A principal component analysis (PCA), is a way to take a large amount of data and plot it on two or three axes. It does this without knowing which groups the data belongs to, so if you perform a PCA, plot it, and the data clusters nicely into the experiment groups, you know there are […]

Here is your basic one-way and two-way ANOVAs. The assumption check is the same for the one-way or two-way ANOVA, you are looking for an even scattering of dots in all three residual plots and your looking for a straight 45 degree line in the q-q plot. A log transformation often helps with biological data. […]

#Power analysis t-test ## Computing the required n power.t.test(delta=100, sd=50, power=0.8, sig.level=0.05, strict=FALSE) ## Computing the power power.t.test(delta=100, sd=50, n=4, sig.level=0.05, strict=FALSE) #Power analysis Two-way ANOVA install.packages(“pwr2”) library(pwr2) ss.2way(a=2, b=2, alpha=0.05, beta=0.2, f.A=0.36, f.B=0.64, B=100) #a and b are the number of factors in each group, B is bootstrap number # F.A and F.B are […]

#Load data example<- read.table(url(“http://jackrrivers.com/wp-content/uploads/2018/03/Example.txt”), header=T) #Welch corrected ttest<-t.test(Dependent~Factor1, data=example) ttest #non corrected noncorrect.ttest<-t.test(Data~Group, data=example, var.equal=T) noncorrect.ttest #Easy way to check the assumptions of equal variance and normality par(mfrow=c(2,2)) plot(aov(Dependent~Factor1, data=example))