One way anova
David_La
3/18/2021
Input data
library(readxl)## Warning: package 'readxl' was built under R version 4.0.3X19_10_20_Black_soldier_fly <- read_excel("C:/Users/Admin/Desktop/LR of BSL/19.10.20 Black soldier fly.xlsx", sheet = "Tukey", col_types = c("text",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric"))## Warning in strptime(x, format, tz = tz): unable to identify current timezone 'C':
## please set environment variable 'TZ'attach(X19_10_20_Black_soldier_fly)General view of the relationship between Treatment and Final body weight
require(ggplot2)## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.0.3h=ggplot(X19_10_20_Black_soldier_fly,aes(x=Treatment,y=`Final weight (g)`))
h+geom_boxplot()+theme_bw()+theme_classic()
Assumption 1: All samplems are independent and >2 categorical groups (if equal 2=> t-test).
X19_10_20_Black_soldier_fly$Treatment=as.factor(X19_10_20_Black_soldier_fly$Treatment)
# treatment as factor checking
class(X19_10_20_Black_soldier_fly$Treatment)## [1] "factor"Assumption 2: Denpendent variable is continuous (all denpendent variables are already continous)
Assumption 3: Normal distribution (in each group)
shapiro.test(`Final weight (g)`)##
## Shapiro-Wilk normality test
##
## data: Final weight (g)
## W = 0.90585, p-value = 0.117Assumption 4 Homogeneity of variances (p>0.05 => variances are Homogeneity)
bartlett.test(`Final weight (g)`~Treatment,data=X19_10_20_Black_soldier_fly)##
## Bartlett test of homogeneity of variances
##
## data: Final weight (g) by Treatment
## Bartlett's K-squared = 5.5779, df = 4, p-value = 0.233ANOVA test
Final_BW= aov(`Final weight (g)`~Treatment)
summary(Final_BW)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 536.7 134.2 14.43 0.00037 ***
## Residuals 10 93.0 9.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Posthoc test by Tukey
require(lsmeans)## Loading required package: lsmeans## Warning: package 'lsmeans' was built under R version 4.0.3## Loading required package: emmeans## Warning: package 'emmeans' was built under R version 4.0.3## The 'lsmeans' package is now basically a front end for 'emmeans'.
## Users are encouraged to switch the rest of the way.
## See help('transition') for more information, including how to
## convert old 'lsmeans' objects and scripts to work with 'emmeans'.require(multcomp)## Loading required package: multcomp## Warning: package 'multcomp' was built under R version 4.0.3## Loading required package: mvtnorm## Warning: package 'mvtnorm' was built under R version 4.0.3## Loading required package: survival## Loading required package: TH.data## Warning: package 'TH.data' was built under R version 4.0.3## Loading required package: MASS## Warning: package 'MASS' was built under R version 4.0.3##
## Attaching package: 'TH.data'## The following object is masked from 'package:MASS':
##
## geyserPosthoc_Final_weight=lsmeans(Final_BW,pairwise~Treatment,adjust="Tukey")
cld(Posthoc_Final_weight[[1]],alpha=0.05,Letters=letters)## Treatment lsmean SE df lower.CL upper.CL .group
## 0%BSF 30.1 1.76 10 26.2 34.0 a
## 5%BSF 32.7 1.76 10 28.8 36.7 ab
## 10%BSF 34.9 1.76 10 31.0 38.8 ab
## 15%BSF 38.6 1.76 10 34.7 42.5 b
## 20%BSF 47.3 1.76 10 43.4 51.2 c
##
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 5 estimates
## significance level used: alpha = 0.05Visualization
level_order=c("0%BSF","5%BSF","10%BSF","15%BSF","20%BSF")
h=ggplot(X19_10_20_Black_soldier_fly,aes(x=factor(Treatment,level_order),y=`Final weight (g)`))
h+stat_summary(fun="mean",geom="bar",fill=("blue"),width=0.5)+stat_summary(geom = "errorbar", fun.data=mean_se,position="dodge",width=0.2)+theme_bw()+theme_classic()+stat_summary(geom= 'text', fun.y = max,position=position_dodge(.7), label = c("a","ab","ab","b","c"), vjust =-0.3)+xlab("Treatment")## Warning: `fun.y` is deprecated. Use `fun` instead.