criterion {flowClust} | R Documentation

## Various Functions for Retrieving Information from Clustering Results

### Description

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.

### Usage

    criterion(object, ...)

    ## S4 method for signature 'flowClust'
    criterion(object, type = "BIC")

    ## S4 method for signature 'flowClustList'
    criterion(object, type = "BIC", max = FALSE,
      show.K = FALSE)

    criterion(object) <- value

    ## S4 replacement method for signature 'flowClustList,character'
    criterion(object) <- value

    posterior(object, assign = FALSE)

    importance(object, assign = FALSE)

    uncertainty(object)

    getEstimates(object, data)

### Arguments

`object` | Object returned from `flowClust` or `filter`. For the replacement method of `criterion`, the object must be of class `flowClustList` or `tmixFilterResultList`.
`type, value` | A character string stating the criterion used to choose the best model. May take either `"BIC"` or `"ICL"`.
`max` | whether `criterion` should return the max value
`show.K` | whether `criterion` should return K
`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.
`...` | Further arguments. Currently this is `type`, a character string. May take `"BIC"`, `"ICL"` or `"logLike"`, to specify the criterion desired.

### Details

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.

### 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 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.

### Note

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. the `assign` argument is set to `TRUE`, only the quantities corresponding to assigned observations will be returned. Quantities corresponding to unassigned observations (outliers and filtered observations) will be reported as `NA`. Hence, A change in the rule to call outliers will incur a change in the number of `NA` values returned.

### 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`

[Package flowClust version 3.18.0 Index]