UPShclus {LocalControl}R Documentation

Hierarchical Clustering of Patients on X-covariates for Unsupervised Propensiy Scoring

Description

Derive a full, hierarchical clustering tree (dendrogram) for all patients (regardless of treatment received) using Mahalonobis between-patient distances computed from specified baseline X-covariate characteristics.

Usage

UPShclus(envir, dframe, xvars, method, metric)

Arguments

envir

name of the working local control classic environment.

dframe

Name of data.frame containing baseline X covariates.

xvars

List of names of X variable(s).

method

Hierarchical Clustering Method: "diana", "agnes" or "hclus".

metric

A valid distance metric for clustering.

Details

The first step in an Unsupervised Propensity Scoring alalysis is always to hierarchically cluster patients in baseline X-covariate space. UPShclus uses a Mahalabobis metric and clustering methods from the R "cluster" library for this key initial step.

Value

An output list object of class UPShclus:

dframe

Name of data.frame containing baseline X covariates.

xvars

List of names of X variable(s).

method

Hierarchical Clustering Method: "diana", "agnes" or "hclus".

upshcl

Hierarchical clustering object created by choice between three possible methods.

Author(s)

Bob Obenchain <wizbob@att.net>

References

See Also

UPSaccum, UPSnnltd and UPSgraph.


[Package LocalControl version 1.1.4 Index]