AIC.serp

Returns the akaike information criterion of a fitted object of class
<code>serp</code>. For the penalized slope, the effective degrees of freedom (edf)
is obtained from the trace of the generalized hat matrix which depends on
the tuning parameter.

Implements a regularization method for cumulative link models using the
Smooth-Effect-on-Response Penalty (SERP). This method allows flexible
modeling of ordinal data by enabling a smooth transition from a general
cumulative link model to a simplified version of the same model. As the
tuning parameter increases from zero to infinity, the subject-specific
effects for each variable converge to a single global effect.
The approach addresses common issues in cumulative link models, such as
parameter unidentifiability and numerical instability, by maximizing a
penalized log-likelihood instead of the standard non-penalized version.
Fitting is performed using a modified Newton's method. Additionally, the
package includes various model performance metrics and descriptive tools.
For details on the implemented penalty method, see
Ugba (2021) <doi:10.21105/joss.03705> and
Ugba et al. (2021) <doi:10.3390/stats4030037>.

Ejike Ugba

serp

Smooth Effects on Response Penalty for CLM

Ejike R. Ugba

AIC.serp function

<dl><dt>object</dt>
<dd>An object of class <code>serp</code>.</dd>
<dt>...</dt>
<dd>additional arguments.</dd>
<dt>k</dt>
<dd>fixed value equal to 2.</dd></dl>

Arguments

AIC for a fitted serp object — AIC.serp

<dl>

<dt>object</dt>
<dd>An object of class <code>serp</code>.</dd>


<dt>...</dt>
<dd>additional arguments.</dd>


<dt>k</dt>
<dd>fixed value equal to 2.</dd>

</dl>

AIC.serp: AIC for a fitted serp object

Description

Usage

Value

Arguments

See Also

Examples