Takes a vector fct of assembly performances
over several experiments
and returns 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
predict_performance(appFct, appMotifs, appOccur,
supMotifs, supOccur,
opt.mean = "amean",
opt.model = "bymot" )a vector of numeric values (assembly properties).
a vector of labels of length(fct) (assembly motifs).
a matrix of occurrence (occurrence of components).
Its first dimension equals to length(fct). Its second dimension
equals to the number of components.
a vector of labels of length(fct) (assembly motifs).
a matrix of occurrence (occurrence of components).
Its first dimension equals to length(fct). Its second dimension
equals to the number of components.
equal to "amean" (by default) or "gmean".
equal to "bymot" (by default) or "byelt".
Return the arithmetic mean of a vector, as standard mean
function.
Prediction is computed using arithmetic mean amean by motif
bymot in a whole (WITHOUT taking into account species contribution).
The components belonging to a same motif are divided into jack[2]
subsets of jack[1] components. Prediction is computed by excluding
jack[1] components, of which the component to predict. If the total
number of components belonging to the motif is lower than
jack[1]*jack[2], prediction is computed by Leave-One-Out (LOO).