**imDEV » R**, and kindly contributed to R-bloggers)

I was interested in modeling the relationship between the power and sample size, while holding the significance level constant (p = 0.05) , for the common two-sample t-Test. Luckily R has great support for power analysis and I found the function I was looking for in the package pwr.

To calculate the power for the two-sample T-test at different effect and sample sizes I needed to wrap the basic function power.t.test().

# Need pwr package if(!require(pwr)){install.packages("pwr");library("pwr")} # t-TEST #--------------------------------- d<-seq(.1,2,by=.1) # effect sizes n<-1:150 # sample sizes t.test.power.effect<-as.data.frame(do.call("cbind",lapply(1:length(d),function(i) { sapply(1:length(n),function(j) { power.t.test(n=n[j],d=d[i],sig.level=0.05,power=NULL,type= "two.sample")$power }) }))) t.test.power.effect[is.na(t.test.power.effect)]<-0 # some powesr couldn't be calculated, set these to zero colnames(t.test.power.effect)<-paste (d,"effect size")

#plot results using base #------------------------------------------------ obj<-t.test.power.effect # object to plot cols<-1:ncol(obj) color<-rainbow(length(cols), alpha=.5) # colors lwd=5 # line thickness lty<-rep(1,length(color)) lty[imp]<-c(2:(length(imp)+1)) #highligh important effect sizes imp<-c(2,5,8) # cuts cuts<-c("small","medium","large") # based on cohen 1988 color[imp]<-c("black") wording<-d wording[imp]<-cuts par(fig=c(0,.8,0,1),new=TRUE) #initialize plot plot(1,type="n",frame.plot=FALSE,xlab="sample size",ylab="power",xlim=c(1,150),ylim=c(0,1),main="t-Test", axes = FALSE) #add custom axis and grid abline(v=seq(0,150,by=10),col = "lightgray", lty = "dotted") abline(h=seq(0,1,by=.05),col = "lightgray", lty = "dotted") axis(1,seq(0,150,by=10)) axis(2,seq(0,1,by=.05)) #plot lines for(i in 1:length(cols)){lines(1:150,obj[,cols[i]],col=color[i],lwd=lwd,lty=lty[i])} #legend par(fig=c(.65,1,0,1),new=TRUE) plot.new() legend("top",legend=wording,col=color,lwd=3,lty=lty,title="Effect Size",bty="n")

Based on this graph, we can see the relationship between power, effect sizes and sample number. I’ve marked the cutoffs suggested by Cohen 1988 delineating small, medium and large effect sizes. Based on this we can see that if we are designing an experiment and are trying to select a sample size for which our test will be powerd at 0.8 we need to consider the expected effect of our experimental treatment. If we think that or treatment should have a moderate effect we should consider some where around 60 samples per group. However and even better analysis would be to directly calculate the sample number needed to achieve some power and significance level given experimentally derived effects sizes based on preliminary data!

And just for kicks here is the same data plotted using ggplot2.

#plot using ggplot2 #------------------------------------------------ #plot results using ggplot2 library(ggplot2);library(reshape) x11() # graphic device on windows obj<-cbind(size=1:150,t.test.power.effect) #flip object for melting melted<-cbind(melt(obj, id="size"),effect=rep(d,each=150)) # melt and bind with effect for mapping ggplot(data=melted, aes(x=melted$size, y=melted$value, color=as.factor(melted$effect))) + geom_line(size=2,alpha=.5) + ylab("power") + xlab("sample size") + ggtitle("t-Test")+theme_minimal() # wow ggplot2 is amazing in its brevity # need to tweak legend and lty, but otherwise very similar

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**imDEV » R**.

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