#install.packages("tidyverse")
#install.packages("ggpubr")
#install.packages("sjPlot")
#install.packages("fBasics")
#install.packages("lmtest")
#install.packages("car")
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --
## 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
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(ggpubr)
library(sjPlot)
## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car
## #refugeeswelcome
library(fBasics)
## Loading required package: timeDate
## Loading required package: timeSeries
library(lmtest)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following object is masked from 'package:timeSeries':
##
## time<-
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(car)
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:fBasics':
##
## densityPlot
## The following object is masked from 'package:dplyr':
##
## recode
## The following object is masked from 'package:purrr':
##
## some
library(readxl)
library(agricolae)
##
## Attaching package: 'agricolae'
## The following objects are masked from 'package:timeDate':
##
## kurtosis, skewness
#import file excel
Oyster <- readxl::read_excel("soal 4 Ancova.xlsx")
head(Oyster,10)
## # A tibble: 10 x 4
## Location Replication Initial_Weight Final_Weight
## <dbl> <dbl> <dbl> <dbl>
## 1 1 1 27.2 32.6
## 2 1 2 32 36.6
## 3 1 3 33 37.7
## 4 1 4 26.8 31
## 5 2 1 28.6 33.8
## 6 2 2 26.8 31.7
## 7 2 3 26.5 30.7
## 8 2 4 26.8 30.4
## 9 3 1 28.6 35.2
## 10 3 2 22.4 29.1
#mengubah setiap faKtor dan ulangan ke dalam bentuk factor
Oyster <- Oyster %>% mutate_if(is.character,as.factor) %>% mutate(Location=as.factor(Location))
Oyster <- Oyster %>% mutate_if(is.character,as.factor) %>% mutate(Replication=as.factor(Replication))
head(Oyster,10)
## # A tibble: 10 x 4
## Location Replication Initial_Weight Final_Weight
## <fct> <fct> <dbl> <dbl>
## 1 1 1 27.2 32.6
## 2 1 2 32 36.6
## 3 1 3 33 37.7
## 4 1 4 26.8 31
## 5 2 1 28.6 33.8
## 6 2 2 26.8 31.7
## 7 2 3 26.5 30.7
## 8 2 4 26.8 30.4
## 9 3 1 28.6 35.2
## 10 3 2 22.4 29.1
# Memeriksa keliniearan
ggscatter(data = Oyster,x = "Initial_Weight",y="Final_Weight")
# Ancova
anova_Oyster <-aov(Final_Weight ~ Initial_Weight+Location,data=Oyster)
Anova(anova_Oyster,type="III")
## Anova Table (Type III tests)
##
## Response: Final_Weight
## Sum Sq Df F value Pr(>F)
## (Intercept) 0.733 1 2.4319 0.1412046
## Initial_Weight 156.040 1 517.3840 1.867e-12 ***
## Location 12.089 4 10.0212 0.0004819 ***
## Residuals 4.222 14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Menggunakan Grafik
plot_model(anova_Oyster,type = "diag")
## [[1]]
##
## [[2]]
## `geom_smooth()` using formula 'y ~ x'
##
## [[3]]
##
## [[4]]
## `geom_smooth()` using formula 'y ~ x'
# Plot order vs residual
res <- residuals(anova_Oyster)
res_order <- data.frame(order=seq_along(res),
residual=res
)
plot_scatter(res_order,x = order,y=residual)+geom_hline(yintercept = 0)
# Uji Normalitas
ksnormTest(res)
##
## Title:
## One-sample Kolmogorov-Smirnov test
##
## Test Results:
## STATISTIC:
## D: 0.3022
## P VALUE:
## Alternative Two-Sided: 0.04071
## Alternative Less: 0.02036
## Alternative Greater: 0.1894
##
## Description:
## Wed Feb 17 19:25:06 2021 by user: Reni_Amelia
print(adTest(res))
##
## Title:
## Anderson - Darling Normality Test
##
## Test Results:
## STATISTIC:
## A: 1.1052
## P VALUE:
## 0.005178
##
## Description:
## Wed Feb 17 19:25:06 2021 by user: Reni_Amelia
shapiroTest(res)
##
## Title:
## Shapiro - Wilk Normality Test
##
## Test Results:
## STATISTIC:
## W: 0.8907
## P VALUE:
## 0.02774
##
## Description:
## Wed Feb 17 19:25:06 2021 by user: Reni_Amelia
# Uji Homogenitas Ragam
bptest(Final_Weight ~ Initial_Weight+Location,data=Oyster,
studentize = F)
##
## Breusch-Pagan test
##
## data: Final_Weight ~ Initial_Weight + Location
## BP = 1.7635, df = 5, p-value = 0.8808