SESOI with different levels of power

Code for Holmes et al. (2017)

#n=2267
#Calculating the smallest partial eta squared the study could detect given smallest effect size f it could detect at 95% power
f95<-c(0.0757429)
eta^2 = f^2 / ( 1 + f^2 )
(f95*f95)/(1+f95*f95)
0.005704262

#Calculating the smallest partial eta squared the study could detect given smallest effect size f it could detect at 80% power
f80<-c(0.0588656)
(f80*f80)/(1+f80*f80)
0.003453193

#Calcualting the 90% CI
#n=2267
#95% power

install.packages("MBESS")
library(MBESS)
ci.pvaf(F.value=0.0757429, df.1=1, df.2=2265, N=2267, conf.level=.90)
$Lower.Limit.Proportion.of.Variance.Accounted.for
0
$Probability.Less.Lower.Limit
0
$Upper.Limit.Proportion.of.Variance.Accounted.for
0.001325457
$Probability.Greater.Upper.Limit
0.05
$Actual.Coverage
0.95

#Calcualting the 90% CI
#n=2267
#80% power
ci.pvaf(F.value=0.0588656, df.1=1, df.2=2265, N=2267, conf.level=.90)
$Lower.Limit.Proportion.of.Variance.Accounted.for
0
$Probability.Less.Lower.Limit
0
$Upper.Limit.Proportion.of.Variance.Accounted.for
0.001207499
$Probability.Greater.Upper.Limit
0.05
$Actual.Coverage
0.95