#  This is R code for computing sample
#  sizes for 2-sample t-tests.
#
#  It is posted as power2t.R 

# Specity the Type I error level
   alpha <- .05

# Specify the power of the test
   power <- .90

# Compute Type II error level
   beta <- 1 - power

# Specify difference in means to be detected
# divided by the standard deviation for one
# observation
    d <- 0.8     

# Specify the ration of sample sizes n1/n2
    ratio <- 1.0        

# Computations for a two sided alternative

  n2 <- ((1+(1/ratio))*(qnorm(alpha/2) + qnorm(beta))^2)/(d*d);
  n1 <- ratio*n2;

# Round up to next largest interger

  n2 <- ceiling(n2)
  n1 <- ceiling(n1)

# Compute degrees of freedom
  
  v <- n1+n2-2

  n2 <- ((1+(1/ratio))*(qt(alpha/2,v) + qt(beta,v))^2)/(d*d)
  n1 <- ratio*n2

  n2 <- ceiling(n2)
  n1 <- ceiling(n1)

 cat("Sample sizes for two sample t-tests",
    "\n \n Two-sided  alternative",
     "\n \n alpha=",alpha,
     "\n power=", power, 
       "\n scaled difference=",d,
       "\n ratio of sample sizes=", ratio,
        "\n n1=",n1, "\n n2=",n2)
 

 # Compute sample sizes for a one-sided two-sample t-test 

   n2 <- ((1+(1/ratio))*(qnorm(alpha) + qnorm(beta))^2)/(d*d)
   n1 <- ratio*n2
   n2 <- ceiling(n2)
   n1 <- ceiling(n1)

    v <- n1+n2-2

   n2 <- ((1+(1/ratio))*(qt(alpha,v) + qt(beta,v))^2)/(d*d)
   n1 <- ratio*n2
   n2 <- ceiling(n2)
   n1 <- ceiling(n1)

cat("Sample sizes for two sample t-tests",
    "\n \n One-sided  alternative",
     "\n \n alpha=",alpha,
     "\n power=", power, 
       "\n scaled difference=",d,
       "\n ratio of sample sizes=", ratio,
        "\n n1=",n1, "\n n2=",n2)