# R/size_n.three_way_mixed_ab_in_c.model_1_c.R In OPDOE: Optimal Design of Experiments

#### Defines functions size_n.three_way_mixed_ab_in_c.model_1_c

```#(size n.three way mixed ab in c. model 1 c)
# Section 3.4.3.1 test factor C in AxB

# 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 C in AxB
size_n.three_way_mixed_ab_in_c.model_1_c <-  function(alpha, beta, delta, a, b, c, cases)
{
n <- 2
dfn <- a*b*(c-1)
dfd <- a*b*c*(n-1)
if (cases == "maximin")
{
lambda <- 0.5*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*b*(c-1)
dfd <- a*b*c*(n-1)
lambda <- ncp(dfn,dfd,alpha,beta)
if (cases == "maximin")
{
n.new <- 2*lambda/(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_C(0.05, 0.1, 0.5, 6, 5, 4, "maximin")
# size.3_4_3_1.test_factor_C(0.05, 0.1, 0.5, 6, 5, 4, "minimin")
```

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OPDOE documentation built on March 18, 2018, 1:23 p.m.