R: Predicting the performances by assembly motif using jackknife...

validate_amean_bymot_jack_xpr

R Documentation

Predicting the performances by assembly motif using jackknife method over several experiments

Description

Take a vector fobs of assembly performances and return a vector of performances predicted as the arithmetic mean of performances of other assemblages that share the same assembly motif.

Assembly motifs are labelled in the vector assMotif. Experiments are labelled in the vector xpr.

Usage

validate_amean_bymot_jack_xpr(fobs, assMotif, jack, xpr)

Arguments

`fobs`	a numeric vector. The vector `fobs` contains the quantitative performances of assemblages.
`assMotif`	a vector of labels of `length(fobs)`. The vector `assMotif` contains the assembly motifs of assemblages.
`jack`	an integer vector of length `2`. The vector specifies the parameters for jackknife method. The first integer `jack[1]` specifies the size of subset, the second integer `jack[2]` specifies the number of subsets.
`xpr`	a vector of numerics of `length(fobs)`. The vector `xpr` contains the weight of each experiment, and the labels (in `names(xpr)`) of different experiments. The weigth of each experiment is used in the computation of the Residual Sum of Squares in the function `rss_clustering`. The used formula is `rss` if each experiment has the same weight. The used formula is `wrss` (barycenter of RSS for each experiment) if each experiment has different weights. All assemblages that belong to a given experiment should then have a same weigth. Each experiment is identified by its names (`names(xpr)`) and the RSS of each experiment is weighted by values of `xpr`. The vector is generated by the function `stats::setNames`.

Details

Predicted performances are computed using arithmetic mean (opt.mean = "amean") of performances of assemblages that share a same assembly motif (opt.model = "bymot").

The assemblages belonging to a same assembly motif are divided into jack[2] subsets of jack[1] assemblages. Prediction is computed by excluding jack[1] assemblages, including the assemblage to predict. If the total number of assemblages belonging to the assembly motif is lower than jack[1]*jack[2], prediction is computed by Leave-One-Out (LOO).

Value