CTscanDataBruzek: Dataset including 198 virtually reconstructed ossa coxae

In PELVIS: Probabilistic Sex Estimate using Logistic Regression, Based on VISual Traits of the Human Os Coxae

Dataset including 198 virtually reconstructed ossa coxae

Description

This dataset includes 198 ossa coxae segmented from CT-scans. The eleven trichotomic traits are given for each os coaxe (possibly with missing values for incomplete bones), along with the geographical origin and known sex of the individual. When possible, the age and stature of the individual are also given. This dataset is used as a training sample for the logistic regression models implemented in PELVIS.

Usage

data(CTscanDataBruzek)

Format

A data frame with 198 observations on the following 16 variables:

Id

a factor with 198 levels (unique ID of each os coxae)

Indiv

a factor with 99 levels (ID of each individual to whom the bone belongs)

Sex

a factor with levels F, M (known sex)

Age

a numeric vector (age of the associated individual in years)

Side

a factor with levels L, R (left or right side)

PrSu1

an ordered factor with levels f, i, m

PrSu2

an ordered factor with levels f, i, m

PrSu3

an ordered factor with levels f, i, m

GrSN1

an ordered factor with levels f, i, m

GrSN2

an ordered factor with levels f, i, m

GrSN3

an ordered factor with levels f, i, m

CArc

an ordered factor with levels F, 0, M

IsPu

an ordered factor with levels F, 0, M

InfP1

an ordered factor with levels f, i, m

InfP2

an ordered factor with levels f, i, m

InfP3

an ordered factor with levels f, i, m

References

Santos, F., Guyomarc'h, P., Rmoutilova, R. and Bruzek, J. (2019) A method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits. American Journal of Physical Anthropology 169(3), 435-447. doi: 10.1002/ajpa.23855

Bruzek, J., Rmoutilova, R., Guyomarc'h, P., & Santos, F. (2019) Supporting data for: A method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2589917

PELVIS documentation built on Aug. 8, 2023, 5:09 p.m.