Anthropometry (version 1.2)

plotTrimmOutl: Trimmed or outlier observations 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 trimmed individuals discarded in each bust class provided by trimowa or with the outlier individuals provided by hipamAnthropom.

Usage

plotTrimmOutl(data,trimmOutl,nsizes,bustVariable,variable,col,xlim,ylim,main)

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' respect

trimmOutl

Trimmed women (if trimowa) or outlier women (if 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: Measur

bustVariable

Bust variable.

variable

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

col

A specification for the trimmed or outlier women 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

Title of the plot.

Value

• A device with the desired plot.

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.

See Also

sampleSpanishSurvey, hipamAnthropom, trimowa

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)

bustVariable <- "bust"
xlim <- c(70,150)
col <- 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)
main <- "Trimmed women \n bust vs chest"

plotTrimmOutl(dataTrimowa,trimmed,bustSizes$nsizes,bustVariable,variable,col,xlim,ylim,main)

variable <- "hip"
range(dataTrimowa[,variable])
#[1] 83.6 152.1
ylim <- c(80,160)
main <- "Trimmed women \n bust vs hip"

plotTrimmOutl(dataTrimowa,trimmed,bustSizes$nsizes,bustVariable,variable,col,xlim,ylim,main)

variable <- "necktoground"
range(dataTrimowa[,variable])
#[1] 117.6 154.9
ylim = c(110,160)
main <- "Trimmed women \n bust vs neck to ground"

plotTrimmOutl(dataTrimowa,trimmed,bustSizes$nsizes,bustVariable,variable,col,xlim,ylim,main)

variable <- "waist"
range(dataTrimowa[,variable])
#[1]  58.6 133.0
ylim <- c(50,140)
main <- "Trimmed women \n bust vs waist"

plotTrimmOutl(dataTrimowa,trimmed,bustSizes$nsizes,bustVariable,variable,col,xlim,ylim,main)


#AN EXAMPLE FOR HIPAM ALGORITHM:
dataHipam <- sampleSpanishSurvey
bust <- dataHipam$bust
bustSizes <- bustSizesStandard(seq(74, 102, 4), seq(107, 131, 6))

type <- "IMO"
maxsplit <- 5 ; orness <- 0.7
ah <- c(23, 28, 20, 25, 25)

set.seed(2013)
res_hipam <- list()
for(i in 1 : (bustSizes$nsizes - 1)){
 data = dataHipam[(bust >= bustSizes$bustCirc[i]) & (bust < bustSizes$bustCirc[i + 1]), ]
 dataMat <- as.matrix(data)
 res_hipam[[i]] <- hipamAnthropom(dataMat, maxsplit = maxsplit, orness = orness, type = type,
                                  ah = ah, verbose = FALSE)
}
outliers <- trimmOutl("HipamAnthropom", res_hipam, oneSize = FALSE, bustSizes$nsizes)

bustVariable <- "bust"
xlim <- c(70, 150)
color <- c("black", "red", "green", "blue", "cyan", "brown", "gray", "deeppink3", "orange", 
            "springgreen4", "khaki3", "steelblue1")
variable <- "hip"
ylim <- c(80, 160)
title_outl <- "Outlier women HIPAM_IMO \n bust vs hip"

plotTrimmOutl(dataHipam, outliers, bustSizes$nsizes, bustVariable, variable, color, 
              xlim, ylim, title_outl)