Killeen:med chi-squared = 80.16, df = 64, p-value = 0.08363
##
##
## Kruskal-Wallis test result for Usia and MRB_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRB_D_
## Kruskal-Wallis chi-squared = 64.37, df = 64, p-value = 0.4635
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MRB_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MRB_S_
## Fligner-Killeen:med chi-squared = 64.387, df = 67, p-value = 0.5678
##
##
## Kruskal-Wallis test result for Usia and MRB_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRB_S_
## Kruskal-Wallis chi-squared = 76.104, df = 67, p-value = 0.2088
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MrB_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MrB_D_
## Fligner-Killeen:med chi-squared = 71.689, df = 66, p-value = 0.2949
##
##
## Kruskal-Wallis test result for Usia and MrB_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MrB_D_
## Kruskal-Wallis chi-squared = 67.843, df = 66, p-value = 0.4142
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MrB_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MrB_S_
## Fligner-Killeen:med chi-squared = 75.207, df = 61, p-value = 0.1043
##
##
## Kruskal-Wallis test result for Usia and MrB_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MrB_S_
## Kruskal-Wallis chi-squared = 76.103, df = 61, p-value = 0.09214
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MBH_D_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MBH_D_
## Fligner-Killeen:med chi-squared = 72.04, df = 56, p-value = 0.07314
##
##
## Kruskal-Wallis test result for Usia and MBH_D_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MBH_D_
## Kruskal-Wallis chi-squared = 78.762, df = 56, p-value = 0.02415
##
##
## Shapiro-Wilk normality test for Usia
##
## Shapiro-Wilk normality test
##
## data: data[[dependent_var]]
## W = 0.94666, p-value = 0.0005412
##
##
## Fligner-Killeen test for homogeneity of variances for Usia and MBH_S_
##
## Fligner-Killeen test of homogeneity of variances
##
## data: Usia by MBH_S_
## Fligner-Killeen:med chi-squared = 58.577, df = 59, p-value = 0.4911
##
##
## Kruskal-Wallis test result for Usia and MBH_S_
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MBH_S_
## Kruskal-Wallis chi-squared = 86.676, df = 59, p-value = 0.01097
for (independent_var in names(results_laki_laki)) {
cat("\nHasil uji untuk variabel independen (Laki-laki):", independent_var, "\n")
print(results_laki_laki[[independent_var]])
}
##
## Hasil uji untuk variabel independen (Laki-laki): Jenis_Kelamin
## $kruskal
## $kruskal$p.value
## [1] NA
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): BB1
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by BB1
## Kruskal-Wallis chi-squared = 83.912, df = 80, p-value = 0.3606
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): BB2
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by BB2
## Kruskal-Wallis chi-squared = 84.75, df = 73, p-value = 0.1638
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): BB3
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by BB3
## Kruskal-Wallis chi-squared = 70.269, df = 70, p-value = 0.4685
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): MA_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MA_D_
## Kruskal-Wallis chi-squared = 91.152, df = 77, p-value = 0.1292
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): MA_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MA_S_
## Kruskal-Wallis chi-squared = 82.097, df = 82, p-value = 0.4762
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): RL_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RL_D_
## Kruskal-Wallis chi-squared = 81.639, df = 78, p-value = 0.3669
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): RL_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RL_S_
## Kruskal-Wallis chi-squared = 65.796, df = 72, p-value = 0.6832
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): CRH_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CRH_D_
## Kruskal-Wallis chi-squared = 76.493, df = 69, p-value = 0.2507
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): CRH_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CRH_S_
## Kruskal-Wallis chi-squared = 86.173, df = 74, p-value = 0.1575
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): CL_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CL_D_
## Kruskal-Wallis chi-squared = 76.319, df = 76, p-value = 0.4682
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): CL_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by CL_S_
## Kruskal-Wallis chi-squared = 75.061, df = 66, p-value = 0.2082
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): RH_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RH_D_
## Kruskal-Wallis chi-squared = 84.008, df = 81, p-value = 0.3876
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): RH_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by RH_S_
## Kruskal-Wallis chi-squared = 82.967, df = 81, p-value = 0.4186
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): ML_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by ML_D_
## Kruskal-Wallis chi-squared = 73.871, df = 71, p-value = 0.3845
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): ML_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by ML_S_
## Kruskal-Wallis chi-squared = 72.455, df = 72, p-value = 0.4628
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): MRH_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRH_D_
## Kruskal-Wallis chi-squared = 85.441, df = 77, p-value = 0.2388
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): MRH_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRH_S_
## Kruskal-Wallis chi-squared = 86.374, df = 76, p-value = 0.195
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): MRB_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRB_D_
## Kruskal-Wallis chi-squared = 64.37, df = 64, p-value = 0.4635
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): MRB_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MRB_S_
## Kruskal-Wallis chi-squared = 76.104, df = 67, p-value = 0.2088
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): MrB_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MrB_D_
## Kruskal-Wallis chi-squared = 67.843, df = 66, p-value = 0.4142
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): MrB_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MrB_S_
## Kruskal-Wallis chi-squared = 76.103, df = 61, p-value = 0.09214
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): MBH_D_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MBH_D_
## Kruskal-Wallis chi-squared = 78.762, df = 56, p-value = 0.02415
##
##
##
## Hasil uji untuk variabel independen (Laki-laki): MBH_S_
## $kruskal
##
## Kruskal-Wallis rank sum test
##
## data: Usia by MBH_S_
## Kruskal-Wallis chi-squared = 86.676, df = 59, p-value = 0.01097
# Uji multikolinearitas sebelum DFA
multicollinearity_test <- function(data, dependent_var, independent_vars) {
formula <- as.formula(paste(dependent_var, "~", paste(independent_vars, collapse = "+")))
model <- lm(formula, data = data)
vif_values <- vif(model)
return(vif_values)
}
vif_results_perempuan_before <- multicollinearity_test(clean_data_perempuan, dependent_var, desc_vars_perempuan)
cat("\nUji Multikolinearitas Sebelum DFA (Perempuan):\n")
##
## Uji Multikolinearitas Sebelum DFA (Perempuan):
print(vif_results_perempuan_before)
## BB1 BB2 BB3 MA_D_ MA_S_ RL_D_ RL_S_ CRH_D_
## 2.749794 1.757259 2.248114 12.150471 5.363303 27.939698 24.880387 34.856994
## CRH_S_ CL_D_ CL_S_ RH_D_ RH_S_ ML_D_ ML_S_ MRH_D_
## 19.409808 33.772597 23.405646 24.347881 13.567499 3.913501 3.602341 23.577601
## MRH_S_ MRB_D_ MRB_S_ MrB_D_ MrB_S_ MBH_D_ MBH_S_
## 23.310823 12.968071 13.448892 5.367834 3.734209 8.219257 8.392261
vif_results_laki_laki_before <- multicollinearity_test(clean_data_laki_laki, dependent_var, desc_vars_laki_laki)
cat("\nUji Multikolinearitas Sebelum DFA (Laki-laki):\n")
##
## Uji Multikolinearitas Sebelum DFA (Laki-laki):
print(vif_results_laki_laki_before)
## BB1 BB2 BB3 MA_D_ MA_S_ RL_D_ RL_S_ CRH_D_
## 2.323296 2.473788 1.955812 11.429444 11.888775 22.644991 17.317910 31.257623
## CRH_S_ CL_D_ CL_S_ RH_D_ RH_S_ ML_D_ ML_S_ MRH_D_
## 15.935203 20.031286 12.216042 29.563146 35.014505 7.400993 7.474817 21.297098
## MRH_S_ MRB_D_ MRB_S_ MrB_D_ MrB_S_ MBH_D_ MBH_S_
## 21.573966 19.586698 19.785924 18.620493 15.990789 7.105134 7.704028
# Melakukan DFA
d_data_perempuan <- clean_data_perempuan[, c(dependent_var, desc_vars_perempuan)]
d_data_perempuan[[dependent_var]] <- as.factor(d_data_perempuan[[dependent_var]])
d_data_laki_laki <- clean_data_laki_laki[, c(dependent_var, desc_vars_laki_laki)]
d_data_laki_laki[[dependent_var]] <- as.factor(d_data_laki_laki[[dependent_var]])
# DFA untuk Perempuan
discriminant_result_perempuan <- lda(as.formula(paste(dependent_var, "~", paste(desc_vars_perempuan, collapse = "+"))), data = d_data_perempuan)
print(discriminant_result_perempuan)
## Call:
## lda(as.formula(paste(dependent_var, "~", paste(desc_vars_perempuan,
## collapse = "+"))), data = d_data_perempuan)
##
## Prior probabilities of groups:
## 2 3 4 5 6 7 8
## 0.16417910 0.13432836 0.04477612 0.13432836 0.07462687 0.10447761 0.17910448
## 9
## 0.16417910
##
## Group means:
## BB1 BB2 BB3 MA_D_ MA_S_ RL_D_ RL_S_ CRH_D_
## 2 115.5273 93.20909 92.22727 128.2909 128.9818 50.57273 50.90909 55.04545
## 3 116.4111 94.85556 93.61111 127.4133 128.2333 52.53333 52.18889 56.92222
## 4 112.6333 94.63333 91.90000 124.6333 125.2667 52.76667 52.30000 55.36667
## 5 121.1111 96.56667 92.51111 122.6000 124.3111 55.23333 53.95556 60.11111
## 6 118.8800 98.56000 97.94000 127.1800 121.3200 55.64000 55.80000 60.06000
## 7 119.9857 95.14286 93.65714 127.4286 126.1714 51.31429 50.64286 57.17143
## 8 121.2550 89.42500 97.75000 125.5083 126.0250 55.75833 55.09167 59.25833
## 9 122.9909 97.07273 94.40909 126.7000 125.5818 55.95455 56.10000 59.98182
## CRH_S_ CL_D_ CL_S_ RH_D_ RH_S_ ML_D_ ML_S_ MRH_D_
## 2 54.80000 53.74545 53.64545 42.99091 43.68182 69.49091 70.17273 54.21818
## 3 55.85556 55.30000 54.22222 46.53333 46.41111 73.65556 74.32222 56.02222
## 4 55.93333 53.60000 53.93333 46.76667 47.50000 69.36667 71.36667 55.13333
## 5 58.75556 58.27778 56.87778 51.60000 49.94444 74.30000 74.28889 59.28889
## 6 61.00000 58.46000 58.46000 46.02000 45.32000 74.26000 74.76000 58.56000
## 7 57.52857 55.32857 55.32857 46.45714 46.37143 72.95714 72.67143 54.40000
## 8 59.16667 57.07500 57.34167 51.69167 51.17500 73.65000 74.37500 56.64167
## 9 60.24545 57.56364 58.07273 50.25455 50.55455 73.44545 72.25455 55.26364
## MRH_S_ MRB_D_ MRB_S_ MrB_D_ MrB_S_ MBH_D_ MBH_S_
## 2 54.13636 40.03636 40.41818 30.70000 31.10000 25.74545 25.60909
## 3 55.06667 40.15556 39.76667 31.10000 31.11111 27.44444 27.65556
## 4 54.80000 41.16667 41.03333 28.90000 28.46667 25.13333 25.36667
## 5 57.91111 39.84444 40.60000 31.13333 31.91111 28.61111 28.56667
## 6 59.06000 42.52000 42.62000 31.82000 31.84000 30.42000 30.78000
## 7 53.74286 40.40000 40.21429 29.55714 30.72857 28.31429 27.67143
## 8 56.00000 43.87500 43.99583 32.53333 32.60000 31.70000 31.84167
## 9 55.69091 42.72727 43.17273 32.54545 31.99091 27.86364 27.61818
##
## Coefficients of linear discriminants:
## LD1 LD2 LD3 LD4 LD5
## BB1 0.048252958 -0.11033251 0.073370220 0.05467813 0.15947232
## BB2 -0.271130896 -0.13431390 -0.001012248 0.15565472 -0.16567832
## BB3 0.286907547 0.02714287 -0.079499145 -0.08807346 -0.02803783
## MA_D_ -0.099666939 -0.15486544 0.009235343 0.33073345 -0.05422712
## MA_S_ 0.054873048 0.34824064 0.194810682 -0.12765451 -0.11120559
## RL_D_ -0.321125922 -0.30096853 0.009280327 0.10181007 0.14536346
## RL_S_ 0.059307797 0.10217863 0.084535298 -0.46133620 -0.22671486
## CRH_D_ 0.005392424 -0.04310700 -0.009082225 0.11169567 -0.41554021
## CRH_S_ 0.372948983 -0.29256290 -0.148309763 -0.19510206 0.35886228
## CL_D_ -0.185042862 -0.17053657 0.060464734 -0.04233007 0.39939499
## CL_S_ -0.193485505 0.30521120 0.242231536 0.08801486 -0.36065395
## RH_D_ 0.403732235 -0.06749937 -0.152337300 0.29716064 -0.24146278
## RH_S_ -0.040411425 0.28856806 0.323977936 0.15597104 0.03222985
## ML_D_ -0.070709618 0.05859510 0.162245998 0.07466444 0.09693937
## ML_S_ 0.071566635 0.16433502 -0.372364701 0.04098416 -0.30705118
## MRH_D_ -0.110511142 0.44265646 -0.060711406 0.21619499 0.13634172
## MRH_S_ -0.085230677 -0.33730358 -0.188606306 -0.18752253 -0.02689880
## MRB_D_ 0.634270333 0.04814806 -0.553044626 -0.08325347 -0.20803415
## MRB_S_ -0.563492201 -0.17963313 0.571971313 0.11809264 0.35632069
## MrB_D_ -0.552120915 0.14946326 0.477152019 -0.56076010 -0.38228459
## MrB_S_ 0.288346192 -0.13613503 -0.163731347 0.34447933 0.32315814
## MBH_D_ 0.117084489 0.11143967 0.150485813 0.01359475 0.36295112
## MBH_S_ 0.412720065 -0.22637016 -0.239399039 -0.10024113 -0.35604378
## LD6 LD7
## BB1 0.071043193 -0.044876509
## BB2 -0.103718051 -0.030357895
## BB3 -0.132531253 -0.042000085
## MA_D_ -0.044326540 -0.006469175
## MA_S_ 0.057135240 -0.076124703
## RL_D_ 0.373625287 -0.011028127
## RL_S_ -0.276730286 -0.010655602
## CRH_D_ -0.040429175 0.037397844
## CRH_S_ -0.255235929 0.017018270
## CL_D_ 0.266989025 -0.017504612
## CL_S_ 0.051243428 -0.070210534
## RH_D_ 0.003843719 0.211404286
## RH_S_ -0.219198077 -0.108379197
## ML_D_ 0.094559401 -0.188595877
## ML_S_ -0.067921670 0.131637319
## MRH_D_ -0.242096786 0.035568608
## MRH_S_ 0.378910531 0.054368892
## MRB_D_ -0.370595527 -0.300983658
## MRB_S_ 0.252627565 0.589545197
## MrB_D_ 0.298825681 -0.216619132
## MrB_S_ 0.034336986 -0.001410612
## MBH_D_ -0.096342035 -0.269174726
## MBH_S_ 0.179217109 0.084105544
##
## Proportion of trace:
## LD1 LD2 LD3 LD4 LD5 LD6 LD7
## 0.5127 0.1855 0.1387 0.0805 0.0356 0.0281 0.0189
d_scores_perempuan <- predict(discriminant_result_perempuan)$x
d_scores_perempuan <- as.data.frame(d_scores_perempuan)
colnames(d_scores_perempuan) <- paste0("LD", 1:ncol(d_scores_perempuan))
contributions_perempuan <- discriminant_result_perempuan$scaling
top_contributors_perempuan <- apply(contributions_perempuan, 2, function(x) desc_vars_perempuan[which.max(abs(x))])
contributions_df_perempuan <- data.frame(Discriminant = colnames(contributions_perempuan), Top_Contributor = top_contributors_perempuan)
print(contributions_df_perempuan)
## Discriminant Top_Contributor
## LD1 LD1 MRB_D_
## LD2 LD2 MRH_D_
## LD3 LD3 MRB_S_
## LD4 LD4 MrB_D_
## LD5 LD5 CRH_D_
## LD6 LD6 MRH_S_
## LD7 LD7 MRB_S_
# DFA untuk Laki-laki
discriminant_result_laki_laki <- lda(as.formula(paste(dependent_var, "~", paste(desc_vars_laki_laki, collapse = "+"))), data = d_data_laki_laki)
print(discriminant_result_laki_laki)
## Call:
## lda(as.formula(paste(dependent_var, "~", paste(desc_vars_laki_laki,
## collapse = "+"))), data = d_data_laki_laki)
##
## Prior probabilities of groups:
## 1 2 3 4 5 6 7
## 0.02020202 0.11111111 0.14141414 0.11111111 0.17171717 0.13131313 0.10101010
## 8 9
## 0.12121212 0.09090909
##
## Group means:
## BB1 BB2 BB3 MA_D_ MA_S_ RL_D_ RL_S_ CRH_D_
## 1 123.0000 96.85000 97.55000 126.3000 127.5500 53.50000 53.25000 56.75000
## 2 119.4182 97.64545 96.98182 127.2091 127.4545 53.33636 53.34545 59.59091
## 3 117.7429 93.95000 96.59286 123.8071 123.6071 58.21429 58.11429 62.22143
## 4 122.9091 99.10909 97.40909 127.5818 129.0818 58.53636 56.43636 63.30000
## 5 125.3118 97.65882 100.45882 125.6059 125.5765 59.88824 59.81176 62.62353
## 6 125.2615 100.06154 101.50000 128.2846 128.0462 58.95385 58.72308 65.54615
## 7 125.4100 99.06000 102.08000 123.1900 124.9600 60.03000 61.21000 65.77000
## 8 127.4050 101.05000 106.05000 126.3417 126.2000 62.00000 62.40000 63.55833
## 9 122.4667 100.01111 102.56667 127.6444 128.5333 62.01111 62.68889 65.78889
## CRH_S_ CL_D_ CL_S_ RH_D_ RH_S_ ML_D_ ML_S_ MRH_D_
## 1 57.60000 56.90000 55.20000 47.85000 47.80000 75.50000 76.40000 57.75000
## 2 59.37273 57.90909 57.35455 46.58182 46.94545 73.48182 73.85455 58.84545
## 3 61.75000 60.16429 59.95000 53.47857 53.22143 76.57143 76.97857 61.20714
## 4 62.01818 61.50909 60.51818 49.15455 47.75455 74.37273 73.98182 62.84545
## 5 62.35294 60.77059 59.78824 54.97647 55.17647 76.01176 75.87647 62.26471
## 6 64.31538 62.88462 62.85385 50.76154 50.63846 75.41538 75.20000 64.87692
## 7 65.23000 65.18000 64.10000 60.16000 59.99000 79.74000 79.78000 64.11000
## 8 63.30833 63.55833 63.30833 56.75833 57.70000 77.11667 76.62500 65.33333
## 9 65.44444 63.60000 63.31111 54.84444 55.46667 77.36667 77.72222 67.15556
## MRH_S_ MRB_D_ MRB_S_ MrB_D_ MrB_S_ MBH_D_ MBH_S_
## 1 57.10000 42.35000 43.45000 33.80000 34.60000 27.30000 27.90000
## 2 58.38182 41.79091 42.13636 33.48182 33.88182 27.12727 27.02727
## 3 61.13571 44.50714 44.33571 34.24286 34.53571 27.60000 27.75000
## 4 61.68182 41.99091 41.54545 31.13636 31.10909 30.40909 29.49091
## 5 62.30588 43.03529 43.31765 32.63529 33.16471 30.76471 30.60588
## 6 63.82308 42.52308 42.67692 31.26923 31.42308 30.76923 30.38462
## 7 64.32000 48.08000 48.55000 38.25000 38.42000 32.58000 32.78000
## 8 65.63333 45.60833 45.83333 33.47500 33.91667 32.94167 33.77500
## 9 66.54444 44.65556 44.81111 32.57778 32.72222 33.67778 33.33333
##
## Coefficients of linear discriminants:
## LD1 LD2 LD3 LD4 LD5
## BB1 0.005186536 -0.0031832549 -0.0228839580 -0.133115226 0.141994196
## BB2 0.020699007 -0.0322700462 0.0986382081 0.135963281 -0.023455822
## BB3 0.016716963 0.0107980147 0.0701985011 -0.052406052 -0.041582293
## MA_D_ 0.033452281 0.0786680691 -0.1017532843 -0.129600357 0.043747372
## MA_S_ 0.067629532 0.0000331198 0.0085678276 0.145514969 0.024424865
## RL_D_ -0.124105113 -0.2314066819 -0.1777208579 -0.246742818 0.027321812
## RL_S_ 0.234011383 0.2176153718 -0.0008007132 0.193847041 -0.101283627
## CRH_D_ -0.199387011 -0.3325527030 -0.6195552669 0.599224440 0.247501962
## CRH_S_ -0.111537252 0.2862145787 -0.2258982308 -0.364980779 -0.079672295
## CL_D_ 0.420357760 0.2839171972 0.4774752369 -0.401836174 -0.099802213
## CL_S_ -0.007118510 -0.3155016917 0.1486305256 0.264619285 0.073431827
## RH_D_ 0.131718959 -0.0192488322 0.0135788873 0.220166621 0.012452165
## RH_S_ 0.076279516 0.1266664929 -0.1386262671 -0.238830265 0.100283918
## ML_D_ 0.030946132 -0.2029346426 0.0036169232 -0.013455837 0.007117049
## ML_S_ -0.064116501 0.1001185628 -0.0889756824 -0.081697785 -0.091038560
## MRH_D_ -0.301329081 0.0275250229 0.2817989587 0.002374947 -0.105075410
## MRH_S_ 0.033160991 -0.0768537050 0.0516450539 -0.066110013 -0.070350089
## MRB_D_ -0.266428701 0.0159274984 0.2137871485 -0.083236134 -0.383527280
## MRB_S_ 0.219019953 -0.1126207447 -0.2383202828 0.056580959 0.109023030
## MrB_D_ -0.128384798 -0.0454840680 0.1074101093 0.402661032 0.292851330
## MrB_S_ 0.139916593 0.5067392402 0.0072343114 -0.103070389 -0.083920544
## MBH_D_ -0.015139321 -0.2034062609 -0.0727274125 0.254772732 0.150310758
## MBH_S_ 0.513922858 -0.0046228101 0.0685759780 -0.012306938 -0.087291897
## LD6 LD7 LD8
## BB1 -1.960548e-02 0.008783152 -0.092913954
## BB2 5.565241e-02 0.056291497 0.021799648
## BB3 7.792573e-03 0.056339728 0.080318064
## MA_D_ -9.774693e-02 0.205212442 0.043836236
## MA_S_ 1.033137e-01 -0.187680665 -0.052550785
## RL_D_ -3.131764e-01 -0.212218682 0.078347378
## RL_S_ 2.961460e-01 0.179297399 -0.054332279
## CRH_D_ -1.297294e-01 0.477233243 0.357555419
## CRH_S_ 3.554082e-01 -0.298212229 -0.277881524
## CL_D_ -5.016582e-03 -0.311983040 -0.198081197
## CL_S_ -3.648274e-01 0.182774571 0.003392232
## RH_D_ -1.276387e-01 0.040741681 -0.059800780
## RH_S_ 1.227557e-01 -0.007134815 0.087549312
## ML_D_ -1.645076e-01 -0.030114624 0.009322595
## ML_S_ 1.424095e-01 -0.013084180 -0.203052423
## MRH_D_ 4.079045e-01 0.066841447 -0.231503002
## MRH_S_ -3.010026e-01 -0.127675937 0.239277513
## MRB_D_ -5.343449e-02 -0.072333044 0.191850508
## MRB_S_ 2.513646e-03 0.103138860 -0.361404751
## MrB_D_ -3.015825e-02 0.076021936 0.092924383
## MrB_S_ 3.935252e-02 -0.059701039 0.193809823
## MBH_D_ -8.734776e-05 -0.276910125 0.328253354
## MBH_S_ 7.128472e-02 0.165161718 -0.152979505
##
## Proportion of trace:
## LD1 LD2 LD3 LD4 LD5 LD6 LD7 LD8
## 0.5595 0.2117 0.0773 0.0696 0.0309 0.0261 0.0181 0.0068
d_scores_laki_laki <- predict(discriminant_result_laki_laki)$x
d_scores_laki_laki <- as.data.frame(d_scores_laki_laki)
colnames(d_scores_laki_laki) <- paste0("LD", 1:ncol(d_scores_laki_laki))
contributions_laki_laki <- discriminant_result_laki_laki$scaling
top_contributors_laki_laki <- apply(contributions_laki_laki, 2, function(x) desc_vars_laki_laki[which.max(abs(x))])
contributions_df_laki_laki <- data.frame(Discriminant = colnames(contributions_laki_laki), Top_Contributor = top_contributors_laki_laki)
print(contributions_df_laki_laki)
## Discriminant Top_Contributor
## LD1 LD1 MBH_S_
## LD2 LD2 MrB_S_
## LD3 LD3 CRH_D_
## LD4 LD4 CRH_D_
## LD5 LD5 MRB_D_
## LD6 LD6 MRH_D_
## LD7 LD7 CRH_D_
## LD8 LD8 MRB_S_
# Uji multikolinearitas setelah DFA
vif_results_perempuan <- multicollinearity_test(cbind(clean_data_perempuan[dependent_var], d_scores_perempuan), dependent_var, colnames(d_scores_perempuan))
cat("\nUji Multikolinearitas Setelah DFA (Perempuan):\n")
##
## Uji Multikolinearitas Setelah DFA (Perempuan):
print(vif_results_perempuan)
## LD1 LD2 LD3 LD4 LD5 LD6 LD7
## 1 1 1 1 1 1 1
vif_results_laki_laki <- multicollinearity_test(cbind(clean_data_laki_laki[dependent_var], d_scores_laki_laki), dependent_var, colnames(d_scores_laki_laki))
cat("\nUji Multikolinearitas Setelah DFA (Laki-laki):\n")
##
## Uji Multikolinearitas Setelah DFA (Laki-laki):
print(vif_results_laki_laki)
## LD1 LD2 LD3 LD4 LD5 LD6 LD7 LD8
## 1 1 1 1 1 1 1 1
# Membuat boxplot data asli dan bersih untuk Perempuan
melted_data_perempuan_original <- melt(data_perempuan[, desc_vars_perempuan])
## Using MrB_D_ as id variables
melted_data_perempuan_clean <- melt(clean_data_perempuan[, desc_vars_perempuan])
## No id variables; using all as measure variables
boxplot_plot_perempuan_original <- ggplot(melted_data_perempuan_original, aes(x = variable, y = value)) +
geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(title = "Boxplot untuk Variabel Independen (Perempuan - Data Asli)", x = "Variabel", y = "Nilai")
boxplot_plot_perempuan_clean <- ggplot(melted_data_perempuan_clean, aes(x = variable, y = value)) +
geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(title = "Boxplot untuk Variabel Independen (Perempuan - Data Bersih)", x = "Variabel", y = "Nilai")
print(boxplot_plot_perempuan_original)
print(boxplot_plot_perempuan_clean)
# Membuat boxplot data asli dan bersih untuk Laki-laki
melted_data_laki_laki_original <- melt(data_laki_laki[, desc_vars_laki_laki])
## Using MrB_D_ as id variables
melted_data_laki_laki_clean <- melt(clean_data_laki_laki[, desc_vars_laki_laki])
## No id variables; using all as measure variables
boxplot_plot_laki_laki_original <- ggplot(melted_data_laki_laki_original, aes(x = variable, y = value)) +
geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(title = "Boxplot untuk Variabel Independen (Laki-laki - Data Asli)", x = "Variabel", y = "Nilai")
boxplot_plot_laki_laki_clean <- ggplot(melted_data_laki_laki_clean, aes(x = variable, y = value)) +
geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(title = "Boxplot untuk Variabel Independen (Laki-laki - Data Bersih)", x = "Variabel", y = "Nilai")
print(boxplot_plot_laki_laki_original)
print(boxplot_plot_laki_laki_clean)
# Menghitung statistik deskriptif untuk semua variabel DFA
desc_stats_perempuan <- describe(d_scores_perempuan)
cat("\nStatistika Deskriptif (Perempuan):\n")
##
## Statistika Deskriptif (Perempuan):
print(desc_stats_perempuan)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## LD1 1 67 0 2.71 -0.62 -0.26 1.85 -3.98 6.45 10.43 0.96 -0.09 0.33
## LD2 2 67 0 1.80 0.09 0.09 1.58 -4.74 4.75 9.50 -0.43 0.24 0.22
## LD3 3 67 0 1.63 0.03 -0.01 1.64 -4.17 3.15 7.32 0.06 -0.42 0.20
## LD4 4 67 0 1.38 -0.29 0.03 1.42 -3.42 2.55 5.97 -0.12 -0.53 0.17
## LD5 5 67 0 1.16 -0.07 -0.02 0.89 -2.41 2.98 5.40 0.22 -0.09 0.14
## LD6 6 67 0 1.12 -0.02 -0.02 0.96 -3.73 2.46 6.19 -0.06 0.99 0.14
## LD7 7 67 0 1.06 0.00 -0.01 0.86 -2.91 2.93 5.84 0.02 0.54 0.13
desc_stats_laki_laki <- describe(d_scores_laki_laki)
cat("\nStatistika Deskriptif (Laki-laki):\n")
##
## Statistika Deskriptif (Laki-laki):
print(desc_stats_laki_laki)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## LD1 1 99 0 2.26 -0.39 -0.02 2.57 -4.64 5.40 10.04 0.10 -0.70 0.23
## LD2 2 99 0 1.58 0.06 0.03 1.84 -3.25 2.88 6.13 -0.13 -1.06 0.16
## LD3 3 99 0 1.22 0.08 0.03 1.24 -3.20 3.04 6.24 -0.19 -0.37 0.12
## LD4 4 99 0 1.20 -0.04 0.01 1.07 -2.78 2.74 5.52 -0.07 -0.32 0.12
## LD5 5 99 0 1.07 -0.01 -0.03 1.17 -2.42 2.61 5.03 0.16 -0.48 0.11
## LD6 6 99 0 1.05 -0.10 -0.04 0.91 -2.03 4.37 6.40 0.72 1.73 0.11
## LD7 7 99 0 1.03 -0.02 -0.01 0.83 -2.20 3.88 6.08 0.45 1.72 0.10
## LD8 8 99 0 0.98 0.13 0.03 1.09 -2.28 2.31 4.59 -0.26 -0.67 0.10
# Mengubah variabel dependen menjadi faktor
clean_data_perempuan[[dependent_var]] <- as.factor(clean_data_perempuan[[dependent_var]])
clean_data_laki_laki[[dependent_var]] <- as.factor(clean_data_laki_laki[[dependent_var]])
# Analisis Regresi Logistik Ordinal untuk Perempuan
formula_str_perempuan <- paste(dependent_var, "~", paste(colnames(d_scores_perempuan), collapse = " + "))
data_model_perempuan <- cbind(clean_data_perempuan[dependent_var], d_scores_perempuan)
colnames(data_model_perempuan)[1] <- dependent_var # Pastikan nama kolom dependen sesuai
logistic_model_perempuan <- polr(as.formula(formula_str_perempuan), data = data_model_perempuan, Hess = TRUE)
summary(logistic_model_perempuan)
## Call:
## polr(formula = as.formula(formula_str_perempuan), data = data_model_perempuan,
## Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## LD1 0.61352 0.1123 5.46100
## LD2 -0.93021 0.1671 -5.56733
## LD3 0.78669 0.1802 4.36585
## LD4 0.45078 0.1891 2.38323
## LD5 -0.45263 0.2292 -1.97478
## LD6 -0.42589 0.1955 -2.17816
## LD7 -0.01837 0.2143 -0.08571
##
## Intercepts:
## Value Std. Error t value
## 2|3 -3.4781 0.5564 -6.2511
## 3|4 -1.9384 0.4534 -4.2748
## 4|5 -1.4357 0.4284 -3.3514
## 5|6 -0.0058 0.3955 -0.0147
## 6|7 0.7173 0.4069 1.7629
## 7|8 1.7511 0.4425 3.9574
## 8|9 3.5216 0.5440 6.4733
##
## Residual Deviance: 191.3588
## AIC: 219.3588
# Uji Likelihood Ratio untuk godness of fit (Perempuan)
null_model_perempuan <- polr(as.formula(paste(dependent_var, "~ 1")), data = cbind(clean_data_perempuan[dependent_var], d_scores_perempuan), Hess = TRUE)
lr_test_perempuan <- anova(null_model_perempuan, logistic_model_perempuan)
cat("\nUji Likelihood Ratio (Perempuan):\n")
##
## Uji Likelihood Ratio (Perempuan):
print(lr_test_perempuan)
## Likelihood ratio tests of ordinal regression models
##
## Response: Usia
## Model Resid. df Resid. Dev Test Df
## 1 1 60 269.2548
## 2 LD1 + LD2 + LD3 + LD4 + LD5 + LD6 + LD7 53 191.3588 1 vs 2 7
## LR stat. Pr(Chi)
## 1
## 2 77.89596 3.697043e-14
# Uji signifikan parsial (wald) untuk Perempuan
coeftest_result_perempuan <- coeftest(logistic_model_perempuan)
cat("\nUji Signifikansi Parsial (Wald) (Perempuan):\n")
##
## Uji Signifikansi Parsial (Wald) (Perempuan):
print(coeftest_result_perempuan)
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## LD1 0.613523 0.112346 5.4610 1.285e-06 ***
## LD2 -0.930213 0.167084 -5.5673 8.754e-07 ***
## LD3 0.786687 0.180191 4.3659 5.903e-05 ***
## LD4 0.450777 0.189146 2.3832 0.02078 *
## LD5 -0.452628 0.229204 -1.9748 0.05351 .
## LD6 -0.425894 0.195530 -2.1782 0.03386 *
## LD7 -0.018366 0.214288 -0.0857 0.93202
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Odds ratio untuk Perempuan
odds_ratios_perempuan <- exp(coef(logistic_model_perempuan))
cat("\nOdds Ratios (Perempuan):\n")
##
## Odds Ratios (Perempuan):
print(odds_ratios_perempuan)
## LD1 LD2 LD3 LD4 LD5 LD6 LD7
## 1.8469260 0.3944698 2.1961080 1.5695315 0.6359548 0.6531854 0.9818020
# Analisis Regresi Logistik Ordinal untuk Laki-laki
formula_str_laki_laki <- paste(dependent_var, "~", paste(colnames(d_scores_laki_laki), collapse = " + "))
data_model_laki_laki <- cbind(clean_data_laki_laki[dependent_var], d_scores_laki_laki)
colnames(data_model_laki_laki)[1] <- dependent_var # Pastikan nama kolom dependen sesuai
logistic_model_laki_laki <- polr(as.formula(formula_str_laki_laki), data = data_model_laki_laki, Hess = TRUE)
summary(logistic_model_laki_laki)
## Call:
## polr(formula = as.formula(formula_str_laki_laki), data = data_model_laki_laki,
## Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## LD1 1.0803591 0.1293 8.3532776
## LD2 -0.7905567 0.1408 -5.6140179
## LD3 -0.1778049 0.1614 -1.1014114
## LD4 0.2262238 0.1534 1.4751359
## LD5 -0.2026329 0.1752 -1.1567182
## LD6 0.1449148 0.1923 0.7537740
## LD7 0.1003944 0.1712 0.5864703
## LD8 0.0001511 0.1918 0.0007881
##
## Intercepts:
## Value Std. Error t value
## 1|2 -6.9161 0.9574 -7.2237
## 2|3 -3.9359 0.5025 -7.8322
## 3|4 -2.0117 0.3690 -5.4516
## 4|5 -0.7745 0.3125 -2.4787
## 5|6 0.6818 0.2992 2.2785
## 6|7 1.8571 0.3386 5.4847
## 7|8 2.9469 0.4070 7.2411
## 8|9 4.4349 0.5187 8.5506
##
## Residual Deviance: 301.1963
## AIC: 333.1963
# Uji Likelihood Ratio untuk godness of fit (Laki-laki)
null_model_laki_laki <- polr(as.formula(paste(dependent_var, "~ 1")), data = cbind(clean_data_laki_laki[dependent_var], d_scores_laki_laki), Hess = TRUE)
lr_test_laki_laki <- anova(null_model_laki_laki, logistic_model_laki_laki)
cat("\nUji Likelihood Ratio (Laki-laki):\n")
##
## Uji Likelihood Ratio (Laki-laki):
print(lr_test_laki_laki)
## Likelihood ratio tests of ordinal regression models
##
## Response: Usia
## Model Resid. df Resid. Dev Test
## 1 1 91 419.4027
## 2 LD1 + LD2 + LD3 + LD4 + LD5 + LD6 + LD7 + LD8 83 301.1963 1 vs 2
## Df LR stat. Pr(Chi)
## 1
## 2 8 118.2064 0
# Uji signifikan parsial (wald) untuk Laki-laki
coeftest_result_laki_laki <- coeftest(logistic_model_laki_laki)
cat("\nUji Signifikansi Parsial (Wald) (Laki-laki):\n")
##
## Uji Signifikansi Parsial (Wald) (Laki-laki):
print(coeftest_result_laki_laki)
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## LD1 1.08035907 0.12933355 8.3533 1.284e-12 ***
## LD2 -0.79055670 0.14081834 -5.6140 2.554e-07 ***
## LD3 -0.17780489 0.16143367 -1.1014 0.2739
## LD4 0.22622383 0.15335796 1.4751 0.1440
## LD5 -0.20263288 0.17517912 -1.1567 0.2507
## LD6 0.14491482 0.19225235 0.7538 0.4531
## LD7 0.10039442 0.17118416 0.5865 0.5592
## LD8 0.00015113 0.19178169 0.0008 0.9994
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Odds ratio untuk Laki-laki
odds_ratios_laki_laki <- exp(coef(logistic_model_laki_laki))
cat("\nOdds Ratios (Laki-laki):\n")
##
## Odds Ratios (Laki-laki):
print(odds_ratios_laki_laki)
## LD1 LD2 LD3 LD4 LD5 LD6 LD7 LD8
## 2.9457371 0.4535922 0.8371057 1.2538563 0.8165780 1.1559411 1.1056069 1.0001511
# Prediksi dan Evaluasi Model untuk Perempuan
predictions_perempuan <- predict(logistic_model_perempuan, newdata = cbind(clean_data_perempuan[dependent_var], d_scores_perempuan), type = "class")
actual_perempuan <- clean_data_perempuan[[dependent_var]]
confusion_matrix_perempuan <- table(Prediction = predictions_perempuan, Actual = actual_perempuan)
cat("\nConfusion Matrix (Perempuan):\n")
##
## Confusion Matrix (Perempuan):
print(confusion_matrix_perempuan)
## Actual
## Prediction 2 3 4 5 6 7 8 9
## 2 7 3 1 0 0 1 0 0
## 3 3 3 1 2 1 1 0 0
## 4 0 0 0 0 0 0 0 0
## 5 1 3 1 4 2 0 0 2
## 6 0 0 0 0 0 0 0 0
## 7 0 0 0 1 0 0 1 0
## 8 0 0 0 2 2 5 8 3
## 9 0 0 0 0 0 0 3 6
accuracy_perempuan <- sum(diag(confusion_matrix_perempuan)) / sum(confusion_matrix_perempuan)
cat("\nAkurasi (Perempuan):\n")
##
## Akurasi (Perempuan):
print(accuracy_perempuan)
## [1] 0.4179104
# Prediksi dan Evaluasi Model untuk Laki-laki
predictions_laki_laki <- predict(logistic_model_laki_laki, newdata = cbind(clean_data_laki_laki[dependent_var], d_scores_laki_laki), type = "class")
actual_laki_laki <- clean_data_laki_laki[[dependent_var]]
confusion_matrix_laki_laki <- table(Prediction = predictions_laki_laki, Actual = actual_laki_laki)
cat("\nConfusion Matrix (Laki-laki):\n")
##
## Confusion Matrix (Laki-laki):
print(confusion_matrix_laki_laki)
## Actual
## Prediction 1 2 3 4 5 6 7 8 9
## 1 0 1 0 0 0 0 0 0 0
## 2 1 4 0 1 0 0 0 0 0
## 3 1 6 11 2 3 3 0 0 0
## 4 0 0 2 1 1 0 0 0 0
## 5 0 0 1 6 7 5 2 1 0
## 6 0 0 0 1 5 3 0 0 0
## 7 0 0 0 0 0 2 5 2 3
## 8 0 0 0 0 1 0 2 4 4
## 9 0 0 0 0 0 0 1 5 2
accuracy_laki_laki <- sum(diag(confusion_matrix_laki_laki)) / sum(confusion_matrix_laki_laki)
cat("\nAkurasi (Laki-laki):\n")
##
## Akurasi (Laki-laki):
print(accuracy_laki_laki)
## [1] 0.3737374
# Cut-off analysis untuk Perempuan
roc_list_perempuan <- lapply(desc_vars_perempuan, function(var) {
roc(clean_data_perempuan[[dependent_var]], clean_data_perempuan[[var]])
})
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls > cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls > cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls > cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls > cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls > cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
for (i in 1:length(roc_list_perempuan)) {
var_name <- desc_vars_perempuan[i]
roc_curve <- roc_list_perempuan[[i]]
cut_off <- median(clean_data_perempuan[[var_name]], na.rm = TRUE)
plot <- ggroc(roc_curve) +
geom_vline(xintercept = 1 - roc_curve$specificities[which.max(roc_curve$sensitivities)], linetype = "dashed") +
geom_hline(yintercept = roc_curve$sensitivities[which.max(roc_curve$sensitivities)], linetype = "dashed") +
annotate("text", x = 1 - roc_curve$specificities[which.max(roc_curve$sensitivities)],
y = roc_curve$sensitivities[which.max(roc_curve$sensitivities)],
label = paste(cut_off, "\n(Sens:", round(roc_curve$sensitivities[which.max(roc_curve$sensitivities)], 3),
", Spec:", round(roc_curve$specificities[which.max(roc_curve$sensitivities)], 3), ")"),
hjust = 1, vjust = -1) +
labs(title = var_name, x = "1 - Specificity", y = "Sensitivity")
# Display the plot
print(plot)
}
# Cut-off analysis untuk Laki-laki
roc_list_laki_laki <- lapply(desc_vars_laki_laki, function(var) {
roc(clean_data_laki_laki[[dependent_var]], clean_data_laki_laki[[var]])
})
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
for (i in 1:length(roc_list_laki_laki)) {
var_name <- desc_vars_laki_laki[i]
roc_curve <- roc_list_laki_laki[[i]]
cut_off <- median(clean_data_laki_laki[[var_name]], na.rm = TRUE)
plot <- ggroc(roc_curve) +
geom_vline(xintercept = 1 - roc_curve$specificities[which.max(roc_curve$sensitivities)], linetype = "dashed") +
geom_hline(yintercept = roc_curve$sensitivities[which.max(roc_curve$sensitivities)], linetype = "dashed") +
annotate("text", x = 1 - roc_curve$specificities[which.max(roc_curve$sensitivities)],
y = roc_curve$sensitivities[which.max(roc_curve$sensitivities)],
label = paste(cut_off, "\n(Sens:", round(roc_curve$sensitivities[which.max(roc_curve$sensitivities)], 3),
", Spec:", round(roc_curve$specificities[which.max(roc_curve$sensitivities)], 3), ")"),
hjust = 1, vjust = -1) +
labs(title = var_name, x = "1 - Specificity", y = "Sensitivity")
# Display the plot
print(plot)
}
# Menggabungkan ROC untuk perempuan dan laki-laki
library(ggplot2)
library(pROC)
# Cut-off analysis untuk Perempuan
roc_list_perempuan <- lapply(desc_vars_perempuan, function(var) {
roc(clean_data_perempuan[[dependent_var]], clean_data_perempuan[[var]])
})
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls > cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls > cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls > cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls > cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls > cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
## Warning in roc.default(clean_data_perempuan[[dependent_var]],
## clean_data_perempuan[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 2, case = 3
## Setting direction: controls < cases
# Cut-off analysis untuk Laki-laki
roc_list_laki_laki <- lapply(desc_vars_laki_laki, function(var) {
roc(clean_data_laki_laki[[dependent_var]], clean_data_laki_laki[[var]])
})
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls < cases
## Warning in roc.default(clean_data_laki_laki[[dependent_var]],
## clean_data_laki_laki[[var]]): 'response' has more than two levels. Consider
## setting 'levels' explicitly or using 'multiclass.roc' instead
## Setting levels: control = 1, case = 2
## Setting direction: controls > cases
# Membuat plot untuk setiap variabel
for (i in 1:length(roc_list_perempuan)) {
var_name <- desc_vars_perempuan[i]
# ROC untuk perempuan
roc_curve_perempuan <- roc_list_perempuan[[i]]
cut_off_perempuan <- median(clean_data_perempuan[[var_name]], na.rm = TRUE)
# ROC untuk laki-laki
roc_curve_laki_laki <- roc_list_laki_laki[[i]]
cut_off_laki_laki <- median(clean_data_laki_laki[[var_name]], na.rm = TRUE)
# Dataframe untuk plot
plot_data_perempuan <- data.frame(
Specificity = 1 - roc_curve_perempuan$specificities,
Sensitivity = roc_curve_perempuan$sensitivities,
Gender = "Perempuan"
)
plot_data_laki_laki <- data.frame(
Specificity = 1 - roc_curve_laki_laki$specificities,
Sensitivity = roc_curve_laki_laki$sensitivities,
Gender = "Laki-laki"
)
plot_data <- rbind(plot_data_perempuan, plot_data_laki_laki)
# Membuat plot ROC
plot <- ggplot(plot_data, aes(x = Specificity, y = Sensitivity, color = Gender)) +
geom_line() +
geom_vline(xintercept = 1 - roc_curve_perempuan$specificities[which.max(roc_curve_perempuan$sensitivities)], linetype = "dashed", color = "red") +
geom_hline(yintercept = roc_curve_perempuan$sensitivities[which.max(roc_curve_perempuan$sensitivities)], linetype = "dashed", color = "red") +
geom_vline(xintercept = 1 - roc_curve_laki_laki$specificities[which.max(roc_curve_laki_laki$sensitivities)], linetype = "dashed", color = "blue") +
geom_hline(yintercept = roc_curve_laki_laki$sensitivities[which.max(roc_curve_laki_laki$sensitivities)], linetype = "dashed", color = "blue") +
annotate("text", x = 1 - roc_curve_perempuan$specificities[which.max(roc_curve_perempuan$sensitivities)],
y = roc_curve_perempuan$sensitivities[which.max(roc_curve_perempuan$sensitivities)],
label = paste(cut_off_perempuan, "\n(Sens:", round(roc_curve_perempuan$sensitivities[which.max(roc_curve_perempuan$sensitivities)], 3),
", Spec:", round(roc_curve_perempuan$specificities[which.max(roc_curve_perempuan$sensitivities)], 3), ")"),
hjust = 1, vjust = -1, color = "red") +
annotate("text", x = 1 - roc_curve_laki_laki$specificities[which.max(roc_curve_laki_laki$sensitivities)],
y = roc_curve_laki_laki$sensitivities[which.max(roc_curve_laki_laki$sensitivities)],
label = paste(cut_off_laki_laki, "\n(Sens:", round(roc_curve_laki_laki$sensitivities[which.max(roc_curve_laki_laki$sensitivities)], 3),
", Spec:", round(roc_curve_laki_laki$specificities[which.max(roc_curve_laki_laki$sensitivities)], 3), ")"),
hjust = 1, vjust = -1, color = "blue") +
labs(title = var_name, x = "1 - Specificity", y = "Sensitivity") +
scale_color_manual(values = c("Perempuan" = "red", "Laki-laki" = "blue"))
# Display the plot
print(plot)
}
# Kesimpulan
# Menghubungkan hasil regresi dengan kontribusi variabel asli
contributions_df_perempuan <- data.frame(
Discriminant = colnames(d_scores_perempuan),
Variable = top_contributors_perempuan,
Estimate = coef(logistic_model_perempuan),
Std.Error = sqrt(diag(vcov(logistic_model_perempuan))),
z.value = coef(logistic_model_perempuan) / sqrt(diag(vcov(logistic_model_perempuan))),
p.value = 2 * (1 - pnorm(abs(coef(logistic_model_perempuan) / sqrt(diag(vcov(logistic_model_perempuan))))))
)
## Warning in data.frame(Discriminant = colnames(d_scores_perempuan), Variable =
## top_contributors_perempuan, : row names were found from a short variable and
## have been discarded
contributions_df_perempuan$Significance <- ifelse(contributions_df_perempuan$p.value < 0.001, "Signifikan", "Tidak Signifikan")
cat("\nKesimpulan (Perempuan):\n")
##
## Kesimpulan (Perempuan):
print(contributions_df_perempuan)
## Discriminant Variable Estimate Std.Error z.value p.value
## 1 LD1 MRB_D_ 0.61352265 0.1123462 5.46099916 4.734623e-08
## 2 LD2 MRH_D_ -0.93021279 0.1670842 -5.56732963 2.586728e-08
## 3 LD3 MRB_S_ 0.78668671 0.1801909 4.36585245 1.266279e-05
## 4 LD4 MrB_D_ 0.45077718 0.1891457 2.38322728 1.716160e-02
## 5 LD5 CRH_D_ -0.45262776 0.2292044 -1.97477792 4.829336e-02
## 6 LD6 MRH_S_ -0.42589430 0.1955296 -2.17815800 2.939428e-02
## 7 LD7 MRB_S_ -0.01836564 0.2142880 -0.08570538 9.317006e-01
## 8 LD1 MRB_D_ 0.61352265 0.5563929 1.10267880 2.701667e-01
## 9 LD2 MRH_D_ -0.93021279 0.4534362 -2.05147455 4.022076e-02
## 10 LD3 MRB_S_ 0.78668671 0.4284008 1.83633356 6.630834e-02
## 11 LD4 MrB_D_ 0.45077718 0.3954850 1.13980862 2.543660e-01
## 12 LD5 CRH_D_ -0.45262776 0.4068891 -1.11241050 2.659617e-01
## 13 LD6 MRH_S_ -0.42589430 0.4424872 -0.96250081 3.357981e-01
## 14 LD7 MRB_S_ -0.01836564 0.5440165 -0.03375933 9.730691e-01
## Significance
## 1 Signifikan
## 2 Signifikan
## 3 Signifikan
## 4 Tidak Signifikan
## 5 Tidak Signifikan
## 6 Tidak Signifikan
## 7 Tidak Signifikan
## 8 Tidak Signifikan
## 9 Tidak Signifikan
## 10 Tidak Signifikan
## 11 Tidak Signifikan
## 12 Tidak Signifikan
## 13 Tidak Signifikan
## 14 Tidak Signifikan
contributions_df_laki_laki <- data.frame(
Discriminant = colnames(d_scores_laki_laki),
Variable = top_contributors_laki_laki,
Estimate = coef(logistic_model_laki_laki),
Std.Error = sqrt(diag(vcov(logistic_model_laki_laki))),
z.value = coef(logistic_model_laki_laki) / sqrt(diag(vcov(logistic_model_laki_laki))),
p.value = 2 * (1 - pnorm(abs(coef(logistic_model_laki_laki) / sqrt(diag(vcov(logistic_model_laki_laki))))))
)
## Warning in data.frame(Discriminant = colnames(d_scores_laki_laki), Variable =
## top_contributors_laki_laki, : row names were found from a short variable and
## have been discarded
contributions_df_laki_laki$Significance <- ifelse(contributions_df_laki_laki$p.value < 0.001, "Signifikan", "Tidak Signifikan")
cat("\nKesimpulan (Laki-laki):\n")
##
## Kesimpulan (Laki-laki):
print(contributions_df_laki_laki)
## Discriminant Variable Estimate Std.Error z.value p.value
## 1 LD1 MBH_S_ 1.0803590747 0.1293336 8.3532776491 0.000000e+00
## 2 LD2 MrB_S_ -0.7905566971 0.1408183 -5.6140179489 1.976817e-08
## 3 LD3 CRH_D_ -0.1778048929 0.1614337 -1.1014114233 2.707176e-01
## 4 LD4 CRH_D_ 0.2262238312 0.1533580 1.4751358818 1.401760e-01
## 5 LD5 MRB_D_ -0.2026328764 0.1751791 -1.1567182237 2.473875e-01
## 6 LD6 MRH_D_ 0.1449148210 0.1922524 0.7537739894 4.509849e-01
## 7 LD7 CRH_D_ 0.1003944158 0.1711842 0.5864702534 5.575595e-01
## 8 LD8 MRB_S_ 0.0001511338 0.1917817 0.0007880510 9.993712e-01
## 9 LD1 MBH_S_ 1.0803590747 0.9574196 1.1284071380 2.591480e-01
## 10 LD2 MrB_S_ -0.7905566971 0.5025240 -1.5731718808 1.156790e-01
## 11 LD3 CRH_D_ -0.1778048929 0.3690168 -0.4818341212 6.299238e-01
## 12 LD4 CRH_D_ 0.2262238312 0.3124772 0.7239690915 4.690847e-01
## 13 LD5 MRB_D_ -0.2026328764 0.2992184 -0.6772073198 4.982744e-01
## 14 LD6 MRH_D_ 0.1449148210 0.3385924 0.4279920053 6.686569e-01
## 15 LD7 CRH_D_ 0.1003944158 0.4069656 0.2466901949 8.051480e-01
## 16 LD8 MRB_S_ 0.0001511338 0.5186669 0.0002913889 9.997675e-01
## Significance
## 1 Signifikan
## 2 Signifikan
## 3 Tidak Signifikan
## 4 Tidak Signifikan
## 5 Tidak Signifikan
## 6 Tidak Signifikan
## 7 Tidak Signifikan
## 8 Tidak Signifikan
## 9 Tidak Signifikan
## 10 Tidak Signifikan
## 11 Tidak Signifikan
## 12 Tidak Signifikan
## 13 Tidak Signifikan
## 14 Tidak Signifikan
## 15 Tidak Signifikan
## 16 Tidak Signifikan
# Menyediakan ringkasan dengan estimasi parameter untuk Perempuan
summary_table_perempuan <- summary(logistic_model_perempuan)
coefficients_perempuan <- summary_table_perempuan$coefficients[1:length(colnames(d_scores_perempuan)), ]
std_errors_perempuan <- coefficients_perempuan[, "Std. Error"]
z_values_perempuan <- coefficients_perempuan[, "t value"]
p_values_perempuan <- 2 * (1 - pnorm(abs(z_values_perempuan)))
# Menyediakan ringkasan dengan estimasi parameter untuk Laki-laki
summary_table_laki_laki <- summary(logistic_model_laki_laki)
coefficients_laki_laki <- summary_table_laki_laki$coefficients[1:length(colnames(d_scores_laki_laki)), ]
std_errors_laki_laki <- coefficients_laki_laki[, "Std. Error"]
z_values_laki_laki <- coefficients_laki_laki[, "t value"]
p_values_laki_laki <- 2 * (1 - pnorm(abs(z_values_laki_laki)))
# Sesuaikan dengan nama variabel asli
variable_names_perempuan <- top_contributors_perempuan
variable_names_laki_laki <- top_contributors_laki_laki
results_df_perempuan <- data.frame(
Discriminant = colnames(d_scores_perempuan),
Variable = variable_names_perempuan,
Estimate = coefficients_perempuan[, "Value"],
Std.Error = std_errors_perempuan,
z.value = z_values_perempuan,
p.value = p_values_perempuan
)
cat("\nEstimasi Parameter, Std. Error, dan P-value (Perempuan):\n")
##
## Estimasi Parameter, Std. Error, dan P-value (Perempuan):
print(results_df_perempuan)
## Discriminant Variable Estimate Std.Error z.value p.value
## LD1 LD1 MRB_D_ 0.61352265 0.1123462 5.46099916 4.734623e-08
## LD2 LD2 MRH_D_ -0.93021279 0.1670842 -5.56732963 2.586728e-08
## LD3 LD3 MRB_S_ 0.78668671 0.1801909 4.36585245 1.266279e-05
## LD4 LD4 MrB_D_ 0.45077718 0.1891457 2.38322728 1.716160e-02
## LD5 LD5 CRH_D_ -0.45262776 0.2292044 -1.97477792 4.829336e-02
## LD6 LD6 MRH_S_ -0.42589430 0.1955296 -2.17815800 2.939428e-02
## LD7 LD7 MRB_S_ -0.01836564 0.2142880 -0.08570538 9.317006e-01
results_df_laki_laki <- data.frame(
Discriminant = colnames(d_scores_laki_laki),
Variable = variable_names_laki_laki,
Estimate = coefficients_laki_laki[, "Value"],
Std.Error = std_errors_laki_laki,
z.value = z_values_laki_laki,
p.value = p_values_laki_laki
)
cat("\nEstimasi Parameter, Std. Error, dan P-value (Laki-laki):\n")
##
## Estimasi Parameter, Std. Error, dan P-value (Laki-laki):
print(results_df_laki_laki)
## Discriminant Variable Estimate Std.Error z.value p.value
## LD1 LD1 MBH_S_ 1.0803590747 0.1293336 8.353277649 0.000000e+00
## LD2 LD2 MrB_S_ -0.7905566971 0.1408183 -5.614017949 1.976817e-08
## LD3 LD3 CRH_D_ -0.1778048929 0.1614337 -1.101411423 2.707176e-01
## LD4 LD4 CRH_D_ 0.2262238312 0.1533580 1.475135882 1.401760e-01
## LD5 LD5 MRB_D_ -0.2026328764 0.1751791 -1.156718224 2.473875e-01
## LD6 LD6 MRH_D_ 0.1449148210 0.1922524 0.753773989 4.509849e-01
## LD7 LD7 CRH_D_ 0.1003944158 0.1711842 0.586470253 5.575595e-01
## LD8 LD8 MRB_S_ 0.0001511338 0.1917817 0.000788051 9.993712e-01
# Kesimpulan untuk Perempuan
kesimpulan_perempuan <- contributions_df_perempuan %>%
group_by(Variable) %>%
summarise(Significance = list(Significance),
Estimate = list(Estimate))
cat("\nKesimpulan (Perempuan):\n")
##
## Kesimpulan (Perempuan):
for (i in 1:nrow(kesimpulan_perempuan)) {
var <- kesimpulan_perempuan$Variable[i]
signifikan_ld <- contributions_df_perempuan %>% filter(Variable == var & Significance == "Signifikan") %>% select(Discriminant, Estimate)
tidak_signifikan_ld <- contributions_df_perempuan %>% filter(Variable == var & Significance == "Tidak Signifikan") %>% select(Discriminant, Estimate)
cat(var, ":\n")
if (nrow(signifikan_ld) > 0) {
cat(" Signifikan di: \n")
for (j in 1:nrow(signifikan_ld)) {
cat(" ", signifikan_ld$Discriminant[j], "dengan estimasi parameter", signifikan_ld$Estimate[j], "\n")
}
}
if (nrow(tidak_signifikan_ld) > 0) {
cat(" Tidak Signifikan di: \n")
for (j in 1:nrow(tidak_signifikan_ld)) {
cat(" ", tidak_signifikan_ld$Discriminant[j], "dengan estimasi parameter", tidak_signifikan_ld$Estimate[j], "\n")
}
}
}
## CRH_D_ :
## Tidak Signifikan di:
## LD5 dengan estimasi parameter -0.4526278
## LD5 dengan estimasi parameter -0.4526278
## MRB_D_ :
## Signifikan di:
## LD1 dengan estimasi parameter 0.6135226
## Tidak Signifikan di:
## LD1 dengan estimasi parameter 0.6135226
## MRB_S_ :
## Signifikan di:
## LD3 dengan estimasi parameter 0.7866867
## Tidak Signifikan di:
## LD7 dengan estimasi parameter -0.01836564
## LD3 dengan estimasi parameter 0.7866867
## LD7 dengan estimasi parameter -0.01836564
## MRH_D_ :
## Signifikan di:
## LD2 dengan estimasi parameter -0.9302128
## Tidak Signifikan di:
## LD2 dengan estimasi parameter -0.9302128
## MRH_S_ :
## Tidak Signifikan di:
## LD6 dengan estimasi parameter -0.4258943
## LD6 dengan estimasi parameter -0.4258943
## MrB_D_ :
## Tidak Signifikan di:
## LD4 dengan estimasi parameter 0.4507772
## LD4 dengan estimasi parameter 0.4507772
# Mengambil estimasi parameter yang signifikan saja untuk Perempuan
selected_estimates_perempuan <- contributions_df_perempuan %>%
filter(Significance == "Signifikan") %>%
select(Variable, Discriminant, Estimate)
cat("\nEstimasi Parameter yang Diambil (Perempuan):\n")
##
## Estimasi Parameter yang Diambil (Perempuan):
print(selected_estimates_perempuan)
## Variable Discriminant Estimate
## 1 MRB_D_ LD1 0.6135226
## 2 MRH_D_ LD2 -0.9302128
## 3 MRB_S_ LD3 0.7866867
# Kesimpulan untuk Laki-laki
kesimpulan_laki_laki <- contributions_df_laki_laki %>%
group_by(Variable) %>%
summarise(Significance = list(Significance),
Estimate = list(Estimate))
cat("\nKesimpulan (Laki-laki):\n")
##
## Kesimpulan (Laki-laki):
for (i in 1:nrow(kesimpulan_laki_laki)) {
var <- kesimpulan_laki_laki$Variable[i]
signifikan_ld <- contributions_df_laki_laki %>% filter(Variable == var & Significance == "Signifikan") %>% select(Discriminant, Estimate)
tidak_signifikan_ld <- contributions_df_laki_laki %>% filter(Variable == var & Significance == "Tidak Signifikan") %>% select(Discriminant, Estimate)
cat(var, ":\n")
if (nrow(signifikan_ld) > 0) {
cat(" Signifikan di: \n")
for (j in 1:nrow(signifikan_ld)) {
cat(" ", signifikan_ld$Discriminant[j], "dengan estimasi parameter", signifikan_ld$Estimate[j], "\n")
}
}
if (nrow(tidak_signifikan_ld) > 0) {
cat(" Tidak Signifikan di: \n")
for (j in 1:nrow(tidak_signifikan_ld)) {
cat(" ", tidak_signifikan_ld$Discriminant[j], "dengan estimasi parameter", tidak_signifikan_ld$Estimate[j], "\n")
}
}
}
## CRH_D_ :
## Tidak Signifikan di:
## LD3 dengan estimasi parameter -0.1778049
## LD4 dengan estimasi parameter 0.2262238
## LD7 dengan estimasi parameter 0.1003944
## LD3 dengan estimasi parameter -0.1778049
## LD4 dengan estimasi parameter 0.2262238
## LD7 dengan estimasi parameter 0.1003944
## MBH_S_ :
## Signifikan di:
## LD1 dengan estimasi parameter 1.080359
## Tidak Signifikan di:
## LD1 dengan estimasi parameter 1.080359
## MRB_D_ :
## Tidak Signifikan di:
## LD5 dengan estimasi parameter -0.2026329
## LD5 dengan estimasi parameter -0.2026329
## MRB_S_ :
## Tidak Signifikan di:
## LD8 dengan estimasi parameter 0.0001511338
## LD8 dengan estimasi parameter 0.0001511338
## MRH_D_ :
## Tidak Signifikan di:
## LD6 dengan estimasi parameter 0.1449148
## LD6 dengan estimasi parameter 0.1449148
## MrB_S_ :
## Signifikan di:
## LD2 dengan estimasi parameter -0.7905567
## Tidak Signifikan di:
## LD2 dengan estimasi parameter -0.7905567
# Mengambil estimasi parameter yang signifikan saja untuk Laki-laki
selected_estimates_laki_laki <- contributions_df_laki_laki %>%
filter(Significance == "Signifikan") %>%
select(Variable, Discriminant, Estimate)
cat("\nEstimasi Parameter yang Diambil (Laki-laki):\n")
##
## Estimasi Parameter yang Diambil (Laki-laki):
print(selected_estimates_laki_laki)
## Variable Discriminant Estimate
## 1 MBH_S_ LD1 1.0803591
## 2 MrB_S_ LD2 -0.7905567
# Menggabungkan variabel yang sama dan mengambil estimasi dengan p-value paling rendah untuk Perempuan
selected_estimates_perempuan <- contributions_df_perempuan %>%
filter(Significance == "Signifikan") %>%
group_by(Variable) %>%
arrange(p.value) %>%
slice(1) %>%
select(Variable, Discriminant, Estimate, p.value)
cat("\nEstimasi Parameter yang Diambil (Perempuan):\n")
##
## Estimasi Parameter yang Diambil (Perempuan):
print(selected_estimates_perempuan)
## # A tibble: 3 × 4
## # Groups: Variable [3]
## Variable Discriminant Estimate p.value
## <chr> <chr> <dbl> <dbl>
## 1 MRB_D_ LD1 0.614 0.0000000473
## 2 MRB_S_ LD3 0.787 0.0000127
## 3 MRH_D_ LD2 -0.930 0.0000000259
# Menggabungkan variabel yang sama dan mengambil estimasi dengan p-value paling rendah untuk Laki-laki
selected_estimates_laki_laki <- contributions_df_laki_laki %>%
filter(Significance == "Signifikan") %>%
group_by(Variable) %>%
arrange(p.value) %>%
slice(1) %>%
select(Variable, Discriminant, Estimate, p.value)
cat("\nEstimasi Parameter yang Diambil (Laki-laki):\n")
##
## Estimasi Parameter yang Diambil (Laki-laki):
print(selected_estimates_laki_laki)
## # A tibble: 2 × 4
## # Groups: Variable [2]
## Variable Discriminant Estimate p.value
## <chr> <chr> <dbl> <dbl>
## 1 MBH_S_ LD1 1.08 0
## 2 MrB_S_ LD2 -0.791 0.0000000198
library(ggplot2)
library(dplyr)
library(readxl)
# Memastikan nama kolom yang benar
colnames(data) <- c("Usia", "Jenis_Kelamin", colnames(data)[3:ncol(data)])
# Menggabungkan data laki-laki dan perempuan
data_combined <- bind_rows(
data %>% filter(Jenis_Kelamin == "Perempuan"),
data %>% filter(Jenis_Kelamin == "Laki-laki")
)
# Memetakan kategori usia
map_usia_category <- function(usia) {
if (usia == 1) {
return("6-10 tahun")
} else if (usia == 2) {
return("11-15 tahun")
} else if (usia == 3) {
return("16-20 tahun")
} else if (usia == 4) {
return("21-25 tahun")
} else if (usia == 5) {
return("26-30 tahun")
} else if (usia == 6) {
return("31-35 tahun")
} else if (usia == 7) {
return("36-40 tahun")
} else if (usia == 8) {
return("41-45 tahun")
} else if (usia == 9) {
return("46-50 tahun")
} else {
return("Unknown")
}
}
data_combined <- data_combined %>%
mutate(Kategori_Usia = sapply(Usia, map_usia_category))
category_order <- c("6-10 tahun", "11-15 tahun", "16-20 tahun", "21-25 tahun",
"26-30 tahun", "31-35 tahun", "36-40 tahun", "41-45 tahun", "46-50 tahun")
data_combined$Kategori_Usia <- factor(data_combined$Kategori_Usia, levels = category_order, ordered = TRUE)
usia_count_combined <- data_combined %>%
group_by(Kategori_Usia, Jenis_Kelamin) %>%
summarise(Frekuensi = n(), .groups = 'drop')
# Membuat barplot dengan ggplot2
barplot_combined <- ggplot(usia_count_combined, aes(x = Kategori_Usia, y = Frekuensi, fill = Jenis_Kelamin)) +
geom_bar(stat = "identity", position = "dodge") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(title = "Frekuensi Kategori Usia Berdasarkan Jenis Kelamin", x = "Kategori Usia", y = "Frekuensi") +
scale_fill_manual(values = c("Perempuan" = "pink", "Laki-laki" = "blue"))
print(barplot_combined)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.1
## ✔ readr 2.1.5
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ ggplot2::%+%() masks psych::%+%()
## ✖ ggplot2::alpha() masks psych::alpha()
## ✖ plotly::filter() masks dplyr::filter(), stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::recode() masks car::recode()
## ✖ plotly::select() masks MASS::select(), dplyr::select()
## ✖ purrr::some() masks car::some()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(cutpointr)
##
## Attaching package: 'cutpointr'
##
## The following objects are masked from 'package:pROC':
##
## auc, roc
# Daftar variabel yang akan dianalisis
variables <- colnames(data)[3:ncol(data)]
# Fungsi untuk melakukan cut-point analysis pada setiap variabel
cutpoint_analysis <- function(var, data) {
data <- data %>% drop_na(var) # Menghapus baris yang mengandung NA pada variabel yang dianalisis
median_data <- mean(data[[var]], na.rm = TRUE)
data$class <- ifelse(data[[var]] > median_data, 1, 2)
if (length(unique(data$class)) == 2) { # Pastikan ada dua kelas
opt_cut <- cutpointr(data, !!sym(var), class, method = minimize_metric, metric = misclassification_cost, na.rm = TRUE)
cat("\nAnalisis untuk variabel:", var, "\n")
print(summary(opt_cut))
plot_obj = plot_metric(opt_cut)
print(plot_obj)
# Hitung p-value menggunakan uji statistik yang sesuai
# Contoh: uji t-test untuk membandingkan rata-rata kedua kelompok
p_value <- t.test(data[[var]] ~ data$class)$p.value
cat("P-value:", p_value, "\n")
} else {
cat("\nVariabel", var, "tidak memiliki dua kelas setelah pembagian berdasarkan median.\n")
}
}
# Melakukan analisis untuk setiap variabel
for (var in variables) {
cutpoint_analysis(var, data_perempuan)
}
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: BB1
## Method: minimize_metric
## Predictor: BB1
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 62 42
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 116.8 0 1 1 1 62 0 0
## tn
## 42
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## Overall 95.0 102.990 112.000 118.400 116.7592 121.725 125.625 131.9 7.180112
## 1 116.8 117.300 119.725 121.155 121.6784 123.300 127.855 131.9 3.143271
## 2 95.0 100.835 107.575 111.200 109.4976 113.075 114.590 116.3 4.919820
## NAs
## 0
## 0
## 0
## P-value: 2.188508e-21
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: BB2
## Method: minimize_metric
## Predictor: BB2
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 53 51
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 93.4 0 1 1 1 53 0 0
## tn
## 51
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 81.9 85.345 89.575 93.4 93.34904 96.625 100.95 106.4 5.100974 0
## 1 93.4 93.560 95.300 96.6 97.36415 98.600 103.82 106.4 2.995066 0
## 2 81.9 82.550 87.800 89.5 89.17647 91.350 93.10 93.3 3.069999 0
## P-value: 5.999011e-25
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: BB3
## Method: minimize_metric
## Predictor: BB3
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 56 48
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 92.4 0 1 1 1 56 0 0
## tn
## 48
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 72.4 78.775 89.900 92.75 92.31538 96.800 100.740 104.4 6.558841 0
## 1 92.4 92.575 94.775 96.65 96.88750 98.925 101.225 104.4 2.866297 0
## 2 72.4 74.260 84.975 89.45 86.98125 90.925 91.865 92.1 5.540284 0
## P-value: 4.86074e-17
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MA_D_
## Method: minimize_metric
## Predictor: MA_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 55 49
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 127.1 0 1 1 1 55 0 0
## tn
## 49
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## Overall 104.9 116.465 122.875 127.25 126.8713 130.25 137.00 141.0 6.421450
## 1 127.1 127.270 128.700 130.20 131.5836 134.05 138.97 141.0 3.688157
## 2 104.9 112.860 120.000 122.40 121.5820 125.10 126.20 126.7 4.379161
## NAs
## 0
## 0
## 0
## P-value: 9.327152e-22
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MA_S_
## Method: minimize_metric
## Predictor: MA_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 46 58
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 128.1 0 1 1 1 46 0 0
## tn
## 58
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## Overall 104.9 118.900 122.4 126.50 128.0404 131.45 135.895 276.2 15.815511
## 1 128.1 128.325 130.1 132.10 135.1935 133.20 138.775 276.2 21.440350
## 2 104.9 117.790 120.2 122.95 122.3672 125.65 127.130 127.8 3.877664
## NAs
## 0
## 0
## 0
## P-value: 0.0002171657
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: RL_D_
## Method: minimize_metric
## Predictor: RL_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 58 46
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 52.5 0 1 1 1 58 0 0
## tn
## 46
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 36.7 41.020 48.425 52.8 52.45577 56.900 61.680 69.3 6.420520 0
## 1 52.5 52.685 53.875 56.4 56.91207 58.675 64.775 69.3 3.721963 0
## 2 36.7 39.675 43.475 47.4 46.83696 50.725 52.100 52.4 4.351978 0
## P-value: 2.940521e-21
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: RL_S_
## Method: minimize_metric
## Predictor: RL_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 58 46
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 52.5 0 1 1 1 58 0 0
## tn
## 46
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 36.5 41.105 49.175 52.80 52.24904 56.525 60.80 65.1 6.016998 0
## 1 52.5 52.685 54.000 55.85 56.47414 58.275 62.42 65.1 3.172445 0
## 2 36.5 39.375 43.950 47.55 46.92174 50.275 52.00 52.2 4.254405 0
## P-value: 6.10901e-21
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: CRH_D_
## Method: minimize_metric
## Predictor: CRH_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 58 46
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 57.2 0 1 1 1 58 0 0
## tn
## 46
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 41.5 45.28 53.55 57.85 56.55865 60.225 64.910 84.8 6.589615 0
## 1 57.2 57.57 58.40 60.10 61.04138 62.075 66.960 84.8 4.162575 0
## 2 41.5 43.00 47.55 51.20 50.90652 54.875 55.975 56.4 4.345849 0
## P-value: 8.64658e-21
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: CRH_S_
## Method: minimize_metric
## Predictor: CRH_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 62 42
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 56.2 0 1 1 1 62 0 0
## tn
## 42
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 40.6 45.945 52.075 57.45 56.18365 60.200 63.155 68.5 5.729063 0
## 1 56.2 56.800 58.100 59.90 60.05161 61.725 64.190 68.5 2.547949 0
## 2 40.6 42.425 48.025 51.00 50.47381 54.150 55.895 56.1 4.095748 0
## P-value: 3.653715e-20
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: CL_D_
## Method: minimize_metric
## Predictor: CL_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 60 44
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 55.2 0 1 1 1 60 0 0
## tn
## 44
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 39.3 43.395 51.400 56.20 54.78558 58.025 62.085 80.6 6.296068 0
## 1 55.2 55.680 56.775 57.95 58.96500 60.650 64.865 80.6 3.900449 0
## 2 39.3 42.005 46.000 49.65 49.08636 52.700 53.585 54.6 4.058158 0
## P-value: 2.289074e-21
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: CL_S_
## Method: minimize_metric
## Predictor: CL_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 59 45
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 55 0 1 1 1 59 0 0
## tn
## 45
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 30 44.00 50.975 56.1 54.61154 58.325 60.97 84.3 6.622194 0
## 1 55 55.88 56.950 58.2 58.85085 59.800 61.87 84.3 4.067083 0
## 2 30 41.12 46.600 50.2 49.05333 53.100 54.20 54.3 5.015820 0
## P-value: 2.666421e-17
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: RH_D_
## Method: minimize_metric
## Predictor: RH_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 54 50
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 47.1 0 1 1 1 54 0 0
## tn
## 50
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 31.8 33.805 40.775 47.20 46.88750 51.250 58.965 66.8 7.525280 0
## 1 47.1 47.330 50.200 51.15 52.69444 54.175 60.140 66.8 4.390187 0
## 2 31.8 32.090 37.925 40.65 40.61600 45.050 46.420 46.6 4.571132 0
## P-value: 8.841388e-25
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: RH_S_
## Method: minimize_metric
## Predictor: RH_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 51 53
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 47.1 0 1 1 1 51 0 0
## tn
## 53
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 31.5 33.99 41.875 46.55 47.09808 51.00 59.67 83.1 8.256212 0
## 1 47.1 47.75 50.050 51.00 53.39216 54.95 62.90 83.1 6.407241 0
## 2 31.5 32.20 38.400 41.90 41.04151 44.50 46.08 46.6 4.393677 0
## P-value: 4.490518e-19
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: ML_D_
## Method: minimize_metric
## Predictor: ML_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 60 44
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 70.8 0 1 1 1 60 0 0
## tn
## 44
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 53.1 58.335 66.950 72.05 70.78942 75.100 78.57 86.2 6.343698 0
## 1 70.8 71.280 72.775 74.30 75.11500 77.050 79.42 86.2 3.207767 0
## 2 53.1 57.545 62.150 66.35 64.89091 69.025 70.30 70.6 4.535074 0
## P-value: 2.444656e-20
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: ML_S_
## Method: minimize_metric
## Predictor: ML_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 65 39
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 70.9 0 1 1 1 65 0 0
## tn
## 39
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 51.5 58.005 67.35 72.2 70.89038 75.2 78.54 82.7 6.025617 0
## 1 70.9 71.140 72.80 74.0 74.63077 76.1 79.02 82.7 2.644388 0
## 2 51.5 56.820 62.00 66.0 64.65641 68.6 70.21 70.5 4.777072 0
## P-value: 1.312187e-16
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MRH_D_
## Method: minimize_metric
## Predictor: MRH_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 52 52
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 55.2 0 1 1 1 52 0 0
## tn
## 52
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 38.7 44.145 51.200 54.95 55.02115 59.525 65.41 82.8 6.881023 0
## 1 55.2 55.410 57.000 59.55 60.20000 61.850 67.31 82.8 4.759387 0
## 2 38.7 41.420 46.875 51.10 49.84231 53.425 54.60 54.7 4.275619 0
## P-value: 1.9593e-20
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MRH_S_
## Method: minimize_metric
## Predictor: MRH_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 54 50
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 54.9 0 1 1 1 54 0 0
## tn
## 50
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 37.1 44.115 50.950 55.05 54.71538 58.000 64.710 80.4 6.516533 0
## 1 54.9 55.200 56.125 57.90 59.34074 61.575 65.645 80.4 4.552773 0
## 2 37.1 41.435 47.325 50.60 49.72000 52.975 54.510 54.7 4.219440 0
## P-value: 1.972345e-19
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MRB_D_
## Method: minimize_metric
## Predictor: MRB_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 46 58
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 41.2 0 1 1 1 46 0 0
## tn
## 58
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 29.9 33.730 38.100 40.55 41.11154 43.800 48.695 65.5 5.693521 0
## 1 41.2 41.550 42.775 44.00 45.44565 45.150 60.225 65.5 5.450289 0
## 2 29.9 31.805 35.800 38.60 37.67414 40.075 40.715 41.1 2.818209 0
## P-value: 1.375304e-12
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MRB_S_
## Method: minimize_metric
## Predictor: MRB_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 51 53
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 41.3 0 1 1 1 51 0 0
## tn
## 53
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 29.8 33.245 38.2 41.15 41.23894 43.2 47.67 65.4 5.493416 0
## 1 41.3 41.450 42.2 43.20 44.92647 44.6 59.05 65.4 5.160885 0
## 2 29.8 32.680 36.0 38.20 37.69057 40.0 41.10 41.2 2.827561 0
## P-value: 2.70065e-13
## Warning in mean.default(data[[var]], na.rm = TRUE): argument is not numeric or
## logical: returning NA
##
## Variabel MrB_D_ tidak memiliki dua kelas setelah pembagian berdasarkan median.
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MrB_S_
## Method: minimize_metric
## Predictor: MrB_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 50 54
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 31.6 0 1 1 1 50 0 0
## tn
## 54
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 24.9 26.915 29.2 31.10 31.41442 33.100 36.200 41.5 3.171977 0
## 1 31.6 31.900 32.4 33.10 33.95600 34.825 39.585 41.5 2.384830 0
## 2 24.9 26.095 28.2 29.35 29.06111 30.200 31.000 31.2 1.599577 0
## P-value: 2.432362e-20
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MBH_D_
## Method: minimize_metric
## Predictor: MBH_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 62 42
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 27.7 0 1 1 1 62 0 0
## tn
## 42
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 18.8 20.70 25.800 28.20 27.64038 30.225 32.285 34.8 3.589805 0
## 1 27.7 27.81 28.800 29.85 30.01613 31.100 32.600 34.8 1.593758 0
## 2 18.8 20.00 21.475 25.10 24.13333 26.650 27.390 27.5 2.731360 0
## P-value: 1.804622e-18
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MBH_S_
## Method: minimize_metric
## Predictor: MBH_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 104 61 43
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 27.8 0 1 1 1 61 0 0
## tn
## 43
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 19.1 21.415 25.775 28.35 27.71058 30.10 31.985 35.1 3.452196 0
## 1 27.8 28.000 28.900 29.90 30.07377 31.10 32.400 35.1 1.577752 0
## 2 19.1 20.440 22.000 25.00 24.35814 26.65 27.190 27.7 2.460278 0
## P-value: 1.603835e-20
library(tidyverse)
library(cutpointr)
# Daftar variabel yang akan dianalisis
variables <- colnames(data)[3:ncol(data)]
# Fungsi untuk melakukan cut-point analysis pada setiap variabel
cutpoint_analysis <- function(var, data) {
data <- data %>% drop_na(var) # Menghapus baris yang mengandung NA pada variabel yang dianalisis
median_data <- mean(data[[var]], na.rm = TRUE)
data$class <- ifelse(data[[var]] > median_data, 1, 2)
if (length(unique(data$class)) == 2) { # Pastikan ada dua kelas
opt_cut <- cutpointr(data, !!sym(var), class, method = minimize_metric, metric = misclassification_cost, na.rm = TRUE)
cat("\nAnalisis untuk variabel:", var, "\n")
print(summary(opt_cut))
plot_obj = plot_metric(opt_cut)
print(plot_obj)
# Hitung p-value menggunakan uji statistik yang sesuai
# Contoh: uji t-test untuk membandingkan rata-rata kedua kelompok
p_value <- t.test(data[[var]] ~ data$class)$p.value
cat("P-value:", p_value, "\n")
} else {
cat("\nVariabel", var, "tidak memiliki dua kelas setelah pembagian berdasarkan median.\n")
}
}
# Melakukan analisis untuk setiap variabel
for (var in variables) {
cutpoint_analysis(var, data_laki_laki)
}
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: BB1
## Method: minimize_metric
## Predictor: BB1
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 71 69
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 122.3 0 1 1 1 71 0 0
## tn
## 69
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## Overall 100.9 108.295 117.075 122.4 122.2147 127.325 133.45 138.6 7.718174
## 1 122.3 123.950 125.800 127.3 128.3980 130.000 135.25 138.6 3.597827
## 2 100.9 105.540 113.700 117.0 115.8522 119.900 121.60 122.1 5.238899
## NAs
## 0
## 0
## 0
## P-value: 1.335152e-32
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: BB2
## Method: minimize_metric
## Predictor: BB2
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 73 67
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 97.9 0 1 1 1 73 0 0
## tn
## 67
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## Overall 84.0 87.475 92.275 98.5 97.71643 101.925 106.505 124.8 6.341284
## 1 97.9 98.660 100.200 101.9 102.59589 104.000 107.480 124.8 3.880952
## 2 84.0 85.610 90.650 92.1 92.40000 96.250 97.100 97.7 3.637390
## NAs
## 0
## 0
## 0
## P-value: 2.404664e-33
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: BB3
## Method: minimize_metric
## Predictor: BB3
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 34 106
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 105 0 1 1 1 34 0 0
## tn
## 106
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## Overall 76.6 81.065 95.450 98.80 104.97643 104.800 113.225 971.0 74.18903
## 1 105.0 105.100 105.425 107.45 134.09706 112.250 117.090 971.0 147.92529
## 2 76.6 80.250 92.800 97.05 95.63585 100.775 103.550 104.8 6.81370
## NAs
## 0
## 0
## 0
## P-value: 0.1391397
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MA_D_
## Method: minimize_metric
## Predictor: MA_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 80 60
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 125.2 0 1 1 1 80 0 0
## tn
## 60
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## Overall 29.7 111.970 120.275 126.65 124.6321 130.600 135.61 141.6 11.213560
## 1 125.2 125.700 127.775 129.95 130.7512 133.875 136.11 141.6 3.582482
## 2 29.7 100.395 115.475 119.60 116.4733 121.550 123.91 124.5 12.672576
## NAs
## 0
## 0
## 0
## P-value: 3.671945e-12
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MA_S_
## Method: minimize_metric
## Predictor: MA_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 79 61
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 125.8 0 1 1 1 79 0 0
## tn
## 61
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## Overall 100.3 114.065 120.90 126.2 125.7293 130.80 135.725 145.3 7.625700
## 1 125.8 126.090 127.85 130.0 130.9848 133.95 137.370 145.3 3.985296
## 2 100.3 108.100 116.40 120.5 118.9230 122.80 125.200 125.6 5.532130
## NAs
## 0
## 0
## 0
## P-value: 1.464366e-26
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: RL_D_
## Method: minimize_metric
## Predictor: RL_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 74 66
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 58.2 0 1 1 1 74 0 0
## tn
## 66
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 39.9 44.090 53.975 58.35 58.18857 62.275 68.030 128.1 9.116260 0
## 1 58.2 58.365 59.825 61.95 63.60811 64.875 70.040 128.1 8.468795 0
## 2 39.9 41.475 48.525 53.55 52.11212 55.875 57.775 58.1 5.122725 0
## P-value: 3.679777e-17
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: RL_S_
## Method: minimize_metric
## Predictor: RL_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 77 63
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 57.8 0 1 1 1 77 0 0
## tn
## 63
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 39.9 42.995 54.05 59.1 57.75500 61.9 67.12 88.6 7.317647 0
## 1 57.8 58.600 59.90 61.3 62.71688 64.4 68.96 88.6 4.507024 0
## 2 39.9 42.030 47.70 53.2 51.69048 56.1 57.29 57.7 5.215600 0
## P-value: 2.141742e-25
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: CRH_D_
## Method: minimize_metric
## Predictor: CRH_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 76 64
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 62.3 0 1 1 1 76 0 0
## tn
## 64
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 43.5 48.995 57.925 63.40 62.21929 66.600 73.125 84.8 7.185176 0
## 1 62.3 63.125 64.550 66.40 67.35132 68.775 75.550 84.8 4.067153 0
## 2 43.5 48.075 52.275 57.15 56.12500 60.525 61.900 62.0 4.971091 0
## P-value: 3.701794e-28
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: CRH_S_
## Method: minimize_metric
## Predictor: CRH_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 77 63
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 61.7 0 1 1 1 77 0 0
## tn
## 63
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 43.9 49.59 57.90 62.8 61.63286 66.175 70.515 78.8 6.638675 0
## 1 61.7 62.52 64.10 66.0 66.43377 68.300 71.120 78.8 3.107614 0
## 2 43.9 47.49 51.95 57.6 55.76508 60.150 60.990 61.5 4.832957 0
## P-value: 8.365934e-28
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: CL_D_
## Method: minimize_metric
## Predictor: CL_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 79 61
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 60.7 0 1 1 1 79 0 0
## tn
## 61
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 42.1 48.145 56.75 61.55 60.62714 64.95 70.145 80.6 6.850141 0
## 1 60.7 61.190 62.95 64.50 65.37342 66.75 71.310 80.6 3.641981 0
## 2 42.1 46.700 50.70 55.30 54.48033 58.40 60.400 60.6 4.836453 0
## P-value: 1.792543e-27
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: CL_S_
## Method: minimize_metric
## Predictor: CL_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 78 62
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 60.5 0 1 1 1 78 0 0
## tn
## 62
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 42.9 47.975 56.150 61.50 60.18929 64.900 68.61 84.3 6.830167 0
## 1 60.5 61.085 63.000 64.40 65.07436 66.450 69.42 84.3 3.521564 0
## 2 42.9 45.385 50.425 54.85 54.04355 57.975 59.69 60.0 4.663202 0
## P-value: 2.084898e-29
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: RH_D_
## Method: minimize_metric
## Predictor: RH_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 61 79
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 52.4 0 1 1 1 61 0 0
## tn
## 79
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 29.3 36.865 45.5 51.45 52.30214 59.45 66.115 72.4 9.261877 0
## 1 52.4 54.400 56.7 61.00 60.98525 65.40 67.600 72.4 4.944486 0
## 2 29.3 35.440 43.1 47.10 45.59747 50.25 51.730 52.2 5.437806 0
## P-value: 2.094998e-36
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: RH_S_
## Method: minimize_metric
## Predictor: RH_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 65 75
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 52.7 0 1 1 1 65 0 0
## tn
## 75
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 33.3 36.895 46.375 51.65 52.52786 60.125 66.205 83.1 9.163378 0
## 1 52.7 53.020 56.200 60.30 60.49538 64.000 67.880 83.1 5.498052 0
## 2 33.3 35.680 42.200 46.90 45.62267 50.050 51.830 52.5 5.243312 0
## P-value: 1.536512e-33
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: ML_D_
## Method: minimize_metric
## Predictor: ML_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 75 65
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 74.4 0 1 1 1 75 0 0
## tn
## 65
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 44.7 59.785 71.45 75.1 74.39571 79.05 86.535 95.0 7.964118 0
## 1 74.4 75.010 76.00 78.5 79.82267 81.75 89.000 95.0 4.749782 0
## 2 44.7 57.440 64.20 71.1 68.13385 72.20 73.800 74.3 6.110065 0
## P-value: 1.826903e-23
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: ML_S_
## Method: minimize_metric
## Predictor: ML_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 76 64
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 74.6 0 1 1 1 76 0 0
## tn
## 64
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 41.2 60.975 71.375 75.00 74.53000 78.550 85.905 96.0 7.564492 0
## 1 74.6 74.975 76.375 78.25 79.62237 82.325 86.350 96.0 4.174002 0
## 2 41.2 58.890 65.275 71.00 68.48281 72.825 73.785 74.4 6.089141 0
## P-value: 1.704684e-22
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MRH_D_
## Method: minimize_metric
## Predictor: MRH_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 74 66
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 62 0 1 1 1 74 0 0
## tn
## 66
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 43 48.730 57.95 62.55 61.90143 66.950 72.265 82.8 7.396003 0
## 1 62 62.595 64.50 66.90 67.42703 69.200 74.290 82.8 3.976241 0
## 2 43 46.125 52.10 56.75 55.70606 59.975 61.500 61.7 5.048408 0
## P-value: 6.929898e-30
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MRH_S_
## Method: minimize_metric
## Predictor: MRH_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 74 66
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 61.9 0 1 1 1 74 0 0
## tn
## 66
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 43.2 47.570 57.875 62.65 61.57571 66.700 71.620 80.4 7.248953 0
## 1 61.9 62.665 64.625 66.55 67.04054 69.075 73.065 80.4 3.627668 0
## 2 43.2 46.200 52.100 57.05 55.44848 59.675 61.200 61.5 5.046865 0
## P-value: 5.762605e-30
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MRB_D_
## Method: minimize_metric
## Predictor: MRB_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 66 74
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 44.1 0 1 1 1 66 0 0
## tn
## 74
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 33.3 34.990 40.000 43.65 44.00714 46.425 53.065 73.8 6.733826 0
## 1 44.1 44.325 45.225 46.70 48.90758 49.950 66.075 73.8 6.365975 0
## 2 33.3 33.865 37.225 40.05 39.63649 41.900 43.605 43.9 3.029923 0
## P-value: 6.044914e-18
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MRB_S_
## Method: minimize_metric
## Predictor: MRB_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 56 84
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 44.8 0 1 1 1 56 0 0
## tn
## 84
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 33.7 35.295 40.775 43.80 44.71786 46.625 58.75 94.1 8.350137 0
## 1 44.8 44.875 45.875 47.50 51.11607 50.450 70.30 94.1 9.575799 0
## 2 33.7 34.660 37.800 41.15 40.45238 42.900 44.30 44.7 3.157835 0
## P-value: 2.940462e-11
## Warning in mean.default(data[[var]], na.rm = TRUE): argument is not numeric or
## logical: returning NA
##
## Variabel MrB_D_ tidak memiliki dua kelas setelah pembagian berdasarkan median.
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MrB_S_
## Method: minimize_metric
## Predictor: MrB_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 64 76
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 33.4 0 1 1 1 64 0 0
## tn
## 76
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 22.6 28.000 30.6 32.90 33.33000 35.525 40.500 45.4 4.078893 0
## 1 33.4 33.415 34.1 35.85 36.78906 38.900 41.665 45.4 3.015732 0
## 2 22.6 27.000 29.2 30.80 30.41711 31.900 33.025 33.3 2.094462 0
## P-value: 1.074643e-26
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MBH_D_
## Method: minimize_metric
## Predictor: MBH_D_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 80 60
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 29.8 0 1 1 1 80 0 0
## tn
## 60
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 20.4 23.600 27.700 30.20 29.74357 32.325 35.105 39.4 3.642203 0
## 1 29.8 29.895 30.750 31.70 32.28875 33.225 36.305 39.4 1.949034 0
## 2 20.4 21.900 24.475 26.85 26.35000 28.300 29.215 29.6 2.381461 0
## P-value: 3.382794e-30
## Assuming the positive class is 1
## Assuming the positive class has higher x values
##
## Analisis untuk variabel: MBH_S_
## Method: minimize_metric
## Predictor: MBH_S_
## Outcome: class
## Direction: >=
##
## AUC n n_pos n_neg
## 1 140 74 66
##
## optimal_cutpoint misclassification_cost acc sensitivity specificity tp fn fp
## 29.8 0 1 1 1 74 0 0
## tn
## 66
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 20.2 23.475 27.850 29.90 29.70143 32.825 34.605 36.4 3.568628 0
## 1 29.8 29.900 31.050 32.50 32.44865 33.750 35.335 36.4 1.675467 0
## 2 20.2 22.075 24.825 27.45 26.62121 28.775 29.200 29.4 2.419696 0
## P-value: 1.02997e-31
# Load necessary libraries
library(ggplot2)
library(readxl)
library(dplyr)
library(rlang)
##
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice
# Load the dataset
file_path <- "/home/taufiq/Documents/freelancer/kak qardawi/project baru/data/data.xlsx" # Adjust the path as needed
data <- read_excel(file_path)
# Select relevant columns
selected_columns <- c("Usia", "MRB(D)", "MRB(S)", "MRH(D)", "MRH(S)", "MrB(D)", "MBH(S)", "MrB(S)")
# Convert all selected columns to numeric, handling non-numeric values as NA
data[selected_columns[-1]] <- lapply(data[selected_columns[-1]], function(x) as.numeric(as.character(x)))
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
# Create individual line plots for each variable
for (variable in selected_columns[-1]) {
p <- ggplot(data, aes(x = Usia, y = !!sym(variable))) +
geom_line(color = "blue") +
labs(title = paste("Line Plot of", variable, "by Age"),
x = "Age",
y = variable) +
theme_minimal()
# Save the plot
ggsave(filename = paste0("plot_", gsub("[()]", "", variable), ".png"), plot = p, width = 8, height = 6)
}