R/mean.ps.R

In cwendorf/DGB: DGB

# DGB
## Mean Difference from Paired Samples

ci.mean.ps <- function(alpha, m1, m2, sd1, sd2, n1, n2, r) {
 # Computes confidence interval for a 
 # difference in population means in a paired-samples design
 # Arguments: 
 #   alpha: alpha level for 1-alpha confidence
 #   mj:    sample mean for measure j
 #   sdj:   sample standard deviation for measure j
 #   nj:    sample size for measure j
 #   r:     correlation between the two measures
 # Values:
 #   estimate, SE, df, lower limit, upper limit
 md <- m2-m1
 se1 <- sd1/n1
 se2 <- sd2/n2
 se <- sqrt(se1^2+se2^2-2*r*se1*se2)
 df <- n1-1
 tcrit <- qt(alpha/2,df,lower.tail=FALSE)
 ll <- md-tcrit*se
 ul <- md+tcrit*se
 out <- t(c(md, se, df, ll, ul))
 colnames(out) <- c("Estimate", "SE", "df", "LL", "UL")
 return(out)  
}

size.ci.mean.ps <- function(alpha, var, cor, w) {
 # Computes sample size required to estimate a difference in 
 # population means with desired precision in a paired-samples design
 # Arguments: 
 #   alpha:  alpha level for 1-alpha confidence 
 #   var:    planning value of average within-group DV variance
 #   cor:    planning value of correlation
 #   w:      desired confidence interval width
 # Values:
 #   required sample size
 z <- qnorm(1 - alpha/2)
 n <- ceiling(8*(1 - cor)*var*(z/w)^2 + z^2/2)
 return(n)
}

size.test.mean.ps <- function(alpha, var, pow, es, cor) {
 # Computes sample size required to test a difference in population
 # means with desired power in a paired-samples design 
 # Arguments: 
 #   alpha:  alpha level for test 
 #   var:    planning value of average within-group DV variance
 #   pow:    desired power
 #   es:     planning value of mean difference 
 #   cor:    planning value of correlation
 # Values:
 #   required sample size
 za <- qnorm(1 - alpha/2)
 zb <- qnorm(pow)
 n <- ceiling(2*var*(1 - cor)*(za + zb)^2/es^2 + za^2/2)
 return(n)
}