R/prop.is.R

In cwendorf/DGB: DGB

# DGB
## Proportions from Two (Independent) Samples

ci.prop.is <- function(alpha, f1, f2, n1, n2) {
 # Computes adjusted Wald confidence interval for difference of
 # population proportions in 2-group design
 # Arguments:
 #   alpha:  alpha level for 1-alpha confidence
 #   f1:     number of participants in group 1 with attribute
 #   f2:     number of participants in group 2 with attribute
 #   n1:     sample size of group 1
 #   n2:     sample size of group 2
 # Values:
 #   MLEs, SE, lower limit, upper limit
 z <- qnorm(1 - alpha/2)
 mle1 <- f1/n1
 mle2 <- f2/n2
 p1 <- (f1 + 1)/(n1 + 2)
 p2 <- (f2 + 1)/(n2 + 2)
 se <- sqrt(p1*(1 - p1)/(n1 + 2) + p2*(1 - p2)/(n2 + 2))
 LL <- p1 - p2 - z*se
 UL <- p1 - p2 + z*se
 out <- t(c(mle1, mle2, se, LL, UL))
 colnames(out) <- c("prop1", "prop2", "SE", "LL", "UL")
 return(out)
}

size.ci.prop.is <- function(alpha, p1, p2, w) {
 # Computes sample size per group required to estimate a difference
 # of proportions in 2-group design with desired precision
 # Arguments: 
 #   alpha:  alpha level for 1-alpha confidence 
 #   p1:     planning value of proportion for group 1
 #   p2:     planning value of proportion for group 2
 #   w:      desired confidence interval width
 # Values:
 #   required sample size
 z <- qnorm(1 - alpha/2)
 n <- ceiling(4*(p1*(1 - p1) + p2*(1 - p2))*(z/w)^2)
 return(n)
}

size.test.prop.is <- function(alpha, power, p1, p2, es) {
 # Computes sample size per group required to test equality
 # of proportions in 2-group design with desired power
 # Arguments: 
 #   alpha:  alpha level for 1-alpha confidence 
 #   power:  desired power of hypothesis test
 #   p1:     planning value of proportion for group 1
 #   p2:     planning value of proportion for group 2
 #   es:     expected effect size
 # Values:
 #   required sample size
 za <- qnorm(1 - alpha/2)
 zb <- qnorm(power)
 n <- ceiling((p1*(1 - p1) + p2*(1 - p2))*(za + zb)^2/es^2)
 return(n)
}