dss_plot_pca: A wrapper for plotting a principal component analysis with...

In frederic-santos/rdss: An R-Shiny Application for a Semi-Automatized Approach of Sex Estimation in Biological Anthropology

A wrapper for plotting a principal component analysis with confidence ellipses

Description

Two PCA plots (variables and individuals) are displayed for the reference dataset. On the individuals plot, confidence ellipses for both sexes can be added. The target individual is particularly highlighted.

Usage

dss_plot_pca(ref, imputed_ref, target,
             ellipses = c("none", "classical", "robust"),
             labels = FALSE)

Arguments

ref	dataframe of reference individuals, typically returned by dss_check_data.
imputed_ref	imputed dataframe of reference individuals, typically returned by dss_impute_missing.
target	1-row dataframe, target individual.
ellipses	character string; type of confidence ellipses to be displayed.
labels	boolean: should all individuals IDs be displayed on the plot?

Details

The PCA is computed after standardization of variables (all variables are scaled to unit variance upstream).

Value

No value is returned.

Note

This function is only a wrapper with quite limited facilities for customizing the plot. It is still possible to compute and plot the PCA directly with PCA for more options.

Author(s)

Frédéric Santos.

See Also

PCA

frederic-santos/rdss documentation built on March 25, 2023, 5:25 p.m.