R/ratio.mean.is.R

In cwendorf/dgb: DGB

# DGB
## Ratio of Means from Two (Independent) Samples

ci.ratio.mean.is.data <- function(alpha, y1, y2){
 # Compute confidence interval for a ratio of population means
 # of ratio-scale measurements in a 2-group design. Equality of 
 # variances is not assumed.
 # Arguments:
 #   alpha:  alpha level for 1-alpha confidence
 #   y1      vector of scores for group 1
 #   y2:     vector of scores for group 2
 # Values:
 #   means, mean ratio, lower limit, upper limit
 n1 <- length(y1)
 n2 <- length(y2)
 m1 <- mean(y1)
 m2 <- mean(y2)
 v1 <- var(y1)
 v2 <- var(y2)
 var <- v1/(n2*m1^2) + v2/(n2*m2^2)
 df <- var^2/(v1^2/(m1^4*(n1^3 - n1^2)) + v2^2/(m2^4*(n2^3 - n2^2)))
 tcrit <- qt(1 - alpha/2, df)
 est <- log(m1/m2)
 se <- sqrt(var)
 ll <- exp(est - tcrit*se)
 ul <- exp(est + tcrit*se)
 out <- t(c(m1, m2, exp(est), ll, ul))
 colnames(out) <- c("Mean1", "Mean2", "Mean1/Mean2", "LL", "UL")
 return(out)
}

size.ci.ratio.mean.is <- function(alpha, var, m1, m2, r) {
 # Computes sample size required to estimate a ratio of
 # population means with desired precision in a 2-group design
 # Arguments: 
 #   alpha:  alpha level for 1-alpha confidence 
 #   var:    planning value of average within-group DV variance
 #   m1:     planning value of mean for group 1
 #   m2:     planning value of mean for group 2
 #   r:      desired upper to lower confidence interval endpoint ratio
 # Values:
 #   required sample size per group
 z <- qnorm(1 - alpha/2)
 n <- ceiling(8*var*(1/m1^2 + 1/m2^2)*(z/log(r))^2 + z^2/4)
 return(n)
}