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.
validate_amean_bymot_jack(fobs, assMotif, jack)a numeric vector. The vector fobs contains the
quantitative performances of assemblages.
a vector of labels of length(fobs).
The vector assMotif contains the assembly motifs of assemblages.
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.
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.
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).
validate_amean_byelt_jack_xpr,
validate_gmean_bymot_jack_xpr,
validate_gmean_byelt_jack_xpr