library(knitr)
opts_chunk$set(echo = TRUE )
Package install
Packages <- c("tidyverse", "car", "dunn.test","onewaytests","FSA")
lapply(Packages, library, character.only = TRUE)
## -- Attaching packages --------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --
## √ ggplot2 3.2.1 √ purrr 0.3.3
## √ tibble 2.1.3 √ dplyr 0.8.3
## √ tidyr 1.0.0 √ stringr 1.4.0
## √ readr 1.3.1 √ forcats 0.4.0
## -- Conflicts ------------------------------------------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## Loading required package: carData
##
## Attaching package: 'car'
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## recode
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## some
## ## FSA v0.8.26. See citation('FSA') if used in publication.
## ## Run fishR() for related website and fishR('IFAR') for related book.
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## Attaching package: 'FSA'
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## [16] "stats" "graphics" "grDevices" "utils" "datasets"
## [21] "methods" "base"
Data import
d1<- read.csv("/Users/koho0/Desktop/Dr Hong/Fig 2 2groups/P7 mEPSC freq.csv")
Data structure
str(d1)
## 'data.frame': 47 obs. of 3 variables:
## $ subject: int 1 2 3 4 5 6 7 8 9 10 ...
## $ group : Factor w/ 2 levels "con","sevo": 1 1 1 1 1 1 1 1 1 1 ...
## $ ampl : num 0.05 0.0667 0.025 0.0333 0.0333 ...
Explorative data analysis with graphics
p=ggplot(d1,aes(d1[,2],d1[,3]))
p + geom_boxplot(outlier.shape = NA) + geom_jitter(width = 0.2) + ylab("Value") +xlab("Groups")
easystat=function(d1){
d1=data.frame(d1) # dataframe으로 전환
d1[,2]=as.factor(d1[,2]) # 2행을 factor변수로 변환
lmd=lm(d1[,3]~d1[,2],data=d1) # 선형모형
swd=shapiro.test(lmd$resid) # 정규성 검정
if (swd$p.value<0.05){ # p-value가 0.05보다 작다면(정규성 가정 위반)
cat("1. Normality assumption test by Shapiro_Wilk test is", "\n",
"p =", paste(sprintf("%.3f",as.numeric(swd$p.value))), "\n",
"Normality assumption was rejected", "\n") #
kwd=kruskal.test(d1[,3]~d1[,2],data=d1) # 비모수 검정인 KW test 시행
if (kwd$p.value<0.05){ # KW test결과가 유의하게 나온 경우
cat("2. The result of Kruskall_Wallis test:","\n",
"p =", paste(format(round(kwd$p.value,3), nsmall=3)), "\n",
"A statistically significant difference exist between groups")
DT = dunnTest(d1[,3] ~ d1[,2],data=d1,method="bh")
DT
} else { #
cat( "2. The result of Kruskall_Wallis test:","\n",
"p =", paste(format(round(kwd$p.value,3), nsmall=3)), "\n",
"A statistically significant difference do not exist between groups")
}
}
else { # 정규성 가정을 만족하는 경우
cat("1. Normality assumption test by Shapiro_Wilk test is", "\n",
"p =", paste(format(round(swd$p.value,3), nsamll=3)), "\n",
"Normality assumption was not rejected", "\n") #
bnt=bartlett.test(d1[,3]~d1[,2],data=d1) # 등분산성 검정
if (bnt$p.value<0.05) { # 등분산성 가정을 만족하지 않은 경우
cat("2. Equal variance test by Bartlett test is", "\n", # 만족하지 못했다고 출력
"p =", paste(format(round(bnt$p.value,3), nsmall=3)), "\n",
"Equal variance assumption was rejected","\n")
wtd=oneway.test(d1[,3]~d1[,2], data=d1, na.action=na.omit, var.equal=FALSE) # 등분산성 가정을 만족하지 못했기 때문에 Welch anova 시행
if (wtd$p.value<0.05) { # welch anova가 유의하게 나온경우
cat("3. The result of Welch ANOVA is", "\n",
"p =", paste(format(round(wtd$p.value,3), nsmall=3)), "\n",
"A statistically significant difference exist between groups")
md2=aov(d1[,3]~d1[,2])
summary(md2)
library(moonBook)
paov=comma(summary(md2)[[1]][[1, "Pr(>F)"]])
TukeyHSD(md2)
}
else { # Welch anova가 유의하지 않은 경우
cat("3. The result of Welch ANOVA is", "\n", #
"p =", paste(format(round(wtd$p.value,3), nsmall=3)), "\n",
"A statistically significant difference do not exist between groups")
}
} else { #
cat("2. Equal variance test by Bartlett test is", "\n", # 등분산 가정을 만족했다고 출력
"p =", paste(format(round(bnt$p.value,3), nsmall=3)), "\n",
"Equal variance assumption was not rejected","\n")
md2=aov(d1[,3]~d1[,2],data=d1)
summary(md2)
library(moonBook)
paov=comma(summary(md2)[[1]][[1, "Pr(>F)"]])# anova
if(as.numeric(paov)<0.05) { # 결과가 유의한 경우
cat("3. The result of anova is", "\n",
"p =", paste(sprintf("%.3f",as.numeric(paov))), "\n",
"A statistically significant difference exist between groups")
TukeyHSD(md2) }
else{ # 결과가 유의하지 않은 경우
cat("3. The result of anova is", "\n",
"p =",paste(sprintf("%.3f",as.numeric(paov))), "\n",
"A statistically significant difference do not exist between groups")
}
}
}
}
Statistical Result
easystat(d1)
## 1. Normality assumption test by Shapiro_Wilk test is
## p = 0.000
## Normality assumption was rejected
## 2. The result of Kruskall_Wallis test:
## p = 0.104
## A statistically significant difference do not exist between groups