NUcluster {NU.Learning}

Hierarchical Clustering of experimental units (such as patients) in X-covariate Space

Description

Form the full, hierarchical clustering tree (dendrogram) for all units (regardless of Treatment/Exposure status) using Mahalonobis distances computed from specified baseline X-covariate characteristics.

Usage

  NUcluster(dframe, xvars, method="ward.D")

Arguments

dframe

Name of data.frame containing baseline X covariates.

xvars

List of names of X variable(s).

method

Hierarchical Clustering Method of "diana", "ward.D", "ward.D2", "complete", "average", "mcquitty", "median" or "centroid".

Details

The first step in applying NU.Learning to data is to hierarchically cluster experimental units in baseline X-covariate space ...thereby creating "Blocks" of relatively well-matched units. NUcluster first calls stats::prcomp() to calculate Mahalanobis distances using standardized and orthogonal Principal Coordinates. NUcluster then uses either the divisive cluster::diana() method or one of seven agglomerative methods from stats::hclust() to compute a dendrogram tree. The hclust function is based on Fortran code contributed to STATLIB by F. Murtagh.

Value

An output list object of class NUcluster, derived from cluster::diana or stats::hclust.

dframe

Name of data.frame containing all baseline X-covariates.

xvars

List of 1 or more X-variable names.

method

Hierarchical Clustering Method: "diana", "ward.D", "ward.D2", "complete", "average", "mcquitty", "median" or "centroid".

hclobj

Hierarchical clustering object created by the designated method.

Author(s)

Bob Obenchain <[e-mail]>

References

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.

Murtagh F. (1985) Multidimensional Clustering Algorithms. COMPSTAT Lectures 4.

Obenchain RL. (2010) Local Control Approach using JMP. Chapter 7 of Analysis of Observational Health Care Data using SAS, Cary, NC:SAS Press, pages 151-192.

Rubin DB. (1980) Bias reduction using Mahalanobis metric matching. Biometrics 36: 293-298.

See Also

NUsetup, ltdagg and lrcagg.

Examples

data(radon)
xvars  = c("obesity", "over65", "cursmoke")
hclobj = NUcluster(radon, xvars)  # ...using default method = "ward.D"
plot(hclobj)