Take a numeric vector and return the predicted vector computed as the arithmetic mean of all elements belonging to the same motif.
validate_amean_byelt_jack(fobs, assMotif, mOccur, 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.
a matrix of occurrence (occurrence of elements).
Its first dimension equals to length(fobs). Its second dimension
equals to the number of elements.
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_bymot_jack,
validate_gmean_bymot_jack,
validate_gmean_byelt_jack