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Anthropometry (version 1.2)

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.

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Version

Install

install.packages('Anthropometry')

Monthly Downloads

567

Version

1.2

License

GPL (>= 2)

Homepage

http://www.r-project.org

Maintainer

Guillermo Vinue

Last Published

May 14th, 2015

Functions in Anthropometry (1.2)

archetypesBoundary

Archetypal analysis in multivariate accommodation problem

nearestToArchetypes

Nearest individuals to archetypes

stepArchetypesMod

Archetype algorithm to raw data

plotTreeHipamAnthropom

HIPAM dendogram

Anthropometry-package

Statistical Methods for Anthropometric Data

Anthropometry-internalPlotTree

Several internal functions used to build the HIPAM plot tree

Trimmed clustering based on L1 data depth

descrDissTrunks

Description of the dissimilarities between women's trunks

landmarksSampleSpaSurv

Landmarks of the sampled women of the Spanish Survey

HIPAM algorithm for anthropometric data

checkBranchLocalMO

Evaluation of the candidate clustering partition in $HIPAM_{MO}$

Cube of 34 landmarks

CCbiclustAnthropo

Cheng and Church biclustering algorithm applied to anthropometric data

skeletonsArchetypal

Skeleton plot of archetypal individuals

cdfDissWomenPrototypes

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

Hartigan-Wong k-means for 3D shapes

overlapBiclustersByRows

Overlapped biclusters by rows

Lloyd k-means for 3D shapes

Anthropometry-internalArchetypoids

Several internal functions to compute and represent archetypes and archetypoids

trimmedLloydShapes

Trimmed Lloyd k-means for 3D shapes

Trimmed k-medoids algorithm

parallelep34landm

Parallelepiped of 34 landmarks

Trimmed PAM with OWA operators

stepArchetypoids

Run the archetypoid algorithm several times

Anthropometry-internalTDDclust

Several internal functions to clustering based on the L1 data depth

Trimmed or outlier observations representation

getBestPamsamIMO

Generation of the candidate clustering partition in $HIPAM_{IMO}$

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

parallelep8landm

Parallelepiped of 8 landmarks

Data preprocessing before computing archetypal observations

Helper function for obtaining the anthropometric cases

getBestPamsamMO

Generation of the candidate clustering partition in $HIPAM_{MO}$

Prototypes representation

Figures of 8 landmarks with labelled landmarks

bustSizesStandard

Helper function for defining the bust sizes

USAF 1967 survey

xyplotPCArchetypes

PC scores for archetypes

checkBranchLocalIMO

Evaluation of the candidate clustering partition in $HIPAM_{IMO}$

sampleSpanishSurvey

Sample database of the Spanish anthropometric survey

Dissimilarity matrix between individuals and prototypes

screeArchetypal

Screeplot of archetypal individuals

Cube of 8 landmarks

percentilsArchetypoid

Computing percentiles of a certain archetypoid

Helper function for the 3D landmarks

Helper function for obtaining the trimmed and outlier observations

weightsMixtureUB

Calculation of the weights for the OWA operators

Finding archetypoids

Helper function for the percentils of the archetypoids

Anthropometry-internalHipamAnthropom

Several internal functions used by both $HIPAM_{MO}$ and $HIPAM_{IMO}$ algorithms

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

Helper function for plotting the shapes