validate_using_cross_validation: Predicting by cross-validation of assembly performances
In functClust: Functional Clustering of Redundant Components of a System

Description Usage Arguments Details Value See Also

Take a vector fobs of assembly performances over several experiments and return a vector of performances predicted as the mean performances of assemblages that share the same assembly motif.
Assembly motifs are labelled in the vector assMotif. Experiments are labelled in the vector xpr. Modelling options are indicated in opt.mean and opt.model. Occurrence matrix mOccur is used if opt.model = "byelt". Cross-validation is leave-one-out or jackknifesi

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validate_using_cross_validation(fobs, assMotif, mOccur, xpr,
                  opt.mean = "amean", opt.model = "bymot",
                  opt.jack = FALSE, jack = c(3,4)  )

`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.
`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`.
`opt.mean`	switchs to arithmetic formula `opt.mean = "amean"` or geometric formula `opt.mean = "gmean"`.
`opt.model`	switchs to model type: simple mean by assembly motif `opt.model = "bymot"` or linear model with assembly motif `opt.model = "byelt"`.
`opt.jack`	a logical, that switchs towards cross-validation method. If `opt.jack = FALSE`, a "leave-one-out" is used: predicted performances are computed as the mean of performances of assemblages that share a same assembly motif, experiment by experiment, except the only assemblage to predict. If `opt.jack = TRUE`, a jackknife method is used: the set of assemblages belonging to a same assembly motif is divided into `jack[2]` subsets of `jack[1]` assemblages. Predicted performances are computed, experiment by experiment, 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]`, predictions are computed by Leave-One-Out method.
`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.