Diagnostics plots for sPLS-DA-based feature preselection — plot_feature_preselection

Displays the PLS-DA classification performance across different number of latent components for each prefiltered dataset. The classification error rates are computed with different measures (column facets) and different distance metrics (colours). A vertical grey bar represents for each dataset the number of latent components selected for the feature preselection step. In addition, a circle highlights the measure and distance metric used to select the number of latent component.

Usage

plot_feature_preselection_splsda(
  perf_splsda_res,
  measure = NULL,
  distance = NULL
)

Arguments

perf_splsda_res: A list with the result from the perf_splsda for each dataset to be filtered.
measure: Which measure(s) should be displayed? Can be one of "BER" or "overall". If NULL, all measures will be displayed. Default value is NULL.
distance: Which measure(s) should be displayed? Can be one of "max.dist", "centroids.dist" or "mahalanobis.dist". If NULL, all measures will be displayed. Default value is NULL.

Value

A ggplot.