gam.fit5.post.proc

object

penalty object, output of <code>Sl.setup</code>.

matrix mapping the working smoothing parameters.

lsp0

INTERNAL function for post-processing the output of <code>gam.fit5</code>.

Generalized additive (mixed) models, some of their extensions and
other generalized ridge regression with multiple smoothing
parameter estimation by (Restricted) Marginal Likelihood,
Generalized Cross Validation and similar, or using iterated
nested Laplace approximation for fully Bayesian inference. See
Wood (2017) <doi:10.1201/9781315370279> for an overview.
Includes a gam() function, a wide variety of smoothers, 'JAGS'
support and distributions beyond the exponential family.

Simon Wood

mgcv

Mixed GAM Computation Vehicle with Automatic Smoothness
Estimation

gam.fit5.post.proc function

<dl><dt>object</dt>
<dd>output of <code>gam.fit5</code>.</dd>
<dt>Sl</dt>
<dd>penalty object, output of <code>Sl.setup</code>.</dd>
<dt>L</dt>
<dd>matrix mapping the working smoothing parameters.</dd>
<dt>lsp0</dt>
<dd>log smoothing parameters.</dd>
<dt>S</dt>
<dd>penalty matrix.</dd>
<dt>off</dt>
<dd>vector of offsets.</dd></dl>

Arguments

Simon N. Wood &lt;simon.wood@r-project.org&gt;.

Author

Post-processing output of gam.fit5 — gam.fit5.post.proc

<dl>

<dt>object</dt>
<dd>output of <code>gam.fit5</code>.</dd>


<dt>Sl</dt>
<dd>penalty object, output of <code>Sl.setup</code>.</dd>


<dt>L</dt>
<dd>matrix mapping the working smoothing parameters.</dd>


<dt>lsp0</dt>
<dd>log smoothing parameters.</dd>


<dt>S</dt>
<dd>penalty matrix.</dd>


<dt>off</dt>
<dd>vector of offsets.</dd>

</dl>

gam.fit5.post.proc: Post-processing output of gam.fit5

Description

Usage

Value

Arguments

Author