validate_gmean_byelt_jack_xpr: Predicting the performances by elements occurring within...
In functClust: Functional Clustering of Redundant Components of a System

Description Usage Arguments Details Value See Also

Take a vector fobs of assembly performances and return a vector of performances predicted as the geometric mean of performances of other assemblages that share the same assembly motif. Assembly motifs are described in the vector assMotif. Experiments are specified in the vector xpr.

1	validate_gmean_byelt_jack_xpr(fobs, assMotif, mOccur, jack, xpr)

`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.
`mOccur`	a matrix of occurrence (occurrence of elements). Its first dimension equals to `length(fobs)`. Its second dimension equals to the number of elements.
`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`.

Predicted performances are computed using geometric mean (opt.mean = "gmean") 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).

Return a vector of length(fobs). Its values are computed as the geometric mean of performances of assemblages that share a same assembly motif, by excluding a subset of assemblages containing the assemblage to predict.

validate_amean_bymot_jack_xpr, validate_amean_byelt_jack_xpr, validate_gmean_bymot_jack_xpr

functClust documentation built on Dec. 2, 2020, 5:06 p.m.