R/lc.stdmean.bs.R

In cwendorf/dgb: DGB

# DGB
## Standardized Linear Contrast of Means (Between-Subjects)

ci.lc.stdmean.bs <- function(alpha, m, sd, n, c) {
 # Computes confidence interval for a population standardized linear 
 # function of means in a between-subjects design
 # Arguments: 
 #   alpha: alpha level for 1-alpha confidence
 #   m:     vector of sample means
 #   sd:    vector of sample standard deviation
 #   n:     vector of sample sizes
 #   c:     vector of contrast coefficients
 # Values:
 #   estimate, SE, lower limit, upper limit
 z <- qnorm(1 - alpha/2)
 v <- sd^2
 a <- length(m)
 s <- sqrt(sum(v)/a)
 df <- sum(n) - a
 sp <- sqrt(sum((n-1)*v)/df)
 est1 <- (t(c)%*%m)/s
 est2 <- (t(c)%*%m)/sp
 a1 <- est1^2/(a^2*s^4)
 a2 <- a1*sum((v^2/(2*(n - 1))))
 a3 <- sum((c^2*v/(n - 1)))/s^2
 se1 <- sqrt(a2 + a3)
 ll1 <- est1 - z*se1
 ul1 <- est1 + z*se1
 a1 <- est2^2/a^2
 a2 <- a1*sum(1/(2*(n - 1)))
 a3 <- sum(c^2/n)
 se2 <- sqrt(a2 + a3)
 ll2 <- est2 - z*se2
 ul2 <- est2 + z*se2
 out1 <- t(c(est1, se1, ll1, ul1))
 out2 <- t(c(est2, se2, ll2, ul2))
 out <- rbind(out1, out2)
 colnames(out) <- c("Estimate", "SE", "LL", "UL")
 rownames(out) <- c("Equal Variances Not Assumed:", "Equal Variances Assumed:")
 return(out)
}

size.ci.lc.stdmean.bs <- function(alpha, d, w, c) {
 # Computes sample size required to estimate a standardized linear contrast 
 # of population means with desired precision in a between-subjects design
 # Arguments: 
 #   alpha: alpha level for 1-alpha confidence
 #   d:     planning value of standardized linear contrast  
 #   w:     desired confidence interval width
 #   c:     vector of contrast coefficients 
 # Values:
 #   required sample size per group
 z <- qnorm(1 - alpha/2)
 a <- length(c)
 n <- ceiling((2*d^2/a + 4*(t(c)%*%c))*(z/w)^2)
 return(n)
}