### One Way MANOVA
library(readr)
## Warning: package 'readr' was built under R version 4.3.3
library(openxlsx)
## Warning: package 'openxlsx' was built under R version 4.3.3
data1 <- read.xlsx("C:/Users/ADVAN/Downloads/tugas prak one way.xlsx")
head(data1)
## Panjang Lebar Tinggi JK Jenis.Kelamin
## 1 98 81 38 Betina 0
## 2 103 84 38 Betina 0
## 3 103 86 42 Betina 0
## 4 105 86 42 Betina 0
## 5 109 88 44 Betina 0
## 6 123 92 50 Betina 0
## Uji Normalitas
library(MVN)
## Warning: package 'MVN' was built under R version 4.3.3
data1_fix <- data1[1:3]
head(data1_fix)
## Panjang Lebar Tinggi
## 1 98 81 38
## 2 103 84 38
## 3 103 86 42
## 4 105 86 42
## 5 109 88 44
## 6 123 92 50
test = mvn(data1_fix, mvnTest = "mardia", univariateTest = "SW", multivariatePlot = "qq")
test
## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 20.1644378890549 0.0277351889469023 NO
## 2 Mardia Kurtosis -0.185309113808547 0.85298659004483 YES
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk Panjang 0.9622 0.1244 YES
## 2 Shapiro-Wilk Lebar 0.9496 0.0386 NO
## 3 Shapiro-Wilk Tinggi 0.9186 0.0027 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew Kurtosis
## Panjang 48 124.7083 20.494896 122.0 93 177 106.75 136.5 0.4631205 -0.6205144
## Lebar 48 95.4375 12.675838 93.0 74 132 86.00 102.0 0.7898060 0.1619307
## Tinggi 48 46.3750 8.365647 44.5 35 67 40.00 51.0 0.7491616 -0.4786118
# Penanganan Normalitas : potong data (khusus untuk pembelajaran)
data1_new = data1[1:40,]
data1_fix1 = data1_new[1:3]
library(MVN)
head(data1_new)
## Panjang Lebar Tinggi JK Jenis.Kelamin
## 1 98 81 38 Betina 0
## 2 103 84 38 Betina 0
## 3 103 86 42 Betina 0
## 4 105 86 42 Betina 0
## 5 109 88 44 Betina 0
## 6 123 92 50 Betina 0
test = mvn(data1_fix1, mvnTest = "mardia", univariateTest = "SW", multivariatePlot = "qq")
test
## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 15.2096294813749 0.124604001114039 YES
## 2 Mardia Kurtosis -0.444213783366251 0.656888027929549 YES
## 3 MVN <NA> <NA> YES
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk Panjang 0.9382 0.0300 NO
## 2 Shapiro-Wilk Lebar 0.9396 0.0335 NO
## 3 Shapiro-Wilk Tinggi 0.9100 0.0038 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew Kurtosis
## Panjang 40 124.475 22.372130 118.0 93 177 104.75 138.75 0.4552497 -0.9720242
## Lebar 40 95.475 13.761685 91.5 74 132 84.75 102.75 0.7242198 -0.2866578
## Tinggi 40 46.800 9.072924 43.5 35 67 39.00 51.50 0.5797759 -0.9421718
## Uji Homogenitas Multivariate
library(biotools)
## Warning: package 'biotools' was built under R version 4.3.3
## Loading required package: MASS
## ---
## biotools version 4.2
grup <- data1_new$Jenis.Kelamin
head(grup)
## [1] 0 0 0 0 0 0
boxM(data = data1_fix1, grouping = grup)
##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: data1_fix1
## Chi-Sq (approx.) = 26.742, df = 6, p-value = 0.0001618
## One Way MANOVA
owm = manova(cbind(data1_new$Panjang, data1_new$Lebar, data1_new$Tinggi)~data1_new$Jenis.Kelamin)
summary(owm)
## Df Pillai approx F num Df den Df Pr(>F)
## data1_new$Jenis.Kelamin 1 0.58176 16.692 3 36 5.82e-07 ***
## Residuals 38
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Uji Lanjut (Post Hoc Test)
summary.aov(owm)
## Response 1 :
## Df Sum Sq Mean Sq F value Pr(>F)
## data1_new$Jenis.Kelamin 1 8027.3 8027.3 26.542 8.254e-06 ***
## Residuals 38 11492.7 302.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response 2 :
## Df Sum Sq Mean Sq F value Pr(>F)
## data1_new$Jenis.Kelamin 1 3031.7 3031.70 26.458 8.467e-06 ***
## Residuals 38 4354.3 114.59
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response 3 :
## Df Sum Sq Mean Sq F value Pr(>F)
## data1_new$Jenis.Kelamin 1 1648.5 1648.5 40.107 1.99e-07 ***
## Residuals 38 1561.9 41.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
### TWO WAY MANOVA
library(openxlsx)
data2 <- read.xlsx("C:/Users/ADVAN/Downloads/Guilhot_Symbiosis_2019_MANOVA_dataset.xlsx")
head(data2)
## Environment1 Bacterial_isolate_inoculated1 Environment
## 1 artificial Dm1 0
## 2 artificial Dm1 0
## 3 artificial Dm1 0
## 4 artificial Dm1 0
## 5 artificial Dm1 0
## 6 artificial Dm1 0
## Bacterial_isolate_inoculated
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## Mean(Log.Optical.density.per.individual.relative.to.controls.of.the.same.environment)
## 1 0.28230335
## 2 0.00721307
## 3 -0.01617238
## 4 -0.08373828
## 5 0.25919509
## 6 0.09552793
## Mean(Age.per.individual.relative.to.controls.of.the.same.environment)
## 1 -0.3934855
## 2 -0.5601522
## 3 -0.0434855
## 4 -0.1990411
## 5 0.2398478
## 6 -0.6101522
## Uji Normalitas Multivariate
x1 <- data2[,5]
x2 <- data2[,6]
data2_fix <- data.frame(x1=x1, x2=x2)
library(MVN)
test = mvn(data2_fix, mvnTest = "mardia", univariateTest = "SW", multivariatePlot = "qq")
test
## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 19.3948947968481 0.000657252529131105 NO
## 2 Mardia Kurtosis 4.05703699451687 4.96992121823414e-05 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk x1 0.9906 0.7442 YES
## 2 Shapiro-Wilk x2 0.9565 0.0031 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th
## x1 95 -0.01507748 0.1724739 -0.002187557 -0.439811 0.5339313 -0.1213662
## x2 95 -0.25901832 1.2776969 -0.262034333 -2.928701 4.3212990 -1.2513710
## 75th Skew Kurtosis
## x1 0.09930286 0.1698695 0.4054636
## x2 0.45254311 0.7107731 1.3369359
# Penanganan Normalitas : Pemotongan data (khusus untuk pembelajaran)
data2_new <- data2[1:50,]
data2_fix1 <- data2_new[5:6]
library(MVN)
test = mvn(data2_fix1,mvnTest = "mardia", univariateTest = "SW", multivariatePlot = "qq")
test
## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 1.26321287482413 0.867585369377264 YES
## 2 Mardia Kurtosis -1.60367906494181 0.108784812810945 YES
## 3 MVN <NA> <NA> YES
##
## $univariateNormality
## Test
## 1 Shapiro-Wilk
## 2 Shapiro-Wilk
## Variable
## 1 Mean(Log.Optical.density.per.individual.relative.to.controls.of.the.same.environment)
## 2 Mean(Age.per.individual.relative.to.controls.of.the.same.environment)
## Statistic p value Normality
## 1 0.9806 0.5757 YES
## 2 0.9404 0.0140 NO
##
## $Descriptives
## n
## Mean(Log.Optical.density.per.individual.relative.to.controls.of.the.same.environment) 50
## Mean(Age.per.individual.relative.to.controls.of.the.same.environment) 50
## Mean
## Mean(Log.Optical.density.per.individual.relative.to.controls.of.the.same.environment) -0.05885554
## Mean(Age.per.individual.relative.to.controls.of.the.same.environment) -0.68031198
## Std.Dev
## Mean(Log.Optical.density.per.individual.relative.to.controls.of.the.same.environment) 0.1546923
## Mean(Age.per.individual.relative.to.controls.of.the.same.environment) 0.9192458
## Median
## Mean(Log.Optical.density.per.individual.relative.to.controls.of.the.same.environment) -0.07700081
## Mean(Age.per.individual.relative.to.controls.of.the.same.environment) -0.41134264
## Min
## Mean(Log.Optical.density.per.individual.relative.to.controls.of.the.same.environment) -0.3427157
## Mean(Age.per.individual.relative.to.controls.of.the.same.environment) -2.3220569
## Max
## Mean(Log.Optical.density.per.individual.relative.to.controls.of.the.same.environment) 0.2823033
## Mean(Age.per.individual.relative.to.controls.of.the.same.environment) 1.0065145
## 25th
## Mean(Log.Optical.density.per.individual.relative.to.controls.of.the.same.environment) -0.1457087
## Mean(Age.per.individual.relative.to.controls.of.the.same.environment) -1.5851522
## 75th
## Mean(Log.Optical.density.per.individual.relative.to.controls.of.the.same.environment) 0.04107312
## Mean(Age.per.individual.relative.to.controls.of.the.same.environment) -0.04661050
## Skew
## Mean(Log.Optical.density.per.individual.relative.to.controls.of.the.same.environment) 0.1346990
## Mean(Age.per.individual.relative.to.controls.of.the.same.environment) -0.2290919
## Kurtosis
## Mean(Log.Optical.density.per.individual.relative.to.controls.of.the.same.environment) -0.6722874
## Mean(Age.per.individual.relative.to.controls.of.the.same.environment) -1.1116783
## Uji Homogenitas
# Environment
grup1<- data2_new$Environment
library(biotools)
boxM(data = data2_fix1, grouping=grup1)
##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: data2_fix1
## Chi-Sq (approx.) = NaN, df = 0, p-value = NA
# bacterical
grup2<- data2_new$Bacterial_isolate_inoculated
boxM(data = data2_fix1, grouping = grup2)
##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: data2_fix1
## Chi-Sq (approx.) = 8.8547, df = 12, p-value = 0.7153
## Two Way MANOVA bacterical
environment <- as.factor(data2_new$Environment)
bacterical <- as.factor(data2_new$Bacterial_isolate_inoculated)
x1 <- data2_new$`Mean(Log.Optical.density.per.individual.relative.to.controls.of.the.same.environment)`
x2 <- data2_new$`Mean(Age.per.individual.relative.to.controls.of.the.same.environment)`
manova <- manova(cbind(environment, bacterical) ~ x1*x2, data=data2_new)
# Menampilkan hasil
summary(manova)
## Df Pillai approx F num Df den Df Pr(>F)
## x1 1 0.12087 3.0934 2 45 0.0551100 .
## x2 1 0.29640 9.4785 2 45 0.0003671 ***
## x1:x2 1 0.00756 0.1714 2 45 0.8430321
## Residuals 46
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Uji Lanjut
summary.aov(manova)
## Response environment :
## Df Sum Sq Mean Sq F value Pr(>F)
## x1 1 1.5294e-30 1.5294e-30 5.2458 0.02663 *
## x2 1 6.2700e-32 6.2720e-32 0.2151 0.64496
## x1:x2 1 9.6300e-32 9.6270e-32 0.3302 0.56834
## Residuals 46 1.3411e-29 2.9154e-31
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Response bacterical :
## Df Sum Sq Mean Sq F value Pr(>F)
## x1 1 4.002 4.0025 2.2600 0.13959
## x2 1 30.910 30.9105 17.4537 0.00013 ***
## x1:x2 1 0.121 0.1211 0.0684 0.79485
## Residuals 46 81.466 1.7710
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1