posterior {flowClust}R Documentation

Various Functions for Retrieving Information from Clustering Results

Description

Various functions are available to retrieve the posterior probabilities of clustering memberships z (posterior), the “weights” u (importance), the uncertainty (uncertainty), and the estimates of the cluster means and proportions (getEstimates) resulted from the clustering (filtering) operation.

Usage

posterior(object, assign=FALSE)
importance(object, assign=FALSE)
uncertainty(object)
getEstimates(object)

Arguments

object Object returned from flowClust or filter.
assign A logical value. If TRUE, only the quantity (z for posterior or u for importance) associated with the cluster to which an observation is assigned will be returned. Default is FALSE, meaning that the quantities associated with all the clusters will be returned.

Details

These functions are written to retrieve various slots contained in the object returned from the clustering operation. posterior and importance provide a means to conveniently retrieve information stored in object@z and object@u respectively. uncertainty is to retrieve object@uncertainty, and getEstimates is to retrieve information stored in object@mu (transformed back to the original scale) and object@w.

Value

Denote by K the number of clusters, N the number of observations, and P the number of variables. For posterior and importance, a matrix of size N x K is returned if assign=FALSE (default). Otherwise, a vector of size N is outputted. uncertainty always outputs a vector of size N. getEstimates returns a list with two names elements, locations and proportions. locations is a matrix of size K x P and contains the estimates of the K mean vectors transformed back to the original scale (i.e., rbox(object@mu, object@lambda)). proportions is a vector of size P and contains the estimates of the K cluster proportions.

Author(s)

Raphael Gottardo <raph@stat.ubc.ca>, Kenneth Lo <c.lo@stat.ubc.ca>

References

Lo, K., Brinkman, R. R. and Gottardo, R. (2008) Automated Gating of Flow Cytometry Data via Robust Model-based Clustering. Cytometry A 73, 321-332.

See Also

flowClust, filter, Map

Examples

res <- flowClust(iris[,1:4], K=3)
posterior(res)
posterior(res, assign=TRUE)
importance(res)
importance(res, assign=TRUE)
uncertainty(res)
getEstimates(res)

[Package flowClust version 1.3.2 Index]