R/mean.os.R

In cwendorf/dgb: DGB

# DGB
## Mean from One Sample

ci.mean.os <- function(alpha, m, sd, n) {
 # Computes confidence interval for a population mean
 # Arguments: 
 #   alpha: alpha level for 1-alpha confidence
 #   m:     sample mean
 #   sd:    sample standard deviation
 #   n:     sample size
 # Values:
 #   estimate of population mean, SE, lower limit and upper limit
 df <- n - 1
 tcrit <- qt(1 - alpha/2, df)
 se <- sd/sqrt(n)
 ll <- m - tcrit*se
 ul <- m + tcrit*se
 out <- t(c(m, se, ll, ul))
 colnames(out) <- c("Estimate", "SE",  "LL", "UL")
 return(out)
}

size.ci.mean.os <- function(alpha, var, w) {
 # Computes sample size required to estimate a population 
 # mean with desired precision in a 1-group design
 # Arguments: 
 #   alpha:  alpha level for 1-alpha confidence 
 #   var:    planning value of DV variance
 #   w:      desired confidence interval width
 # Values:
 #   required sample size
 z <- qnorm(1 - alpha/2)
 n <- ceiling(4*var*(z/w)^2 + z^2/2)
 return(n)
}

size.test.mean.os <- function(alpha, var, pow, es) {
 # Computes sample size required to test a population   
 # mean with desired power in a 1-group design
 # Arguments: 
 #   alpha:  alpha level for test 
 #   var:    planning value of DV variance
 #   pow:    desired power
 #   es:     planning value of mean minus null hypothesis value
 # Values:
 #   required sample size
 za <- qnorm(1 - alpha/2)
 zb <- qnorm(pow)
 n <- ceiling(var*(za + zb)^2/es^2 + za^2/2)
 return(n)
}