powercalc {SuperCurve} | R Documentation |
This function estimates the power to detect a difference in the mean log concentration for two groups of samples in a reverse-phase protein array experiment.
powercalc(n, dilnFit, upper = 0.95, lower = 0.50, nrep = 1000, alpha = 0.05)
dilnFit |
A RPPAFit object representing the
result of fitting a four-parameter logistic model |
n |
size of the sample to be drawn |
upper |
quantile of the concentration on the upper end of the curve |
lower |
quantile fo the concentraion on the lower end of the curve |
nrep |
the number of simulations to perform in order to estiamte the power |
alpha |
significance level at which the power is computed |
The powercalc
function estimates the power to detect a
difference between the upper
and lower
quantiles of log
concentration at the specified significance level α,
assuming the samples come from two groups each of size n
. The
power calculation is based on a Wilcoxon rank sum test. The
computation assumes that the only variability arises from the
measurement error, as estimated by the residuals from the
RPPAFit
; thus, it will overestimate the power if
there is additional biological variability within the two groups.
Returns a real number, the power.
Kevin R. Coombes <kcoombes@mdanderson.org>
KRC
path <- system.file("rppaCellData", package="SuperCurve") akt <- RPPA("Akt.txt", path=path) design <- RPPADesign(akt, grouping="blockSample", controls=list("neg con", "pos con")) fit.nls <- RPPAFit(akt, design, "Mean.Net") # Warning: this takes a while! powercalc(10, fit.nls, upper=0.75, lower=0.25)