Form the full, hierarchical clustering tree (dendrogram) for all units (regardless of Treatment/Exposure status) using Mahalonobis distances computed from baseline X-covariate characteristics.
LCcluster(dframe, xvars, method="ward.D")Name of data.frame containing baseline X covariates.
List of names of X variable(s).
Hierarchical Clustering Method of "diana", "ward.D", "ward.D2", "complete", "average", "mcquitty", "median" or "centroid".
An output list object of class LCcluster, derived from cluster::diana or stats::hclust.
Name of data.frame containing all baseline X-covariates.
List of 1 or more X-variable names.
Hierarchical Clustering Method: "diana", "ward.D", "ward.D2", "complete", "average", "mcquitty", "median" or "centroid".
Hierarchical clustering object created by the designated method.
The first step in applying Local Control Strategy to data is to hierarchically cluster experimental units in baseline X-covariate space ...thereby creating "Blocks" of relatively well-matched units. LCcluster first calls stats::prcomp() to calculate Mahalanobis distances using standardized and orthogonal Principal Coordinates. LCcluster 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.
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.
# NOT RUN {
data(radon)
xvars <- c("obesity", "over65", "cursmoke")
hclobj <- LCcluster(radon, xvars) # ...using default method = "ward.D"
plot(hclobj)
# }
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