expectedUtility: Expected utility of an ID mapping, ID filtering, or other...
In IdMappingAnalysis: ID Mapping Analysis

Description Usage Arguments Details Value

expectedUtility calculates mean expected utility and total expected utility across pairs of features from two bioinformatics platforms. It is used to evaluate an ID mapping, ID filtering, or other bioinformatics data preparation method.

  expectedUtility(dataset, label = "",
    bootModelCorClusters,
    columnsToRemove = c("Utp", "Lfp", "deltaPlus", "pi1Hat"),
    Utp, Lfp, deltaPlus, guarantee = 1e-09)

`dataset`	A data frame or list from a call to `fit2clusters`, the posterior probabilities for each observation, their variance estimates. See Details.
`label`	A text string describing the method being studied, to label the return value. This is handy for using `rbind` to combine results for different methods.
`bootModelCorClusters`	Source for mixture model estimates. If missing, extracted from calling frame.
`columnsToRemove`	Names of columns to remove from return value.
`Utp`	Utility of a true positive.
`Lfp`	Loss of a false positive.
`deltaPlus`	Parameter defined as Pr("+" \| "+" or "0")
`guarantee`	Minimum value for posterior probability.

The input dataset should be a dataframe with one row per ID pair, and the following columns:

Utp Utility of a true positive.
Lfp Loss of a false positive.
postProb The posterior probabilities for each observation
postProbVar The variances of the posterior probabilities, usually estimated from the bootstrap using Boot

A data frame with just one row. The columns are:

`Utp`	Utility of a true positive.
`Lfp`	Loss of a false positive.
`deltaPlus`	Parameter defined as Pr("+" \| "+" or "0")
`deltaZero`	Parameter defined as Pr("0" \| "0" or "x")
`nPairs`	Number of ID pairs selected by the method.
`pi1Hat`	The estimate of the probability of the high-correlation component; obtained from
`PrPlus`	Estimated probability that an ID pair is in the "+" group.
`PrTrue`	Estimated probability that an ID pair is in the "+" or "0" group: `PrPlus/deltaPlus`
`PrFalse`	Estimated probability that an ID pair is in the "-" group.
`Utrue`	The component of expected utility from "true positives": `PrTrue * Utp`.
`Lfalse`	The (negative) component of expected utility from "false positives": `PrFalse * Lfp`.
`Eutility1`	The average expected utility per ID pair: Utrue-Lfalse.
`Eutility`	The total expected utility, summing over ID pairs: `nrow(dataset)*Eutility1`.