library(readxl)
## Warning: package 'readxl' was built under R version 4.3.2
data1 <- read_excel("C:/Users/user/Downloads/tugas rancob beneran.xlsx")
data1[,3:ncol(data1)] = data1[,3:ncol(data1)] + 75
data1
## # A tibble: 9 × 7
## kelompok varietas n1 n2 n3 n4 n5
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 V1 75.9 76.2 76.3 76.8 76.2
## 2 1 V2 75.9 76.1 76.3 76.6 76.9
## 3 1 V3 75.9 76.4 76.3 76.4 76.2
## 4 2 V1 75.9 76.3 76.5 76.9 76.4
## 5 2 V2 75.8 75.9 76.5 76.3 76.6
## 6 2 V3 76 76.2 76.4 76.5 76.1
## 7 3 V1 76 76.2 76.4 77.1 76.2
## 8 3 V2 75.8 75.9 76.1 76.1 76.5
## 9 3 V3 75.7 76 76.4 76.4 76.3
library(reshape2)
## Warning: package 'reshape2' was built under R version 4.3.2
data.rakl <- melt(data1, id.vars=c("kelompok","varietas"), variable.name="nitrogen",value.name = "respon")
data.rakl$kelompok <- as.factor(data.rakl$kelompok)
data.rakl$varietas<- as.factor(data.rakl$varietas)
data.rakl$nitrogen <- as.factor(data.rakl$nitrogen)
str(data.rakl)
## 'data.frame': 45 obs. of 4 variables:
## $ kelompok: Factor w/ 3 levels "1","2","3": 1 1 1 2 2 2 3 3 3 1 ...
## $ varietas: Factor w/ 3 levels "V1","V2","V3": 1 2 3 1 2 3 1 2 3 1 ...
## $ nitrogen: Factor w/ 5 levels "n1","n2","n3",..: 1 1 1 1 1 1 1 1 1 2 ...
## $ respon : num 75.9 75.9 75.9 75.9 75.8 76 76 75.8 75.7 76.2 ...
anova.rakl <- aov(respon~varietas+nitrogen+varietas:nitrogen+kelompok,data=data.rakl)
summary(anova.rakl)
## Df Sum Sq Mean Sq F value Pr(>F)
## varietas 2 0.1871 0.0936 5.003 0.0139 *
## nitrogen 4 2.5124 0.6281 33.592 2.59e-10 ***
## kelompok 2 0.0698 0.0349 1.866 0.1735
## varietas:nitrogen 8 0.9729 0.1216 6.504 8.58e-05 ***
## Residuals 28 0.5236 0.0187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
qf(0.05,2,28,lower.tail = F)
## [1] 3.340386
qf(0.05,4,28,lower.tail = F)
## [1] 2.714076
qf(0.05,2,28,lower.tail = F)
## [1] 3.340386
qf(0.05,8,28,lower.tail = F)
## [1] 2.291264
library(phia)
## Warning: package 'phia' was built under R version 4.3.2
## Loading required package: car
## Loading required package: carData
model.rakl<- lm(respon~varietas+nitrogen+varietas:nitrogen+kelompok,data=data.rakl)
interaksi<-interactionMeans(model.rakl)
plot(interaksi)
Dapat dilihat dalam plot interaksi bahwa memang benar bahwa kelompok tidak berpengaruh signifikan karena hasil rataan pada varietas dan juga nitrogen sejajar. Berbeda halnya dengan interaksi antara nitrogen dan varietas yang memiliki perpotongan hal ini menunjukan interkasi antara keduanya signfikan.