| getConfidenceInterval {SuperCurve} | R Documentation |
This function computes confidence intervals for the estimated concentrations in a four-parameter logistic model fit to a set of dilution series in a reverse-phase protein array experiment.
getConfidenceInterval(result, alpha = 0.1, nSim = 50)
result |
A RPPAFit object representing the
result of fitting a four-parameter logistic model |
alpha |
The desired significance of the confidence interval; the width of the resulting interval is 1 - alpha. |
nSim |
The number of times to resample the data in order to estimate the confidence intervals. |
In order to compute the confidence intervals, the function assumes
that the errors in the observed Y intensities are independent
normal values, with mean centered on the estimated curve and standard
deviation that varies smoothly as a function of the (log)
concentration. The smooth function is estimated using
loess. The residuals are resampled from this estimate
and the model is refit; the confidence intervals are computed
empirically as symmetrically defined quantiles of the refit parameter
sets.
An object of the RPPAFit class, containing updated
values for the slots lower, upper, and
conf.width that describe the confidence interval.
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!
fit.nls <- getConfidenceInterval(fit.nls, alpha=0.10, nSim=50)