amean_byelt_jack: Arithmetic mean by elements occurring within assembly motif...
In functClust: Functional Clustering of Redundant Components of a System

Description Usage Arguments Details See Also

Take a numeric vector and return the predicted vector computed as the arithmetic mean of all elements belonging to a same assembly motif.

1	amean_byelt_jack(fobs, mOccur, jack)

`fobs`	a numeric vector. The vector `fobs` contains the quantitative performances 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.
`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.

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).

amean_bymot_jack, gmean_bymot_jack, gmean_byelt_jack

functClust documentation built on Dec. 2, 2020, 5:06 p.m.