Nothing

```
GeneticPower.Quantitative.Numeric <- function(N=1000,
delta=1,
freq=0.15,
minh=c('additive','dominant','recessive'),
sigma=1,
OtherParms=0,
alpha=0.05,
numtests=1,
moi=NULL,
rsquared=NULL)
{
## N = total samples in the analysis
## delta = mean(bb) - mean (AA), where 'b' is the disease allele, 'A' is the reference allele
## freq = allele frequency of disease allele 'b'
## minh = mode of inheritance: "recessive", "additive", "dominant" same as moi=0,0.5,and 1.0, respectively.
## defaults to "additive" if no moi specified either
## sigma = standard deviation of the response phenotype
## OtherParms = the number of additional parameters (really, DOF) in the model that will reduce your overall DOF
## alpha = the desired significance level
## numtests = the number of tests to be corrected by Bonferroni adjustment beforee achieving 'alpha'
## moi = mode of inheritance: 0 for recessive, 0.5 for additive, 1.0 for dominant, or anywhere in between,
## this OVER-RIDES minh...useful for modeling in-between moi's...
## rsquared = fraction of total sum-of-squares explained by fit. OVER-RIDES delta AND sigma.
alphy <- 1-(1-alpha)^(1/numtests);
if (is.null(moi)) {
minh <- match.arg(minh) # can use abbreviated names
moi <- switch(minh,
additive = 0.5,
dominant = 1.0,
recessive = 0 ) # define the mode of inheritance
}
N1 <- N*(1-freq)^2;
N2 <- 2*N*freq*(1-freq);
N3 <- N*freq^2;
mu <- c(0,moi,1)*delta;
if (is.null(rsquared)) {
xbar <- (N2 + N3*2)/N;
ybar <- (N2*mu[2] + N3*mu[3])/N;
slope <- (N1*xbar*ybar + (1-xbar)*N2*(mu[2]-ybar) + (2-xbar)*N3*(mu[3]-ybar))/(N1*xbar^2 + N2*(1-xbar)^2 + N3*(2-xbar)^2);
a <- ybar - slope*xbar;
fits <- a + slope*c(0,1,2);
eNs <- c(N1,N2,N3);
Rsqtop <- sum(eNs*(fits-ybar)^2);
Rsqbottom <- Rsqtop + sum((eNs-1)*sigma^2 + eNs*(mu - fits) + eNs*(mu-fits)^2);
NewRsquared <- Rsqtop/Rsqbottom;
} else {
NewRsquared <- rsquared;
}
power <- pf(qf(1-alphy,df1=1,df2=(N-2-OtherParms)),ncp=(NewRsquared*(N-2)/((1-NewRsquared))),df1=1,df2=(N-2-OtherParms),lower.tail=F);
return(power);
}
```

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