R/prop.ps.R

In cwendorf/dgb: DGB

# DGB
## Proportions from Paired Samples

ci.prop.ps <- function(alpha, f12, f21, n) {
 # Computes adjusted Wald confidence interval for difference of
 # population proportions in 2-level within-subjects design
 # Arguments:
 #   alpha:  alpha level for 1-alpha confidence
 #   f12:    number of participants who have attribute 
 #           in condition 1 but not condition 2
 #   f21:    number of participants who have attribute
 #           in condition 2 but not in condition 1
 #   n:      sample size
 # Values:
 #   lower limit, upper limit
 z <- qnorm(1 - alpha/2)
 p12 <- (f12 + 1)/(n + 2)
 p21 <- (f21 + 1)/(n + 2)
 se <- sqrt(((p12 + p21) - (p12 - p21)^2)/(n + 2))
 LL <- p12 - p21 - z*se
 UL <- p12 - p21 + z*se
 CI <- c(LL, UL)
 return(CI)
}

size.ci.prop.ps <- function(alpha, p1, p2, phi, w) {
 # Computes sample size required to estimate a difference of proportions
 # in 2-level within-subjects 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
 #   phi:    planning value of phi coefficient
 #   w:      desired confidence interval width
 # Values:
 #   required sample size
 z <- qnorm(1 - alpha/2)
 cov <- phi*sqrt(p1*p2*(1 - p1)*(1 - p2))
 n <- ceiling(4*(p1*(1 - p1) + p2*(1 - p2) - 2*cov)*(z/w)^2)
 return(n)
}