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.

1 2 3 4 | ```
getConfidenceInterval(result,
alpha=0.1,
nSim=50,
progmethod=NULL)
``` |

`result` |
object of class |

`alpha` |
numeric scalar specifying desired significance of the confidence interval; the width of the resulting interval is 1 - alpha. |

`nSim` |
numeric scalar specifying number of times to resample the data in order to estimate the confidence intervals. |

`progmethod` |
optional function that can be used to report progress. |

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 class `RPPAFit`

, containing updated values for the
slots `lower`

, `upper`

, and `conf.width`

that describe the
confidence interval.

Kevin R. Coombes kcoombes@mdanderson.org, P. Roebuck proebuck@mdanderson.org

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## Not run:
extdata.dir <- system.file("extdata", package="SuperCurveSampleData")
txtdir <- file.path(extdata.dir, "rppaCellData")
akt <- RPPA("Akt.txt", path=txtdir)
design <- RPPADesign(akt,
grouping="blockSample",
controls=list("neg con", "pos con"))
fit.nls <- RPPAFit(akt, design, "Mean.Net")
## N.B.: this takes a while!
fit.nls <- getConfidenceInterval(fit.nls, alpha=0.10, nSim=50)
## End(Not run)
``` |

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