posterior {flowClust} | R Documentation |
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.
posterior(object, assign=FALSE) importance(object, assign=FALSE) uncertainty(object) getEstimates(object)
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. |
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
.
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.
Raphael Gottardo <raph@stat.ubc.ca>, Kenneth Lo <c.lo@stat.ubc.ca>
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.
res <- flowClust(iris[,1:4], K=3) posterior(res) posterior(res, assign=TRUE) importance(res) importance(res, assign=TRUE) uncertainty(res) getEstimates(res)