plsr - Partial Least Squares Made Easy"

In plsr: Pleasure - Partial Least Squares Analysis with Permutation Testing

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plsr (read: "pleasure") is a package that provides simple to use Partial Least Squares Analysis.

It features:

• P-value calculation through permutation testing for latent variables
• Backward and forward prediction
• Various visualization functions for estimated latent variables

Quick Start

The central function of the package is pls(). Given two matrices X and Y with arbitrary number of variables in columns and equal number of observations in rows this function will compute the latent variables that will maximize the covariance between X and Y. For example, a plsr object can be calculated like so:

library(plsr)
plsr_obj=pls(X=rating_data,Y=tracking_data,n_perm=1000,n_boot=10)

Inspecting Results

Resulting latent variables and their significances can be investigated using the generic functions summary() and plot():

summary(plsr_obj)

plot(plsr_obj)

plsr:::plot_perm_results(plsr_obj)

plsr documentation built on May 1, 2019, 11:28 p.m.