R/size_n.three_way_mixed_ab_in_c.model_1_a.R

In OPDOE: Optimal Design of Experiments

#(size n.three way mixed ab in c. model 1 a)
# Section 3.4.3.1 test factor A

 
# Three way mixed classification. (AxB)>C, Model I
# Factor A, B, C are fixed. Determining n
# a, b, and c are given. Testing hypothesis about factor A
size_n.three_way_mixed_ab_in_c.model_1_a <-  function(alpha, beta, delta, a, b, c, cases)
{
       n <- 2
       dfn <- (a-1)
       dfd <- a*b*c*(n-1)
       if (cases == "maximin")
       {
              lambda <- 0.5*b*c*n*delta*delta
       }
       else if (cases == "minimin")
      {
              lambda <- 0.25*a*b*c*n*delta*delta
      }
      beta.calculated <- Beta(alpha, dfn, dfd, lambda)
      if (is.nan(beta.calculated) || beta.calculated < beta )
      {
   warning(paste("Given parameter will result in too high power.",
                 "To continue either increase the precision or ",
                 "decrease the level of factors."))
               return(NA)
      }
      else
      {
             n <- 5    
             n.new <- 1000
             while (abs(n -n.new)>1e-6)
            {
                  n <- n.new
                  dfn <- (a-1)
                  dfd <- a*b*c*(n-1)
                  lambda <- ncp(dfn,dfd,alpha,beta)
                  if (cases == "maximin")
                  {
                        n.new <- 2*lambda/(b*c*delta*delta)                              
                  }
                  else if (cases == "minimin")
                  {
                        n.new <- 4*lambda/(a*b*c*delta*delta)
                  }
           }  
           return(ceiling(n.new))
    }
}


# example
# size.3_4_3_1.test_factor_A(0.05, 0.1, 0.5, 6, 5, 4, "maximin")
# size.3_4_3_1.test_factor_A(0.05, 0.1, 0.5, 6, 5, 4, "minimin")