get_33_curve <- function(type, statistic, df, df2, p.crit=.05, power=1/3) {
# convert r to t values
type <- as.character(type)
statistic[tolower(type)=="r"] <- statistic[tolower(type)=="r"] / sqrt( (1 - statistic[tolower(type)=="r"]^2) / df[tolower(type)=="r"])
type[tolower(type)=="r"] <- "t"
statistic <- abs(statistic)
type <- c("f", "f", "f", "t", "r")
statistic <- c(5.1, 6.3, 7.1, 2.3, 0.4)
df <- c(1, 1, 1, 38, 98)
df2 <- c(88, 100, 200, 38, 98)
ncp <- get_pp_values(type=type, statistic=statistic, df=df, df2=df2, p.crit=.05, power=1/3)$ncp
res <- data.frame()
# ---------------------------------------------------------------------
# t-values
# Critical values,xc, for p=.05, .04, .03, .02 and ,01
t.crit <- list()
t.CRIT <- c(.975, .98, .985, .99, .995)
for (j in 1:5)
t.crit[[j]] <- qt(t.CRIT[j], df=df[type=="t"])
# Probability of a p-value bigger p=.05, .04, .03, .02 and .01 given p<.05 and ncp=ncp33
t.pp <- c()
for (j in 1:5)
t.pp[j] <- mean((1/power)*(pt(t.crit[[j]], df=df[type=="t"], ncp=ncp[ncp$type=="t", "ncp"])-(1-power)))
t.pp[1] <- 0
t.pp <- c(t.pp, 1)
t.prop <- t.pp[2:6]-t.pp[1:5]
# ---------------------------------------------------------------------
# F-values
# Critical values,xc, for p=.05, .04, .03, .02 and ,01
f.crit <- list()
f.CRIT <- c(.95, .96, .97, .98, .99)
for (j in 1:5)
f.crit[[j]] <- qf(f.CRIT[j], df1=df[type=="f"], df2=df2[type=="f"])
# Probability of a p-value bigger p=.05, .04, .03, .02 and .01 given p<.05 and ncp=ncp33
f.pp <- c()
for (j in 1:5)
f.pp[j] <- mean((1/power)*(pf(f.crit[[j]], df1=df[type=="f"], df2=df2[type=="f"], ncp=ncp[ncp$type=="f", "ncp"])-(1-power)))
f.pp[1] <- 0
f.pp <- c(f.pp, 1)
f.prop <- f.pp[2:6]-f.pp[1:5]
# ---------------------------------------------------------------------
# chi2-values
# Critical values,xc, for p=.05, .04, .03, .02 and ,01
chi.crit <- list()
chi.CRIT <- c(.95, .96, .97, .98, .99)
for (j in 1:5)
chi.crit[[j]] <- qt(chi.CRIT[j], df=df[type=="chi2"])
# Probability of a p-value bigger p=.05, .04, .03, .02 and .01 given p<.05 and ncp=ncp33
chi.pp <- c()
for (j in 1:5)
chi.pp[j] <- mean((1/power)*(pchisq(chi.crit[[j]], df=df[type=="chi2"], ncp=ncp[ncp$type=="chi2", "ncp"])-(1-power)))
chi.pp[1] <- 0
chi.pp <- c(chi.pp, 1)
chi.prop <- chi.pp[2:6]-chi.pp[1:5]
#TODO: z-values!
}
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