anthrCases: Helper function for obtaining the anthropometric cases

Description

Because the goal of the methodologies included in this package is always to estimate a number of anthropometric cases given a data set (both central (prototypes) and boundaries (archetypoids)), this auxiliary function allows the user to identify the cases computed by each method in an easy way.

Usage

anthrCases(field, method, resMethod, oneSize, nsizes)

Arguments

field

Character vector with two possible values. If "anthropometry", the prototypes computed by trimowa, TDDclust, hi

method

This argument is needed if field="anthropometry". It is a character vector with four possible values: "trimowa" for trimowa, "TDDclust" for TDDclust

resMethod

This is the object which saves the results obtained by the aforementioned methodologies and which contains the anthropometric cases to return.

oneSize

A logical specifying whether to obtain prototypes for a single size. This argument is needed if method="trimowa" of method="HipamAnthropom" because these methodologies can compute the prototypes for a single bust size or for the

nsizes

Number of bust sizes. This argument is needed if method="trimowa" of method="HipamAnthropom" because these methodologies compute the prototypes for each bust size defined by the European Normative.

`Value`

Vector with the central or boundary anthropometric cases.

`References`

Vinue, G., Epifanio, I., and Alemany, S., (2015). Archetypoids: a new approach to define representative archetypal data, Computational Statistics and Data Analysis 87, 102--115.
Vinue, G., Simo, A., and Alemany, S., (2014). The k-means algorithm for 3D shapes with an application to apparel design, Advances in Data Analysis and Classification, 1--30.
Vinue, G., Leon, T., Alemany, S., and Ayala, G., (2013). Looking for representative fit models for apparel sizing, Decision Support Systems 57, 22--33.
Ibanez, M. V., Vinue, G., Alemany, S., Simo, A., Epifanio, I., Domingo, J., and Ayala, G., (2012). Apparel sizing using trimmed PAM and OWA operators, Expert Systems with Applications 39, 10512--10520.
Vinue, G., and Ibanez, M. V., (2014). Data depth and Biclustering applied to anthropometric data. Exploring their utility in apparel design. Technical report.

`See Also`

trimowa, TDDclust, hipamAnthropom, LloydShapes, HartiganShapes, trimmedLloydShapes, archetypoids, stepArchetypoids

`Examples`

Run this code#TRIMOWA ALGORITHM FOR ONE SIZE:
set.seed(1900)
rand <- sample(1:600,20)
dataComp <- sampleSpanishSurvey[rand, c(2, 3, 5)]

numVar <- dim(dataComp)[2] 
orness <- 0.7
weightsTrimowa <- weightsMixtureUB(orness, numVar)

numClust <- 3 ; alpha <- 0.01 ; niter <- 5 
ahVect <- c(28, 25, 25) ;  algSteps <- 7

set.seed(1900)
Trimowa <- trimowa(dataComp, weightsTrimowa, numClust, alpha, niter, 
                   algSteps, ahVect, verbose = FALSE)
prototypes_Trimowa <- anthrCases("anthropometry", "trimowa", 
                                 Trimowa, oneSize = TRUE)
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