Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- library(MBESS)
- library(pwr)
- nSims <- 100000 #number of simulated experiments
- p <-numeric(nSims) #set up empty container for all simulated p-values
- obs_pwr <-numeric(nSims) #set up empty container
- t <-numeric(nSims) #set up empty container
- d_all<-numeric(nSims)
- N<-33 #number of participants
- for(i in 1:nSims){ #for each simulated experiment
- x<-rnorm(n = N, mean = 100, sd = 20) #produce 100 simulated participants
- y<-rnorm(n = N, mean = 110, sd = 20) #produce 100 simulated participants
- z<-t.test(x,y) #perform the t-test
- d<-smd(Mean.1= mean(x), Mean.2=mean(y), s.1=sd(x), s.2=sd(y), n.1=N, n.2=N, Unbiased=TRUE)
- d_all[i]<-d
- obs_pwr[i]<-pwr.t.test(n = N, d = d, sig.level = 0.05, type = "two.sample", alternative = "two.sided")$power
- p[i]<-z$p.value #get the p-value and store it
- t[i]<-t.test(x, y, alternative = "two.sided", paired = FALSE, var.equal = TRUE, conf.level = 0.95)$statistic
- }
- #Calculate power in simulation
- cat ("The power (in %) is")
- sum(p < 0.05)/nSims*100
- #now plot histograms of p-values, t-values, d, observed power
- hist(p, main="Histogram of p-values", xlab=("Observed p-value"))
- hist(t, main="Histogram of t-values", xlab=("Observed t-value"))
- hist(d_all, main="Histogram of d", xlab=("Observed d"))
- hist(obs_pwr, main="Histogram of observed power", xlab=("Observed power (%)"))
- plot(p,obs_pwr)
- abline(v = 0.05, lwd = 2, col = "red", lty = 2)
- abline(h = 0.5, lwd = 2, col = "red", lty = 2)
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement