In bryanhanson/LearnPCA: Functions, Data Sets and Vignettes to Aid in Learning Principal Components Analysis (PCA)

# R options & configuration:
set.seed(123)
suppressPackageStartupMessages(library("knitr"))
suppressPackageStartupMessages(library("LearnPCA"))
suppressPackageStartupMessages(library("xtable"))

# Stuff specifically for knitr:
opts_chunk$set(eval = TRUE, echo = TRUE, results = "show")

res <- knitr::knit_child("top_matter.md", quiet = TRUE)
cat(res, sep = '\n')

This vignette collects bits of information and answers to questions we have received from our readers, as well as some tips and warnings that may be useful.

Warning When Analyzing Time Series and Spatial Data

Shinn reports that PCA on data which may be shifted in space or time can give "phantom oscillations" that don't exist in the data. His context is neuroscience but one should be careful with this type of data.

Working with Nominal or Ordinal Response Data

A reader asked if one should center ordinal survey responses, for instance responses on a 1-5 scale. We didn't know the answer, but after some discussion and research we found there is an R package Gifi designed for just this case. There's also a tutorial here. Both of these resources use concepts published in a book by Gifi (1990).

res <- knitr::knit_child("refer_to_works_consulted.md", quiet = TRUE)
cat(res, sep = '\n')

bryanhanson/LearnPCA documentation built on May 17, 2024, 12:33 p.m.