membership {clue} | R Documentation |
Compute the memberships values for objects representing partitions.
cl_membership(x, k = n_of_classes(x)) as.cl_membership(x) as.cl_hard_partition(x)
x |
an R object representing a partition of objects. |
k |
an integer giving the number of columns (corresponding to class ids) to be used in the membership matrix. Must not be less, and default to, the number of classes in the partition. |
cl_membership
is a generic function.
The methods provided in package clue handle the partitions obtained from clustering functions in the base R distribution, as well as packages cclust, cluster, e1071, and mclust (and of course, clue itself).
as.cl_membership
can be used for coercing “raw” class
ids (given as atomic vectors) or membership values (given as numeric
matrices) to membership objects.
An object of class "cl_membership"
with the matrix of
membership values.
For as.cl_hard_partition
, an object of class
"cl_membership"
with the membership values of the hard
partition obtained by taking the class ids of the (first) maximal
membership values.
## Getting the memberships of a single soft partition. d <- dist(USArrests) hclust_methods <- c("ward", "single", "complete", "average", "mcquitty", "median", "centroid") hclust_results <- lapply(hclust_methods, function(m) hclust(d, m)) ## Now create an ensemble from the results. hens <- cl_ensemble(list = hclust_results) names(hens) <- hclust_methods ## Create a dissimilarity object from this. d1 <- cl_dissimilarity(hens) ## And compute a soft partition. require("cluster") party <- fanny(d1, 2) cl_membership(party) ## The "nearest" hard partition to this: as.cl_hard_partition(party) ## (which has the same class ids as cl_class_ids(party)). ## Converting all elements in an ensemble of partitions to their ## memberships. pens <- cl_boot(USArrests, 30, 3) pens pens <- cl_ensemble(list = lapply(pens, cl_membership)) pens pens[[length(pens)]]