Take a numeric vector and return the predicted vector computed as the arithmetic mean of all elements belonging to a same assembly motif.
amean_byelt_jack(fobs, mOccur, jack)a numeric vector. The vector fobs contains the
quantitative performances 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.
Modelled performances are computed
using arithmetic mean (opt.mean = "amean") of performances.
Assemblages share a same assembly motif (opt.model = "bymot").
Modelled performances are the average
of mean performances of assemblages that contain the same elements
as the assemblage to predict,
except a subset of assemblages.
This procedure corresponds to a linear model with each assembly motif
based on the element occurrence in each assemblage.
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).