# R code for p curves

[This article was first published on

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

**Psychological Statistics**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

I have finally got around to posting the R code for my p curve simulation. Those familiar with R will realize how crude it is (I've been caught up with other urgent stuff and had no time to explore further). You are welcome to play with (and improve!) the code. Changing delta will alter the (at present) fixed effect size. It would be more realistic to vary this (and the sample sizes). A good starting point for the effect size distribution (in the population) might be a normal distribution with say a mean of zero and a variance of 1 (see Gillett, 1994). delta <- 0.5 m1 <- 10 sd <- 2 m2 <- m1 + sd*delta n1 <- n2 <-25 n.sims <- 500 p.data <- replicate(n.sims, t.test(rnorm(n1, m1,sd), rnorm(n2, m2,sd))$p.val, simplify=T) par(mfrow=c(5,3)) for (i in 1:15) { p.data <- replicate(n.sims, t.test(rnorm(n1, m1,sd), rnorm(n2, m2,sd))$p.val, simplify=T) hist(p.data, xlim=c(0,0.1), breaks = 99, col = 'gray') }R code html script courtesy of Pretty R at inside-R.org

*References*

Gillett, R. (1994). Post Hoc Power Analysis.

*Journal of Applied Psychology*,*79*, 783-785.To

**leave a comment**for the author, please follow the link and comment on their blog:**Psychological Statistics**.R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.