PELVIS (version 1.2.1)

indivSexing: Internal function for sexing one single human os coxae using revised Bruzek's method (2019)

Description

Produces a statistical sex estimate from eleven characters observed on the human os coxae, following Bruzek (2019), and using logistic regression models.

Usage

indivSexing(ref, newInd)

Arguments

ref

A learning dataset for logistic regression models, basically the dataset ‘refData’ included in PELVIS (or any other dataset with the same variables).

newInd

A new os coxae to be determined, with eleven observed traits (possibly with missing values).

Value

A list with the following components:

PredictedSex

One unique character value, ‘F’, ‘I’ or ‘M’: final sex estimate for the studied os coxae.

PostProb

Posterior probability for the individual to be a male.

BestModel

Best logistic regression model for the studied os coxae according to the BIC criterion.

VariablesUsed

Names of the variables (including part or all of the nonmissing traits for the studied os coaxe) used in this best model.

cvRate

Success rate in cross-validation. Cf. Santos et al. (2019) for more details about cross-validation here.

cvIndet

Rate of individuals remaining indeterminate using the best logistic regression model.

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. doi: 10.1002/ajpa.23855

Examples

# NOT RUN {
data(refDataBruzek02)
# Pick the first individual of the reference dataset with its 11 traits, as an example:
individual <- refDataBruzek02[1, -c(1:6)]
individual
# Produce a sex estimate for this individual:
indivSexing(ref=refDataBruzek02, newInd=individual)
# }