totalPenaltySpace

INTERNAL function to obtain (orthogonal) basis for the null space and
 range space of the penalty, and obtain actual null space dimension
 components are roughly rescaled to avoid any dominating.

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

totalPenaltySpace function

<dl><dt>S</dt>
<dd>a list of penalty matrices, in packed form.</dd>
<dt>H</dt>
<dd>the coefficient matrix of an user supplied fixed quadratic 
penalty on the parameters of the GAM.</dd>
<dt>off</dt>
<dd>a vector where the i-th element is the offset for the i-th matrix.</dd>
<dt>p</dt>
<dd>total number of parameters.</dd></dl>

Arguments

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

Author

Obtaining (orthogonal) basis for null space and range of the penalty matrix — totalPenaltySpace

<dl>

<dt>S</dt>
<dd>a list of penalty matrices, in packed form.</dd>


<dt>H</dt>
<dd>the coefficient matrix of an user supplied fixed quadratic 
penalty on the parameters of the GAM.</dd>


<dt>off</dt>
<dd>a vector where the i-th element is the offset for the i-th matrix.</dd>


<dt>p</dt>
<dd>total number of parameters.</dd>

</dl>

totalPenaltySpace: Obtaining (orthogonal) basis for null space and range of the penalty matrix

Description

Usage

Value

Arguments

Author