library(knitr)
## Warning: package 'knitr' was built under R version 4.0.5
### getting working directory###
getwd()
## [1] "C:/Users/Acer/Desktop"
setwd( "C:/Users/Acer/Desktop")
###Creating dataframe##Dataframe name spp##
####Data was obtained from this link https://stat.ethz.ch/~meier/teaching/anova/split-plot-designs.html###
##Variable names were change for this exercise###
spp<-read.csv( "C:/Users/Acer/Desktop/Rpractice3a.csv")
###Verification of imported data###
names(spp)
## [1] "plot" "growring.media" "variety" "yield"
head(spp)
## plot growring.media variety yield
## 1 1 control A 8.9
## 2 1 control B 9.5
## 3 1 control C 11.7
## 4 1 control D 15.0
## 5 2 control A 10.8
## 6 2 control B 11.0
str(spp)
## 'data.frame': 32 obs. of 4 variables:
## $ plot : int 1 1 1 1 2 2 2 2 3 3 ...
## $ growring.media: chr "control" "control" "control" "control" ...
## $ variety : chr "A" "B" "C" "D" ...
## $ yield : num 8.9 9.5 11.7 15 10.8 11 12.1 12.9 15.1 11.6 ...
###installing libraries###
library (agricolae)
## Warning: package 'agricolae' was built under R version 4.0.5
library(ggplot2)
###interaction plots###
with(spp, interaction.plot(x.factor = variety,
trace.factor = growring.media,
response = yield))
###interaction plots with error bars###
library (ggpubr)
## Warning: package 'ggpubr' was built under R version 4.0.5
ggline(spp, x = "variety", y = "yield", color = "growring.media",
add = c("mean_se", "dotplot"),
palette = c("#0384fc", "#9803fc"))
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
library(lmerTest)
## Warning: package 'lmerTest' was built under R version 4.0.5
## Loading required package: lme4
## Warning: package 'lme4' was built under R version 4.0.5
## Loading required package: Matrix
## Warning: package 'Matrix' was built under R version 4.0.5
##
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
##
## lmer
## The following object is masked from 'package:stats':
##
## step
fit.spp <- lmer(yield~ growring.media * variety + (1 | plot),
data = spp)
###Linear Mixed Model###
summary(fit.spp)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: yield ~ growring.media * variety + (1 | plot)
## Data: spp
##
## REML criterion at convergence: 98
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6746 -0.5535 -0.0335 0.4873 2.3828
##
## Random effects:
## Groups Name Variance Std.Dev.
## plot (Intercept) 0.2003 0.4475
## Residual 2.0137 1.4190
## Number of obs: 32, groups: plot, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 10.325 0.744 23.425 13.878 8.60e-13 ***
## growring.medianew 6.100 1.052 23.425 5.798 6.16e-06 ***
## varietyB -0.950 1.003 18.000 -0.947 0.35630
## varietyC 1.725 1.003 18.000 1.719 0.10274
## varietyD 3.900 1.003 18.000 3.887 0.00108 **
## growring.medianew:varietyB -1.700 1.419 18.000 -1.198 0.24646
## growring.medianew:varietyC -1.275 1.419 18.000 -0.898 0.38078
## growring.medianew:varietyD -1.825 1.419 18.000 -1.286 0.21471
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) grwrn. vartyB vartyC vartyD grw.:B grw.:C
## grwrng.mdnw -0.707
## varietyB -0.674 0.477
## varietyC -0.674 0.477 0.500
## varietyD -0.674 0.477 0.500 0.500
## grwrng.md:B 0.477 -0.674 -0.707 -0.354 -0.354
## grwrng.md:C 0.477 -0.674 -0.354 -0.707 -0.354 0.500
## grwrng.md:D 0.477 -0.674 -0.354 -0.354 -0.707 0.500 0.500
###Anova Type III####
anova(fit.spp)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## growring.media 137.413 137.413 1 6 68.2395 0.0001702 ***
## variety 96.431 32.144 3 18 15.9627 2.594e-05 ***
## growring.media:variety 4.173 1.391 3 18 0.6907 0.5695061
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1