validate_ftree: Predictions of assembly performances using a species...
In functClust: Functional Clustering of Redundant Components of a System

Description Usage Arguments Details Value

Take a hierarchical tree of species clustering, a matrix of occurrency and the corresponding vector of performances, and return the predictions, statistics and other informations.

validate_ftree(tree.I, fobs, mOccur,
              xpr = stats::setNames(rep(1, length(fobs)),
                                    rep("a", length(fobs))),
              opt.method = "divisive", opt.mean = "amean",
              opt.model = "byelt",
              opt.jack   = FALSE,
              jack       = as.integer(c(3, 4)),
              opt.nbMax  = dim(mOccur)[2])

`tree.I`	an integer square-matrix. The matrix represents a hierarchical tree of species clustering.
`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.
`xpr`	a vector of numerics of `length(fobs)`. The vector `xpr` contains the weight of each experiment, and the labels (in `names(xpr)`) of different experiments. The weigth of each experiment is used in the computation of the Residual Sum of Squares in the function `rss_clustering`. The used formula is `rss` if each experiment has the same weight. The used formula is `wrss` (barycenter of RSS for each experiment) if each experiment has different weights. All assemblages that belong to a given experiment should then have a same weigth. Each experiment is identified by its names (`names(xpr)`) and the RSS of each experiment is weighted by values of `xpr`. The vector `xpr` is generated by the function `stats::setNames`.
`opt.method`	a string that specifies the method to use. `opt.method = c("sort", "divisive", "agglomerative", "cluster")`. The three first methods generate hierarchical trees. Each tree is complete, running from a unique trunk to as many leaves as components. The last method generates, at each level of the tree, a clustering of components into a given, predefined number of clusters. Because it is repeated from the trunk until to leaves, by increasing the number of clusters, the method generates a non-hierarchical tree. If `opt.method = "sort"`, the components are sorted by their effect of assemblage performances. If `opt.method = "divisive"`, the components are clustered according to a hierarchical process by using a divisive method, from the trivial cluster where all components are together, towards the clustering where each component is a cluster. If `opt.method = "agglomerative"`, the components are clustered according to a hierarchical process by using an agglomerative method, from the trivial clustering where each component is a clsuter, towards the cluster where all components are together. The method that gives the best result is `opt.method = "divisive"`. If `opt.method = "cluster"`, the components are clustered according to a non-hierarchical process by using the method of McNaughton-Smith et al., 1964. In this case, one must specify the number of wished clusters. Recall that, if `affectElt` is specified, the option `opt.method` does not need to be filled out. `affectElt` determines a level of component clustering, and a tree is built: (i) by using `opt.method = "divisive"` from the defined level in tree towards as many leaves as components; (ii) by using `opt.method = "agglomerative"` from the defined level in tree towards the trunk of tree.
`opt.mean`	a character equals to `"amean"` or `"gmean"`. Switch to arithmetic formula if `opt.mean = "amean"`. Switch to geometric formula if `opt.mean = "gmean"`.
`opt.model`	a character equals to `"bymot"` or `"byelt"`. Switch to simple mean by assembly motif if `opt.model = "bymot"`. Switch to linear model with assembly motif if `opt.model = "byelt"`.
`opt.jack`	a logical, that switchs towards cross-validation method. If `opt.jack = FALSE`, a "leave-one-out" is used: predicted performances are computed as the mean of performances of assemblages that share a same assembly motif, experiment by experiment, except the only assemblage to predict. If `opt.jack = TRUE`, a jackknife method is used: the set of assemblages belonging to a same assembly motif is divided into `jack[2]` subsets of `jack[1]` assemblages. Predicted performances are computed, experiment by experiment, 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]`, predictions are computed by Leave-One-Out method.
`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.
`opt.nbMax`	an integer, that indicates the maximum number of tree levels to cluster. By default, `opt.nbMax = nbElt` for clustering components all along the tree, from the trunc to the leaves, to be able to determine the optimum number of component functional groups. However, in `ftest_` and `fboot_` functions, there is no point in cluster the components beyond the optimum number of functional groups. In these functions, `opt.nbMax =` optimum number of functional groups, by default.