#Packages
#install.packages("tidyverse")
#install.packages("ggpubr")
#install.packages("readxl")
#install.packages("sjPlot")
#install.packages("fBasics")
#install.packages("lmtest")
#install.packages("zoo")
#install.packages("sciplot")
#install.packages("lsr")
#install.packages("gplots")
##memanggil package
library(tidyverse)
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## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.4 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0
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## x dplyr::filter() masks stats::filter()
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library(sjPlot)
library(fBasics)
## Loading required package: timeDate
## Loading required package: timeSeries
library(lmtest)
## Loading required package: zoo
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## time<-
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## as.Date, as.Date.numeric
library(car)
## Loading required package: carData
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## method from
## influence.merMod lme4
## cooks.distance.influence.merMod lme4
## dfbeta.influence.merMod lme4
## dfbetas.influence.merMod lme4
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library(readxl)
library(ggpubr)
library(MASS)
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## select
library(sjmisc)
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## add_case
library(sciplot)
library(lsr)
library(gplots)
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## lowess
#Import Dataset
#Data yang kita miliki memiliki format .xlsx . Untuk megimport data tersebut, kita dapat
#menggunakan fungsi read_excel dari library readxl .
metal = read_excel("soal 1 faktorial ral.xlsx")
head(metal)
## # A tibble: 6 x 4
## FeedRate DepthOfCut SurfaceFinish Ulangan
## <dbl> <dbl> <dbl> <dbl>
## 1 0.2 0.15 74 1
## 2 0.2 0.15 64 2
## 3 0.2 0.15 60 3
## 4 0.2 0.18 79 1
## 5 0.2 0.18 68 2
## 6 0.2 0.18 73 3
str(metal)
## tibble [36 x 4] (S3: tbl_df/tbl/data.frame)
## $ FeedRate : num [1:36] 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 ...
## $ DepthOfCut : num [1:36] 0.15 0.15 0.15 0.18 0.18 0.18 0.2 0.2 0.2 0.25 ...
## $ SurfaceFinish: num [1:36] 74 64 60 79 68 73 82 88 92 99 ...
## $ Ulangan : num [1:36] 1 2 3 1 2 3 1 2 3 1 ...
#Mengubah kolom faktor ke bentuk factor
metal$DepthOfCut<- as.factor(metal$DepthOfCut)
metal$FeedRate<- as.factor(metal$FeedRate)
#Menampilkan hasil ANOVA
anova_bat <- aov(SurfaceFinish~FeedRate+DepthOfCut+FeedRate:DepthOfCut,data=metal)
summary(anova_bat)
## Df Sum Sq Mean Sq F value Pr(>F)
## FeedRate 2 3160.5 1580.2 55.018 1.09e-09 ***
## DepthOfCut 3 2125.1 708.4 24.663 1.65e-07 ***
## FeedRate:DepthOfCut 6 557.1 92.8 3.232 0.018 *
## Residuals 24 689.3 28.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Catatan: 1. FeedRate:DepthOfCut menandakan interaksi antara Feed Rate dan Depth Of Cut
#Plot Interaksi
lineplot.CI(x.factor=metal$FeedRate,response=metal$SurfaceFinish,group=metal$DepthOfCut,ci.fun=ciMean,xlab="FeedRate",ylab="SurfaceFinish",legend=TRUE, x.leg="bottomright")
#Uji Asumsi ANOVA
#Menggunakan Grafik
plot_model(anova_bat,type = "diag")
## [[1]]
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## `geom_smooth()` using formula 'y ~ x'
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## `geom_smooth()` using formula 'y ~ x'
res <- residuals(anova_bat)
res_order <- data.frame(order=seq_along(res),
residual=res
)
plot_scatter(res_order,x = order,y=residual)+geom_hline(yintercept = 0)
res <- residuals(anova_bat)
ksnormTest(res)
##
## Title:
## One-sample Kolmogorov-Smirnov test
##
## Test Results:
## STATISTIC:
## D: 0.3513
## P VALUE:
## Alternative Two-Sided: 0.0001774
## Alternative Less: 8.87e-05
## Alternative Greater: 0.0001526
##
## Description:
## Wed Feb 17 19:39:41 2021 by user: Reni_Amelia
print(adTest(res))
##
## Title:
## Anderson - Darling Normality Test
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## Test Results:
## STATISTIC:
## A: 0.3259
## P VALUE:
## 0.5097
##
## Description:
## Wed Feb 17 19:39:41 2021 by user: Reni_Amelia
shapiroTest(res)
##
## Title:
## Shapiro - Wilk Normality Test
##
## Test Results:
## STATISTIC:
## W: 0.9705
## P VALUE:
## 0.4397
##
## Description:
## Wed Feb 17 19:39:41 2021 by user: Reni_Amelia
bptest(SurfaceFinish~FeedRate+DepthOfCut+FeedRate:DepthOfCut,data=metal,studentize = F)
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## Breusch-Pagan test
##
## data: SurfaceFinish ~ FeedRate + DepthOfCut + FeedRate:DepthOfCut
## BP = 6.6135, df = 11, p-value = 0.8295
leveneTest(SurfaceFinish~FeedRate*DepthOfCut*FeedRate:DepthOfCut,data=metal)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 11 0.3209 0.9733
## 24