Principal Component Analysis — pc_and

This function performs principal component analysis. In the first, missing data is imputed to the median. Subsequent to the derivation of the PC, the median imputed PC data is used to identify the number of informative or "significant" PC by (1) an acceleration analysis, and (2) a parrallel analysis. Finally the number of sample outliers are determined at 3, 4, and 5 standard deviations from the mean on the top PCs as determined by the acceleration factor analysis.

Usage

pc_and_outliers(
  metaboprep,
  source_layer = "input",
  sample_ids = NULL,
  feature_ids = NULL
)

Arguments

metaboprep: an object of class Metaboprep
source_layer: character, type/source of data to use
sample_ids: character, vector of sample ids to include, default NULL includes all
feature_ids: character, vector of feature ids to include, default NULL includes all

Value

a data.frame