Anthropometry (version 1.2)

trimmOutl: Helper function for obtaining the trimmed and outlier observations

Description

The methodologies included in this package that are developed to the clothing design problem take into account that a clothing sizing system is intended to cover only what we could call standard population, leaving out those individuals who are extreme respect to a set of measurements. For trimowa, TDDclust and LloydShapes, HartiganShapes and trimmedLloydShapes these individuals are called trimmed individuals and for hipamAnthropom these individuals are called outlier individuals.

Usage

trimmOutl(method, resMethod, oneSize, nsizes)

Arguments

method

It is a character vector with four possible values: "trimowa" for trimowa, "TDDclust" for TDDclust, "HipamAnthropom" for

resMethod

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

oneSize

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

nsizes

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

Value

• Vector with the trimmed or outlier observations.

References

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., Leon, T., Alemany, S., and Ayala, G., (2013). Looking for representative fit models for apparel sizing, Decision Support Systems 57, 22--33.

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., 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

Examples

#TRIMOWA ALGORITHM:
dataTrimowa <- sampleSpanishSurvey
numVar <- dim(dataTrimowa)[2]
bust <- dataTrimowa$bust
bustSizes <- bustSizesStandard(seq(74, 102, 4), seq(107, 131, 6))

orness <- 0.7
weightsTrimowa <- weightsMixtureUB(orness,numVar)

numClust <- 3 ; alpha <- 0.01 ; niter <- 10 ; algSteps <- 7
ah <- c(23, 28, 20, 25, 25)

set.seed(2014)
res_trimowa <- list()
for (i in 1 : (bustSizes$nsizes - 1)){ 
 data = dataTrimowa[(bust >= bustSizes$bustCirc[i]) & (bust < bustSizes$bustCirc[i + 1]), ]   
 res_trimowa[[i]] <- trimowa(data, weightsTrimowa, numClust, alpha, niter, 
                             algSteps,ah,verbose=FALSE)
}

trimmed <- trimmOutl("trimowa", res_trimowa, oneSize = FALSE, bustSizes$nsizes)