plotPrototypes {Anthropometry}

Prototypes representation

Description

This function represents the scatter plots of bust circumference against other selected variable (chest, hip, neck to ground or waist) jointly with the prototypes obtained for each bust class provided by either trimowa or hipamAnthropom. In addition, the prototypes defined by the European standard on sizing systems. Size designation of clothes. Part 3: Measurements and intervals can be also displayed.

Usage

plotPrototypes(data,prototypes,nsizes,bustVariable,variable,col,xlim,ylim,
               main,EN)

Arguments

data

Data frame. It should contain the chest, neck to ground, waist, hip and bust measurements of the individuals. In order to be able to represent them, the name of the columns of the database must be 'chest', 'necktoground', 'waist', 'hip' and 'bust' respectively, see sampleSpanishSurvey. Each row corresponds to an observation, and each column corresponds to a variable. All variables are numeric.

prototypes

Prototypes (medoids) i.e., typical persons within the sample, obtained with trimowa or hipamAnthropom.

nsizes

Number of subsets (classes), into the database is segmented. In our approach, the whole anthropometric Spanish survey is segmented into twelve bust segments, according to the European standard on sizing systems. Size designation of clothes. Part 3: Measurements and intervals.

bustVariable

Bust variable.

variable

Anthropometric variable to be plotted. It can be 'chest', 'necktoground', 'waist' and 'hip'.

col

A specification for the medoids color in each bust class.

xlim

Axis lenght of the x axis according to the range of the bust variable.

ylim

Axis lenght of the y axis according to the range of the selected variable among chest, hip, neck to ground and waist.

main

Main title of the plot.

EN

A logical value. If TRUE, the prototypes defined by the European standard for each variable are represented. See section Details for more details.

Details

Ir order to check the goodness of trimowa, the sizes defined by the prototypes can be compared with those defined by the European standard to sizing system. This standard establishes 12 sizes according to the combinations of the bust, waist and hip measurements and does not fix neither chest nor height standard measurements. We can approximate the chest measurements through a linear regression analysis, taking the bust measurements detailed in the standard as independent variable. Besides, we take as neck to ground measurements for the standard sizing system, the values 132, 136 and 140 cm because those are the most repeated values and they are those which best cover our data set. See Ibanez et al. (2012) for a complete explanation.

Value

A device with the desired plot.

Note

As mentioned, this function is especially defined for the sizes established by the European standard on sizing systems. Part 3: Measurements and intervals. In order to use this function with other standard, this function must be adapted.

Author(s)

Guillermo Vinue

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., (2014). Looking for representative fit models for apparel sizing, Decision Support Systems 57, 22–33.

European Committee for Standardization. Size designation of clothes. Part 3: Measurements and intervals. (2005).

See Also

sampleSpanishSurvey, weightsMixtureUB, trimowa, getDistMatrix, trimmedoid, hipamAnthropom

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)

#For reproducing results, seed for randomness:
#suppressWarnings(RNGversion("3.5.0"))
#set.seed(2014)
numSizes <- 2
res_trimowa <- computSizesTrimowa(dataTrimowa, bust, bustSizes$bustCirc, numSizes,
                                  weightsTrimowa, numClust, alpha, niter, algSteps, 
                                  ah, FALSE)

prototypes <- anthrCases(res_trimowa, numSizes)

bustVariable <- "bust"
xlim <- c(72, 132)
color <- c("black", "red", "green", "blue", "cyan", "brown", "gray",
           "deeppink3", "orange", "springgreen4", "khaki3", "steelblue1")

variable <- "chest"
range(dataTrimowa[,variable])
#[1] 76.7755 135.8580
ylim <- c(70,140)
title <- "Prototypes \n bust vs chest"

plotPrototypes(dataTrimowa, prototypes, numSizes, bustVariable, 
               variable, color, xlim, ylim, title, FALSE)
plotPrototypes(dataTrimowa, prototypes, numSizes, bustVariable, 
               variable, color, xlim, ylim, title, TRUE)

#For other plots and an example for the hipam algorithm, 
#see www.uv.es/vivigui/softw/more_examples.R