validate_amean_bymot_jack: Predicting the performances by assembly motif using jackknife...
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 arithmetic mean of performances of other assemblages that share the same assembly motif.

Assembly motifs are labelled in the vector assMotif.

1	validate_amean_bymot_jack(fobs, assMotif, jack)

`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.

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).

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

validate_amean_byelt_jack_xpr, validate_gmean_bymot_jack_xpr, validate_gmean_byelt_jack_xpr

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