adm1
2024-10-28
### One Way MANOVA
data1 <- read.csv("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 2
X1 = data1[,5]
X2 = data1[,10]
data1_fix = data.frame(X1,X2)
library(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
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew Kurtosis
## X1 29 40.188966 18.938908 41.57 10.85 69.46 22.28 56.8 -0.01608223 -1.389012
## X2 29 5.344828 2.918853 5.00 1.00 10.00 3.00 7.0 0.21081617 -1.212950## Uji Homogenitas Multivariate
library(biotools)## Warning: package 'biotools' was built under R version 4.3.3## Loading required package: MASS## ---
## biotools version 4.2grup <- data1$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 = 0.6561u
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 26### 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
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew Kurtosis
## x1 29 26.642069 14.603796 23.98 6.51 49.99 14.80 41.94 0.23383309 -1.581631
## x2 29 40.188966 18.938908 41.57 10.85 69.46 22.28 56.80 -0.01608223 -1.389012
## x3 29 5.344828 2.918853 5.00 1.00 10.00 3.00 7.00 0.21081617 -1.212950grop <- data2$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$School_Programs
head(grup)## [1] 1 1 1 0 0 1boxM(data = data2_fix, grouping = grup)##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: data2_fix
## Chi-Sq (approx.) = 11.841, df = 6, p-value = 0.06561## Two Way MANOVA
Access_to_Counseling <- as.factor(data2$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