admgojo
2024-10-28
### One Way MANOVA
data1 <- read.csv("D:/UNPAD/Semester 3/Analisis Data Multivariat 1/rokokk.csv")
data1## Year Age_Group Gender Smoking_Prevalence Drug_Experimentation
## 1 2024 15-19 Both 18.85 32.40
## 2 2024 10-14 Female 34.88 41.57
## 3 2023 10-14 Both 42.00 56.80
## 4 2024 40-49 Both 33.75 42.90
## 5 2023 15-19 Male 47.90 39.62
## 6 2022 70-79 Male 20.14 47.29
## 7 2021 30-39 Female 38.38 26.60
## 8 2022 10-14 Female 7.87 54.67
## 9 2021 70-79 Both 11.61 63.65
## 10 2020 60-69 Female 23.98 17.62
## 11 2020 40-49 Male 42.85 66.66
## 12 2022 50-59 Both 48.72 11.41
## 13 2024 25-29 Male 37.71 19.46
## 14 2022 60-69 Female 44.99 42.47
## 15 2020 15-19 Male 11.78 65.31
## 16 2020 60-69 Male 41.94 62.21
## 17 2023 30-39 Both 49.99 57.28
## 18 2024 15-19 Female 24.61 39.62
## 19 2021 30-39 Female 15.08 22.28
## 20 2021 15-19 Male 15.40 30.66
## 21 2023 30-39 Both 15.17 45.36
## 22 2022 70-79 Male 10.49 50.57
## 23 2021 25-29 Female 9.95 67.80
## 24 2020 60-69 Male 26.59 14.25
## 25 2022 15-19 Male 18.41 15.59
## 26 2023 15-19 Female 46.00 32.20
## 27 2020 10-14 Both 14.80 69.46
## 28 2022 60-69 Male 12.27 18.92
## 29 2023 80+ Male 6.51 10.85
## Socioeconomic_Status Peer_Influence School_Programs Family_Background
## 1 High 5 Yes 1
## 2 High 6 Yes 10
## 3 High 6 Yes 2
## 4 Middle 10 No 9
## 5 High 1 No 2
## 6 High 10 Yes 7
## 7 Low 4 No 7
## 8 Middle 5 Yes 9
## 9 Low 9 Yes 1
## 10 Low 2 No 3
## 11 High 5 No 5
## 12 High 2 No 8
## 13 Low 5 Yes 2
## 14 High 8 No 4
## 15 Low 9 No 1
## 16 Low 5 Yes 9
## 17 High 2 Yes 9
## 18 Middle 10 No 8
## 19 High 1 Yes 6
## 20 Low 3 Yes 7
## 21 Low 6 Yes 4
## 22 Middle 6 Yes 9
## 23 High 3 Yes 4
## 24 High 2 Yes 3
## 25 Middle 7 No 3
## 26 High 1 No 7
## 27 Middle 3 No 6
## 28 Middle 9 Yes 7
## 29 Middle 6 Yes 5
## Mental_Health Access_to_Counseling Parental_Supervision Substance_Education
## 1 5 No 4 No
## 2 5 No 9 Yes
## 3 7 Yes 2 No
## 4 7 Yes 2 No
## 5 4 Yes 4 No
## 6 4 No 4 No
## 7 1 Yes 2 No
## 8 2 No 4 No
## 9 6 Yes 5 Yes
## 10 6 Yes 5 Yes
## 11 3 No 9 No
## 12 3 Yes 10 No
## 13 4 Yes 1 No
## 14 1 No 5 Yes
## 15 10 No 2 Yes
## 16 9 Yes 2 No
## 17 3 Yes 4 No
## 18 4 Yes 8 No
## 19 8 No 7 Yes
## 20 5 No 5 Yes
## 21 3 No 10 Yes
## 22 2 No 2 Yes
## 23 7 Yes 6 No
## 24 6 No 10 No
## 25 9 Yes 6 No
## 26 1 Yes 3 No
## 27 10 Yes 8 No
## 28 10 Yes 6 Yes
## 29 10 Yes 9 Yes
## Community_Support Media_Influence
## 1 3 1
## 2 9 3
## 3 5 1
## 4 10 9
## 5 10 3
## 6 4 2
## 7 4 10
## 8 4 2
## 9 2 6
## 10 9 9
## 11 2 10
## 12 10 4
## 13 10 8
## 14 3 2
## 15 9 1
## 16 1 4
## 17 8 1
## 18 6 8
## 19 2 6
## 20 9 7
## 21 4 6
## 22 7 8
## 23 5 3
## 24 3 5
## 25 8 4
## 26 5 10
## 27 8 8
## 28 1 10
## 29 5 6head(data1)## Year Age_Group Gender Smoking_Prevalence Drug_Experimentation
## 1 2024 15-19 Both 18.85 32.40
## 2 2024 10-14 Female 34.88 41.57
## 3 2023 10-14 Both 42.00 56.80
## 4 2024 40-49 Both 33.75 42.90
## 5 2023 15-19 Male 47.90 39.62
## 6 2022 70-79 Male 20.14 47.29
## Socioeconomic_Status Peer_Influence School_Programs Family_Background
## 1 High 5 Yes 1
## 2 High 6 Yes 10
## 3 High 6 Yes 2
## 4 Middle 10 No 9
## 5 High 1 No 2
## 6 High 10 Yes 7
## Mental_Health Access_to_Counseling Parental_Supervision Substance_Education
## 1 5 No 4 No
## 2 5 No 9 Yes
## 3 7 Yes 2 No
## 4 7 Yes 2 No
## 5 4 Yes 4 No
## 6 4 No 4 No
## Community_Support Media_Influence
## 1 3 1
## 2 9 3
## 3 5 1
## 4 10 9
## 5 10 3
## 6 4 2Dataset ini memberikan gambaran lengkap tentang tren merokok dan eksperimen narkoba pada remaja dari tahun ke tahun. Dataset ini mencakup berbagai faktor demografis seperti kelompok umur, jenis kelamin, status sosial ekonomi, dan pengaruh dari keluarga dan teman sebaya.
X1 = data1[,5]
X2 = data1[,10]
data1_fix = data.frame(X1,X2)
data1_fix## X1 X2
## 1 32.40 5
## 2 41.57 5
## 3 56.80 7
## 4 42.90 7
## 5 39.62 4
## 6 47.29 4
## 7 26.60 1
## 8 54.67 2
## 9 63.65 6
## 10 17.62 6
## 11 66.66 3
## 12 11.41 3
## 13 19.46 4
## 14 42.47 1
## 15 65.31 10
## 16 62.21 9
## 17 57.28 3
## 18 39.62 4
## 19 22.28 8
## 20 30.66 5
## 21 45.36 3
## 22 50.57 2
## 23 67.80 7
## 24 14.25 6
## 25 15.59 9
## 26 32.20 1
## 27 69.46 10
## 28 18.92 10
## 29 10.85 10library(MVN)## Warning: package 'MVN' was built under R version 4.3.3head(data1_fix)## X1 X2
## 1 32.40 5
## 2 41.57 5
## 3 56.80 7
## 4 42.90 7
## 5 39.62 4
## 6 47.29 4test = mvn(data1_fix, mvnTest = "mardia", univariateTest = "SW", multivariatePlot = "qq")
test## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 3.4470283211295 0.48597837918879 YES
## 2 Mardia Kurtosis -1.29362706590044 0.195794268838087 YES
## 3 MVN <NA> <NA> YES
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk X1 0.9387 0.0929 YES
## 2 Shapiro-Wilk X2 0.9342 0.0706 YES
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew Kurtosis
## X1 29 40.188966 18.938908 41.57 10.85 69.46 22.28 56.8 -0.01608223 -1.389012
## X2 29 5.344828 2.918853 5.00 1.00 10.00 3.00 7.0 0.21081617 -1.212950Berdasarkan hasil uji normal multivariat, data berdistribusi normal karena hasil p value Mardia Skewness dan Mardia Kurtosis lebih alpha (0,05).
## Uji Homogenitas Multivariate
library(biotools)## Warning: package 'biotools' was built under R version 4.3.3## Loading required package: MASS## ---
## biotools version 4.2grup <- data1$Gender
head(grup)## [1] "Both" "Female" "Both" "Both" "Male" "Male"boxM(data = data1_fix, grouping = grup)##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: data1_fix
## Chi-Sq (approx.) = 4.1522, df = 6, p-value = 0.6561P-value lebih besar dari tingkat signifikansi (0,05), data ini homogen
owm = manova(cbind(data1_fix$X1, data1_fix$X2)~data1$Gender[1:29])
summary(owm)## Df Pillai approx F num Df den Df Pr(>F)
## data1$Gender[1:29] 2 0.18846 1.3524 4 52 0.2632
## Residuals 26P-value 0.2632 lebih besar dari tingkat signifikansi (0.05), maka ada perbedaan signifikan dalam variabel dependen yang dipertimbangkan berdasarkan jenis kelamin. Dengan kata lain, jenis kelamin tidak memiliki pengaruh yang signifikan terhadap variabel yang dianalisis dalam data ini.
### TWO WAY MANOVA
library(openxlsx)## Warning: package 'openxlsx' was built under R version 4.3.3data2 <- read.xlsx("rokokk22.xlsx")
data2## Access_to_Counseling Smoking_Prevalence Drug_Experimentation Mental_Health
## 1 0 18.85 32.4 5
## 2 0 34.88 41.57 5
## 3 1 42.0 56.8 7
## 4 1 33.75 42.9 7
## 5 1 47.9 39.62 4
## 6 0 20.14 47.29 4
## 7 1 38.38 26.6 1
## 8 0 7.87 54.67 2
## 9 1 11.61 63.65 6
## 10 1 23.98 17.62 6
## 11 0 42.85 66.66 3
## 12 1 48.72 11.41 3
## 13 1 37.71 19.46 4
## 14 0 44.99 42.47 1
## 15 0 11.78 65.31 10
## 16 1 41.94 62.21 9
## 17 1 49.99 57.28 3
## 18 1 24.61 39.62 4
## 19 0 15.08 22.28 8
## 20 0 15.4 30.66 5
## 21 0 15.17 45.36 3
## 22 0 10.49 50.57 2
## 23 1 9.95 67.8 7
## 24 0 26.59 14.25 6
## 25 1 18.41 15.59 9
## 26 1 46.0 32.2 1
## 27 1 14.8 69.46 10
## 28 1 12.27 18.92 10
## 29 1 6.51 10.85 10
## School_Programs
## 1 1
## 2 1
## 3 1
## 4 0
## 5 0
## 6 1
## 7 0
## 8 1
## 9 1
## 10 0
## 11 0
## 12 0
## 13 1
## 14 0
## 15 0
## 16 1
## 17 1
## 18 0
## 19 1
## 20 1
## 21 1
## 22 1
## 23 1
## 24 1
## 25 0
## 26 0
## 27 0
## 28 1
## 29 1head(data2)## Access_to_Counseling Smoking_Prevalence Drug_Experimentation Mental_Health
## 1 0 18.85 32.4 5
## 2 0 34.88 41.57 5
## 3 1 42.0 56.8 7
## 4 1 33.75 42.9 7
## 5 1 47.9 39.62 4
## 6 0 20.14 47.29 4
## School_Programs
## 1 1
## 2 1
## 3 1
## 4 0
## 5 0
## 6 1data2$Smoking_Prevalence <- as.numeric(data2$Smoking_Prevalence)
data2$Drug_Experimentation <- as.numeric(data2$Drug_Experimentation)x11 <- data2$Smoking_Prevalence
x22 <- data2$Drug_Experimentation
x33 <- data2$Mental_Health
data2_fix <- data.frame(x1=x11, x2=x22, x3=x33);data2_fix## x1 x2 x3
## 1 18.85 32.40 5
## 2 34.88 41.57 5
## 3 42.00 56.80 7
## 4 33.75 42.90 7
## 5 47.90 39.62 4
## 6 20.14 47.29 4
## 7 38.38 26.60 1
## 8 7.87 54.67 2
## 9 11.61 63.65 6
## 10 23.98 17.62 6
## 11 42.85 66.66 3
## 12 48.72 11.41 3
## 13 37.71 19.46 4
## 14 44.99 42.47 1
## 15 11.78 65.31 10
## 16 41.94 62.21 9
## 17 49.99 57.28 3
## 18 24.61 39.62 4
## 19 15.08 22.28 8
## 20 15.40 30.66 5
## 21 15.17 45.36 3
## 22 10.49 50.57 2
## 23 9.95 67.80 7
## 24 26.59 14.25 6
## 25 18.41 15.59 9
## 26 46.00 32.20 1
## 27 14.80 69.46 10
## 28 12.27 18.92 10
## 29 6.51 10.85 10test = mvn(data2_fix, mvnTest = "mardia", univariateTest = "SW", multivariatePlot = "qq")
test## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 10.0454583600137 0.436514161462383 YES
## 2 Mardia Kurtosis -1.5454121752046 0.122246597202847 YES
## 3 MVN <NA> <NA> YES
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk x1 0.8981 0.0088 NO
## 2 Shapiro-Wilk x2 0.9387 0.0929 YES
## 3 Shapiro-Wilk x3 0.9342 0.0706 YES
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew Kurtosis
## x1 29 26.642069 14.603796 23.98 6.51 49.99 14.80 41.94 0.23383309 -1.581631
## x2 29 40.188966 18.938908 41.57 10.85 69.46 22.28 56.80 -0.01608223 -1.389012
## x3 29 5.344828 2.918853 5.00 1.00 10.00 3.00 7.00 0.21081617 -1.212950Berdasarkan hasil uji normal multivariat, data berdistribusi normal karena hasil p value Mardia Skewness dan Mardia Kurtosis lebih alpha (0,05).
grop <- data2$Access_to_Counseling
head(grop)## [1] 0 0 1 1 1 0boxM(data = data2_fix, grouping = grop)##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: data2_fix
## Chi-Sq (approx.) = 3.1404, df = 6, p-value = 0.791grup <- data2$School_Programs
head(grup)## [1] 1 1 1 0 0 1boxM(data = data2_fix, grouping = grup)##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: data2_fix
## Chi-Sq (approx.) = 11.841, df = 6, p-value = 0.06561Karena p-value lebih besar dari tingkat signifikansi (0,05), maka data tersebut memenuhi asumsi homogenitas.
## Two Way MANOVA
Access_to_Counseling <- as.factor(data2$Access_to_Counseling)
School_Programs <- as.factor(data2$School_Programs)
manova <- manova(cbind(x11, x22, x33) ~ Access_to_Counseling * School_Programs, data = data2)
# Menampilkan hasil
summary(manova)## Df Pillai approx F num Df den Df Pr(>F)
## Access_to_Counseling 1 0.27291 2.87765 3 23 0.05801
## School_Programs 1 0.10609 0.90988 3 23 0.45156
## Access_to_Counseling:School_Programs 1 0.26078 2.70465 3 23 0.06895
## Residuals 25
##
## Access_to_Counseling .
## School_Programs
## Access_to_Counseling:School_Programs .
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1- Access to Counseling
Nilai p sebesar 0.05801 menunjukkan bahwa ada perbedaan yang signifikan dalam variabel dependen.
- School Programs
Nilai p sebesar 0.45156 menunjukkan tidak ada perbedaan signifikan dalam variabel dependen.
- Access_to_Counseling:School_Programs
Nilai p sebesar 0.06895 menunjukkan adanya untuk interaksi antara Access to Counseling dan School Programs.