miscellaneous {flowClust} | R Documentation |
Various functions are available to retrieve the information criteria (criterion
), the posterior probabilities of clustering memberships z (posterior
), the “weights” u (importance
), the uncertainty (uncertainty
), and the estimates of the cluster proportions, means and variances (getEstimates
) resulted from the clustering (filtering) operation.
criterion(object, ...) criterion(object) <- value posterior(object, assign=FALSE) importance(object, assign=FALSE) uncertainty(object) getEstimates(object, data)
object |
Object returned from flowClust or
filter . For the replacement method of
criterion , the object must be of class
flowClustList or tmixFilterResultList . |
... |
Further arguments. Currently this is type , a
character string. May take "BIC" , "ICL" or
"logLike" , to specify the criterion desired. |
value |
A character string stating the criterion used to choose
the best model. May take either "BIC" or "ICL" . |
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. |
data |
A numeric vector, matrix, data frame of observations, or
object of class flowFrame ; an optional argument. This is the
object on which flowClust or
filter was performed. |
These functions are written to retrieve various slots contained in the object returned from the clustering operation. criterion
is to retrieve object@BIC
, object@ICL
or object@logLike
. It replacement method modifies object@index
and object@criterion
to select the best model according to the desired criterion. posterior
and importance
provide a means to conveniently retrieve information stored in object@z
and object@u
respectively. uncertainty
is to retrieve object@uncertainty
. getEstimates
is to retrieve information stored in object@mu
(transformed back to the original scale) and object@w
; when the data object is provided, an approximate variance estimate (on the original scale, obtained by performing one M-step of the EM algorithm without taking the Box-Cox transformation) will also be computed.
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 named elements, proportions
, locations
and, if the data object is provided, dispersion
. proportions
is a vector of size P and contains the estimates of the K cluster 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)
). dispersion
is an array of dimensions K x P x P, containing the approximate estimates of the K covariance matrices on the original scale.
When object@nu=Inf
, the Mahalanobis distances instead of the “weights” are stored in object@u
. Hence, importance
will retrieve information corresponding to the Mahalanobis distances.
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) criterion(res) posterior(res) posterior(res, assign=TRUE) importance(res) importance(res, assign=TRUE) uncertainty(res) getEstimates(res)