UPShclus {USPS} | R Documentation |
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.
hclobj <- UPShclus(dframe, xvars, method="diana")
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". |
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.
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. |
Bob Obenchain <wizbob@att.net>
Kaufman L, Rousseeuw PJ. (1990) Finding Groups in Data. An Introduction to Cluster Analysis. New York: John Wiley and Sons.
Kereiakes DJ, Obenchain RL, Barber BL, et al. (2000) Abciximab provides cost effective survival advantage in high volume interventional practice. Am Heart J 140: 603-610.
Obenchain RL. (2004) Unsupervised Propensity Scoring: NN and IV Plots. Proceedings of the American Statistical Association (on CD) 8 pages.
Obenchain RL. (2009) USPSinR.pdf ../R_HOME/library/USPS 40 pages.
Rubin DB. (1980) Bias reduction using Mahalanobis metric matching. Biometrics 36: 293-298.
UPSaccum
, UPSnnltd
and UPSgraph
.
data(lindner) UPSxvars <- c("stent", "height", "female", "diabetic", "acutemi", "ejecfrac", "ves1proc") UPSharch <- UPShclus(lindner, UPSxvars) plot(UPSharch)