{RR()} #################################################### ### Examples borrowed from: ### http://www.statmethods.net/stats/power.html ### You can also look at: ### http://www.ats.ucla.edu/stat/R/dae/t_test_power.htm if(!require(pwr)){ install.packages("pwr", repos="http://ftp.heanet.ie/mirrors/cran.r-project.org/") # install.packages("pwr", lib="/home/tuusuario/R/x86_64-pc-linux-gnu-library/2.15", repos="http://ftp.heanet.ie/mirrors/cran.r-project.org/") } #require(pwr, lib="/home/tuusuario/R/x86_64-pc-linux-gnu-library/2.15" ) require(pwr) # What is the power of a one-tailed t-test, with a # significance level of 0.01, 25 people in each group, # and an effect size equal to 0.75? pwr.t.test(n=25,d=0.75,sig.level=.01,alternative="greater") cat("</br></br>") # Using a two-tailed test proportions, and assuming a # significance level of 0.01 and a common sample size of # 30 for each proportion, what effect size can be detected # with a power of .75? pwr.2p.test(n=30,sig.level=0.01,power=0.75) cat("</br></br>") # For a one-way ANOVA comparing 5 groups, calculate the # sample size needed in each group to obtain a power of # 0.80, when the effect size is moderate (0.25) and a # significance level of 0.05 is employed. pwr.anova.test(k=5,f=.25,sig.level=.05,power=.8) cat("</br></br>") ################################################ {RR}
Two-sample t test power calculation n = 25 d = 0.75 sig.level = 0.01 power = 0.5988572 alternative = greater NOTE: n is number in *each* group
Difference of proportion power calculation for binomial distribution (arcsine transformation) h = 0.8392269 n = 30 sig.level = 0.01 power = 0.75 alternative = two.sided NOTE: same sample sizes
Balanced one-way analysis of variance power calculation k = 5 n = 39.1534 f = 0.25 sig.level = 0.05 power = 0.8 NOTE: n is number in each group