One Way MANOVA
Ricardo
2024-10-27
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library("openxlsx")
data <- read.xlsx(file.choose())
data## Education South Sex Experience Union Wage Age Sector Married
## 1 8 0 P 21 0 5.1 35 1 1
## 2 9 0 P 42 0 4.95 57 1 1
## 3 12 0 L 1 0 6.67 19 1 0
## 4 12 0 L 4 0 4 22 0 0
## 5 12 0 L 17 0 7.5 35 0 1
## 6 13 0 L 9 1 13.07 28 0 0
## 7 12 0 L 9 0 19.47 27 0 0
## 8 16 0 L 11 0 13.28 33 1 1
## 9 12 0 L 9 0 8.75 27 0 0
## 10 12 0 L 17 1 11.35 35 0 1
## 11 12 0 L 19 1 11.5 37 1 0
## 12 12 0 L 37 0 7.3 55 2 1
## 13 12 0 L 26 1 22.2 44 1 1
## 14 11 0 L 16 0 3.65 33 0 0
## 15 12 0 L 33 0 20.55 51 0 1
## 16 12 0 P 16 1 5.71 34 1 1
## 17 7 0 L 42 1 7 55 1 1
## 18 12 0 L 9 0 3.75 27 0 0
## 19 12 0 L 23 0 9.56 41 0 1
## 20 12 0 L 8 0 9.36 26 1 1
## 21 10 0 L 30 0 6.5 46 0 1
## 22 12 0 P 8 0 3.35 26 1 1
## 23 10 1 L 27 0 4.45 43 0 0
## 24 8 1 L 27 0 6.5 41 0 1
## 25 9 1 L 30 1 6.25 45 0 0
## 26 9 1 L 29 0 19.98 44 0 1
## 27 7 1 L 44 0 8 57 0 1
## 28 11 1 L 14 0 4.5 31 0 1
## 29 6 1 L 45 0 5.75 57 1 1head(data)## Education South Sex Experience Union Wage Age Sector Married
## 1 8 0 P 21 0 5.1 35 1 1
## 2 9 0 P 42 0 4.95 57 1 1
## 3 12 0 L 1 0 6.67 19 1 0
## 4 12 0 L 4 0 4 22 0 0
## 5 12 0 L 17 0 7.5 35 0 1
## 6 13 0 L 9 1 13.07 28 0 0str(data)## 'data.frame': 29 obs. of 9 variables:
## $ Education : num 8 9 12 12 12 13 12 16 12 12 ...
## $ South : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Sex : chr "P" "P" "L" "L" ...
## $ Experience: num 21 42 1 4 17 9 9 11 9 17 ...
## $ Union : num 0 0 0 0 0 1 0 0 0 1 ...
## $ Wage : chr "5.1" "4.95" "6.67" "4" ...
## $ Age : num 35 57 19 22 35 28 27 33 27 35 ...
## $ Sector : num 1 1 1 0 0 0 0 1 0 0 ...
## $ Married : num 1 1 0 0 1 0 0 1 0 1 ...data[, c(1, 4, 6)] <- lapply(data[, c(1, 4, 6)], as.numeric)
## Statistik Uji
library(MVN)## Warning: package 'MVN' was built under R version 4.4.1test = mvn(data[1:29,c(1,4,6)], mvnTest = "mardia", univariateTest = "SW", multivariatePlot = "qq")
test## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 13.2822084762488 0.208318454443323 YES
## 2 Mardia Kurtosis -0.214329504809508 0.830290111083336 YES
## 3 MVN <NA> <NA> YES
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk Education 0.8590 0.0012 NO
## 2 Shapiro-Wilk Experience 0.9451 0.1365 YES
## 3 Shapiro-Wilk Wage 0.8277 0.0003 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew
## Education 29 10.827586 2.172375 12 6.00 16.0 9.0 12.00 -0.3918675
## Experience 29 21.482759 12.740881 19 1.00 45.0 9.0 30.00 0.3390871
## Wage 29 8.965517 5.436784 7 3.35 22.2 5.1 11.35 1.1815078
## Kurtosis
## Education -0.0565370
## Experience -1.0901924
## Wage 0.2018184## Uji Homogenitas Multivariate
library(biotools)## Warning: package 'biotools' was built under R version 4.4.1## Loading required package: MASS## ---
## biotools version 4.2grup <- data$Sex;grup## [1] "P" "P" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "L" "P" "L" "L" "L"
## [20] "L" "L" "P" "L" "L" "L" "L" "L" "L" "L"head(grup)## [1] "P" "P" "L" "L" "L" "L"boxM(data = data[1:29,c(1,4,6)], grouping = grup)##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: data[1:29, c(1, 4, 6)]
## Chi-Sq (approx.) = 5.2636, df = 6, p-value = 0.5105## One Way MANOVA
owm = manova(cbind(data$Education, data$Experience, data$Wage)~data$Sex)
summary(owm)## Df Pillai approx F num Df den Df Pr(>F)
## data$Sex 1 0.09884 0.91401 3 25 0.4484
## Residuals 27