logLik.lm.rrpp

<code>logLik.lm.rrpp</code> returns the log-likelihood of
an <code>lm.rrpp</code> object. Ridge regularization will be performed for 
ill-conditioned or singular residual covariance matrices, but dimension
reduction could be augmented via projection, using the arguments, tol
and pc.no. See <code>ordinate</code> for details.

utilities

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).  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

logLik.lm.rrpp function

<dl><dt>object</dt>
<dd>Object from <code>lm.rrpp</code></dd>
<dt>tol</dt>
<dd>A value indicating the magnitude below which 
components should be omitted, following projection. See <code>ordinate</code> 
for details.</dd>
<dt>pc.no</dt>
<dd>Optionally, a number specifying the maximal number of 
principal components, passed onto <code>ordinate</code>, as argument, rank.</dd>
<dt>Z</dt>
<dd>A logical value for whether to calculate Z scores based on RRPP.</dd>
<dt>gls.null</dt>
<dd>A logical value for if a fit has a GLS estimation, should
the null model (intercept) also have a GLS estimation, for estimating Z. 
Should be FALSE if the log-likelihood is measured to compare different GLS
estimations for a covariance matrices</dd>
<dt>...</dt>
<dd>further arguments passed to or from other methods</dd></dl>

Arguments

Author

Calculate the log-likelihood of a lm.rrpp fit — logLik.lm.rrpp

<dl>

<dt>object</dt>
<dd>Object from <code>lm.rrpp</code></dd>


<dt>tol</dt>
<dd>A value indicating the magnitude below which 
components should be omitted, following projection. See <code>ordinate</code> 
for details.</dd>


<dt>pc.no</dt>
<dd>Optionally, a number specifying the maximal number of 
principal components, passed onto <code>ordinate</code>, as argument, rank.</dd>


<dt>Z</dt>
<dd>A logical value for whether to calculate Z scores based on RRPP.</dd>


<dt>gls.null</dt>
<dd>A logical value for if a fit has a GLS estimation, should
the null model (intercept) also have a GLS estimation, for estimating Z. 
Should be FALSE if the log-likelihood is measured to compare different GLS
estimations for a covariance matrices</dd>


<dt>...</dt>
<dd>further arguments passed to or from other methods</dd>

</dl>

logLik.lm.rrpp: Calculate the log-likelihood of a lm.rrpp fit

Description

Usage

Arguments

Author