plot.predict.lm.rrpp

plot object (from <code><a rd-options="" href="/link/predict.lm.rrpp?package=RRPP&version=1.1.2" data-mini-rdoc="RRPP::predict.lm.rrpp">predict.lm.rrpp</a></code>)

A logical argument for whether the data space should be 
rotated to its 
principal components

A logical argument to change error bars to ellipses 
in multivariate plots. 
It has no function for univariate plots or is abscissa is not NULL.

ellipse

An optional vector (numeric of factor) equal in length 
to predictions to use for 
plotting as the abscissa (x-axis), in which case predictions are the 
ordinate (y-axis). This might be 
helpful if predictions are made for a continuous independent variable. 
The abscissa would be the
same variable used to make predictions (and can be the data.frame used 
for 
newdata in <code><a rd-options="" href="/link/predict.lm.rrpp?package=RRPP&version=1.1.2" data-mini-rdoc="RRPP::predict.lm.rrpp">predict.lm.rrpp</a></code>).

abscissa

A logical argument for whether points should be labeled 
(in multivariate plots).

label

other arguments passed to plot, arrows, points, or text (helpful 
to employ
different colors or symbols for different groups). See
<code><a rd-options="" href="/link/plot.default?package=RRPP&version=1.1.2" data-mini-rdoc="RRPP::plot.default">plot.default</a></code>, <code><a rd-options="" href="/link/arrows?package=RRPP&version=1.1.2" data-mini-rdoc="RRPP::arrows">arrows</a></code>, <code><a rd-options="" href="/link/points?package=RRPP&version=1.1.2" data-mini-rdoc="RRPP::points">points</a></code>,
<code><a rd-options="" href="/link/par?package=RRPP&version=1.1.2" data-mini-rdoc="RRPP::par">par</a></code>, and <code><a rd-options="" href="/link/text?package=RRPP&version=1.1.2" data-mini-rdoc="RRPP::text">text</a></code>

utilities

visualization

Linear model calculations are made for many random versions of data.
Using residual randomization in a permutation procedure, sums of squares are
calculated over many permutations to generate empirical probability distributions
for evaluating model effects. This packaged is described by
Collyer & Adams (2018) <doi:10.1111/2041-210X.13029>. Additionally, coefficients, statistics, fitted values, and residuals generated over many
permutations can be used for various procedures including pairwise tests, prediction, classification, and
model comparison. This package should provide most tools one could need for the analysis of
high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well.

Michael Collyer

RRPP

Linear Model Evaluation with Randomized Residuals in a
Permutation Procedure

Dean Adams

plot.predict.lm.rrpp function

plot object (from <code><a rd-options='' href='predict.lm.rrpp'>predict.lm.rrpp</a></code>)

An optional vector (numeric of factor) equal in length 
to predictions to use for 
plotting as the abscissa (x-axis), in which case predictions are the 
ordinate (y-axis). This might be 
helpful if predictions are made for a continuous independent variable. 
The abscissa would be the
same variable used to make predictions (and can be the data.frame used 
for 
newdata in <code><a rd-options='' href='predict.lm.rrpp'>predict.lm.rrpp</a></code>).

other arguments passed to plot, arrows, points, or text (helpful 
to employ
different colors or symbols for different groups). See
<code><a rd-options='' href='plot.default'>plot.default</a></code>, <code><a rd-options='' href='arrows'>arrows</a></code>, <code><a rd-options='' href='points'>points</a></code>,
<code><a rd-options='' href='par'>par</a></code>, and <code><a rd-options='' href='text'>text</a></code>

plot.predict.lm.rrpp: Plot Function for RRPP

Description

Usage

Arguments

Examples