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Anthropometry (version 1.18)

Statistical Methods for Anthropometric Data

Description

Statistical methodologies especially developed to analyze anthropometric data. These methods are aimed at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the statistical concept of data depth, statistical shape analysis and archetypal analysis. Please see Vinue (2017) .

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Version

Install

install.packages('Anthropometry')

Monthly Downloads

567

Version

1.18

License

GPL (>= 2)

Homepage

https://www.R-project.org

Maintainer

Guillermo Vinue

Last Published

January 13th, 2023

Functions in Anthropometry (1.18)

Trimmed clustering based on L1 data depth

Anthropometry-package

Statistical Methods for Anthropometric Data

Anthropometry-internalTDDclust

Several internal functions to clustering based on the L1 data depth

CCbiclustAnthropo

Cheng and Church biclustering algorithm applied to anthropometric data

Anthropometry-internalPlotTree

Several internal functions used to build the HIPAM plot tree

USAF 1967 survey

Lloyd k-means for 3D shapes

Hartigan-Wong k-means for 3D shapes

Anthropometry-internalArchetypoids

Several internal functions to compute and represent archetypes and archetypoids

Anthropometry-internalHipamAnthropom

Several internal functions used by both $HIPAM_MO$ and $HIPAM_IMO$ algorithms

computSizesTrimowa

Computation of the trimowa elements for a given number of sizes defined by the EN

Helper generic function for obtaining the anthropometric cases

checkBranchLocalIMO

Evaluation of the candidate clustering partition in $HIPAM_IMO$

computSizesHipamAnthropom

Computation of the hipamAnthropom elements for a given number of sizes defined by the EN

cdfDissWomenPrototypes

CDF for the dissimilarities between women and computed medoids and standard prototypes

checkBranchLocalMO

Evaluation of the candidate clustering partition in $HIPAM_MO$

Helper function for the 3D landmarks

archetypesBoundary

Archetypal analysis in multivariate accommodation problem

bustSizesStandard

Helper function for defining the bust sizes

Finding archetypoids

landmarksSampleSpaSurv

Landmarks of the sampled women of the Spanish Survey

getBestPamsamIMO

Generation of the candidate clustering partition in $HIPAM_IMO$

Cube of 8 landmarks

Cube of 34 landmarks

Figures of 8 landmarks with labelled landmarks

descrDissTrunks

Description of the dissimilarities between women's trunks

Dissimilarity matrix between individuals and prototypes

HIPAM algorithm for anthropometric data

getBestPamsamMO

Generation of the candidate clustering partition in $HIPAM_MO$

nearestToArchetypes

Nearest individuals to archetypes

Auxiliary optra subroutine of the Hartigan-Wong k-means for 3D shapes

parallelep34landm

Parallelepiped of 34 landmarks

Trimmed or outlier observations representation

Prototypes representation

Data preprocessing before computing archetypal observations

plotTreeHipamAnthropom

HIPAM dendogram

parallelep8landm

Parallelepiped of 8 landmarks

overlapBiclustersByRows

Overlapped biclusters by rows

Helper function for plotting the shapes

percentilsArchetypoid

Helper function for computing percentiles of a certain archetypoid

skeletonsArchetypal

Skeleton plot of archetypal individuals

stepArchetypoids

Run the archetypoid algorithm several times

screeArchetypal

Screeplot of archetypal individuals

Auxiliary qtran subroutine of the Hartigan-Wong k-means for 3D shapes

Helper generic function for obtaining the trimmed and outlier observations

stepArchetypesRawData

Archetype algorithm to raw data

xyplotPCArchetypes

PC scores for archetypes

Trimmed k-medoids algorithm

sampleSpanishSurvey

Sample database of the Spanish anthropometric survey

trimmedLloydShapes

Trimmed Lloyd k-means for 3D shapes

Trimmed PAM with OWA operators

weightsMixtureUB

Calculation of the weights for the OWA operators