{smcl}
{* 6december2012}{...}
{hline}
help for {hi:hcavar}{right:Jean-Benoit Hardouin}
{hline}
{title:Hierarchical Clusters Analysis of variables}
{p 8 14 2}{cmd:hcavar} {it:varlist}
[{cmd:,} {cmdab:prox}{it:(keyword)} {cmdab:mat:rix}{it:(matrix)} {cmdab:meth:od}{it:(keyword)}
{cmdab:part:ition}{it:(numlist)} {cmdab:meas:ures} {cmdab:det:ect} {cmdab:nodendro:gram}]
{title:Description}
{p 4 8 2}
{cmd:hcavar} is the new name of the old {cmd:hcaccprox} module.
{p 4 8 2}
{cmd: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
(See {help measure option} for more details). 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).
{title:Options}
{p 4 8 2}{cmd:prox} defines the proximity measures to use : {it:jaccard}
(alias {it:a}), {it: russel}, {it:dice}, {it:matching} (alias {it:ad}), {it:pearson}
(alias {it:corr}), conditional covariance ({it:ccov}), conditional correlation
({it:ccor}), or Mantel Haenszel ({it:mh}). By default, this option is put to
{it:pearson}. {it:pearson} is the only one option available with ordinal or numerous
variables.
{p 4 8 2}{cmd:matrix} allows using a matrix as distance matrix.
{p 4 8 2}{cmd:method} defines the method to aggregate two clusters. See {help cluster}
for more details about these methods. The complete name of the method
must be indicated (with or without "linkage"), none abbreviation is allowed.
{it:waveragelinkage} is used by default.
{p 4 8 2}{cmd:partition} lists the partitions of variables to detail by
the program.
{p 4 8 2}{cmd:measures} displays the used proximity measures matrix between
the variables.
{p 4 8 2}{cmd:detect} computes, for binary variables, the DETECT, Iss and R indexes
for the partitions indicated in the {cmd:partitions} option.
{p 4 8 2}{cmdnodendrogram} enables the displaying of th dendrogram.
{title:Examples}
{p 4 8 2}{cmd:. hcavar var1-var10} /*displays only the dendrogram*/
{p 4 8 2}{cmd:. hcavar var*, partition(1/6) measures method(single)} /*Single linkage, details of 6 partitions*/
{p 4 8 2}{cmd:. hcavar itemA1-itemA7 itemB1-itemB7, prox(ccor) method(single) detect part(1/4)} /*details of 4 partitions, conditional correlations*/
{title:Outputs}
{p 4 8 2}{cmd:. r(nbvar)} contains the number of variables
{p 4 8 2}{cmd:. r(measures)} is the distances measures matrix between the variables
{p 4 8 2}{cmd:. r(clusters)} is a matrix obtained with the {cmd:partition} option
containing the composition of the partitions defined with this option.
{p 4 8 2}{cmd:. r(indexes)} is obtained with the {cmd:detect} option.
This matrix contain the DETECT, Iss and R indexes associated to each partition
defined with the {cmd:partition} option.
{title:Reference}
{p 4 8 2}{cmd:Roussos L. A, Stout W. F. and Marden J. I.}, {it:Using new proximity measures with hierarchical cluster analysis to detect multidimensionality}. Journal of Educational Measurement, {cmd:35}(1), pp 1-30, 1998.
{p 4 8 2}{cmd:Zhang J. and Stout W. F.}, {it:The theorical DETECT index of dimensionality and its application to approximate simple structure}. Psychometrika, {cmd:64}(2), pp 213-249, 1999.
{title:Also see}
{p 4 13 2} help for {help cluster}, help for {help detect} (if installed)
{title:Author}
{p 4 8 2}Jean-Benoit Hardouin, PhD, assistant professor{p_end}
{p 4 8 2}EA 4275 SPHERE "Team of Biostatistics, Clinical Research and Subjective Measures in Health Sciences"{p_end}
{p 4 8 2}University of Nantes - Faculty of Pharmaceutical Sciences{p_end}
{p 4 8 2}1, rue Gaston Veil - BP 53508{p_end}
{p 4 8 2}44035 Nantes Cedex 1 - FRANCE{p_end}
{p 4 8 2}Email:
{browse "mailto:jean-benoit.hardouin@univ-nantes.fr":jean-benoit.hardouin@univ-nantes.fr}{p_end}
{p 4 8 2}Website {browse "http://www.anaqol.org":AnaQol}