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Updated16 mai 2018
The Stata module "Hcavar"
hcavar realizes a Hierarchical Clusters Analysis on variables. The variables can be numerous, ordinal or binary. The distances (dissimilarity measures for binary variables) between two variables are computed as the squared root of 2 times one minus the Pearson correlation. For binary variables, it is possible to use other similarity coefficients as Matching, Jaccard, Russel or Dice. The distance matrix is computed as the squared root of one minus the value of these coefficients. In the field of Item Response Theory, it is possible to define conditional measures to the score as defined by Roussos, Stout and Marden (1998): conditional correlations, conditional covariance, or Mantel-Haenszel measures of similarity. In the same field, it is possible to compute, for a set of obtained partition of the items, the DETECT, Iss and R indexes defined by Zhang and Stout (1999).
Type "findit hcavar" or "ssc install hcavar" directly from your Stata browser.
Syntax (version 3.2)
hcavar varlist [, prox(jaccard(alias a)/ matching(alias ad)/ pearson(alias corr)/ russel/ dice/ ccov/ ccor/ mh) method(single/ complete/ average(alias upgma)/ waverage(alias wpgma)/ median/ wards) partition(numlist) measures matrix(matrix) detect nodendrogram ]
This program requires an access to the following program(s):
The old name of hcavar is hcaccprox (versions 1 et 2).
- prox(jaccard(alias a)/ matching(alias ad)/ pearson(alias corr)/ russel/ dice/ ccov/ ccor/ mh): allows chosing the proximity measures (pearson by default)
- method(single/ complete/ average(alias upgma)/ waverage(alias wpgma)/ median/ wards): allows defining the aggregation method (waverage - by default). The keyword can be, or not, followed by "linkage"
- partition(numlist): allows giving details about the partitions defined in numlist
- measures: displays the proximity matrix between the items
- matrix(matrix): Use the matrix matrix as distance matrix between the variables
- detect: computes, for the partitions defined in the partition option, the indexes DETECT, Iss and R
- nodendrogram: enables the displaying of the dendrogram.
hcavar var*, partition(1/6) measures method(single)
hcavar itemA1-itemA7 itemB1-itemB7, prox(ccor) method(single) detect part(1/4)