160621-RMarkdown-QPCR.Rmd

---
title: "160621-RMarkdown-QPCR.Rmd"
output: word_document
---

Strain : Sb

```{r,echo=FALSE}
require(car)

doss<-read.csv2("doss.csv",header=TRUE,sep=";",dec=".")


```
Summary

```{r,echo=FALSE}

summary(doss)

tab<-data.frame(table(doss$ASSAY),tapply(doss$QUANTITY,doss$ASSAY,mean,na.rm=TRUE),tapply(doss$QUANTITY,doss$ASSAY,sd,na.rm=TRUE))
tab<-tab[,2:4]
names(tab)<-c("Freq","Mean","Sd")
bou<-max(tab$Mean+2*tab$Sd)

tab1<-tab
tab1$Mean<-format(tab$Mean,scientific=TRUE,digits=3)
tab1$Sd<-format(tab$Sd,scientific=TRUE,digits=3)
tab1

par(mfcol=c(1,2))
boxplot(doss$QUANTITY~doss$ASSAY,xlab="Zinc concentration (mM)",ylab="Quantity (# Genome)")
barplot(tab$Sd+tab$Mean,width=1,space=3,col="white")
#,ylim=c(0,bou))
barplot(tab$Mean,,width=1,space=3,col="black",names.arg=FALSE,add=TRUE)
barplot(tab$Mean-tab$Sd,width=1,space=3,col="white",add=TRUE)
```

Normality tests for Quantity and residus

Normality Tests: Shapiro-Wilk
If Shapiro-Wilk p-value > 0.05 => Normality, else, use Wilcoxon

```{r,echo=FALSE}

shapiro.test(doss$QUANTITY)


```

Test d'homosc?dasticit?: Bartlett test
If p-value>0.05, then run ANOVA, else run Kruskal- Wallis

```{r,echo=FALSE}

bartlett.test(doss$QUANTITY~doss$ASSAY)
```


Statistical test: Anova

```{r,echo=FALSE}
drop1(lm(doss$QUANTITY~doss$ASSAY),.~.,test="F")
```
Post-hoc multiple T-test

```{r,echo=FALSE}
pairwise.t.test(doss$QUANTITY,doss$ASSAY,paired=FALSE,p.adjust.method="bonferroni")
```

Statistical test: Kruskal- Wallis

```{r,echo=FALSE}
kruskal.test(doss$QUANTITY~doss$ASSAY,na.action=na.omit)
```


Post-hoc Kruskal-Wallis
Dunn or Nemenyi ? and co test (by ranking)

```{r,echo=FALSE}
library(PMCMR)

posthoc.kruskal.nemenyi.test(doss$QUANTITY,doss$ASSAY,dist="Tukey",na.action=na.omit)

#posthoc.kruskal.dunn.test(doss$QUANTITY,doss$ASSAY,p.adjust.method="bonferroni")
#same stuffs more or less... A bit less OK for some ^^


#dunn.test provides Dunn's (1964) z test approximation to a rank sum test employing both the same ranks used in the Kruskal-Wallis test, and the pooled variance estimate implied by the null hypothesis of the Kruskal-Wallis (akin to using the pooled variance to calculate t test statistics following an ANOVA).

#By contrast, the Kruskal-Nemenyi test as implemented in posthoc.kruskal.nemenyi.test is based on either the Studentized range distribution, or the ??2 distribution depending on user choice.

```