logLik.scam

AIC.scam

fitted model objects of class <code>scam</code> as produced by <code>scam()</code>.

object

Function to extract the log-likelihood for a fitted <code>scam</code>
model (fitted by penalized likelihood maximization). 
Used by <code>AIC</code>.
The function is a clone of <code>logLik.gam</code> of the <code>mgcv</code> package.
The documentation below is similar to that of object <code><a rd-options="" href="/link/logLik.gam?package=scam&version=1.2-5" data-mini-rdoc="scam::logLik.gam">logLik.gam</a></code>.

models

regression

smooth

Routines for generalized additive modelling under shape
constraints on the component functions of the linear predictor
(Pya and Wood, 2015) <doi:10.1007/s11222-013-9448-7>.
Models can contain multiple shape constrained (univariate
and/or bivariate) and unconstrained terms. The routines of gam()
in package 'mgcv' are used for setting up the model matrix,
printing and plotting the results. Penalized likelihood
maximization based on Newton-Raphson method is used to fit a
model with multiple smoothing parameter selection by GCV or
UBRE/AIC.

Natalya Pya

scam

Shape Constrained Additive Models

logLik.scam function

Function to extract the log-likelihood for a fitted <code>scam</code>
model (fitted by penalized likelihood maximization). 
Used by <code>AIC</code>.
The function is a clone of <code>logLik.gam</code> of the <code>mgcv</code> package.
The documentation below is similar to that of object <code><a rd-options='' href='logLik.gam'>logLik.gam</a></code>.

logLik.scam: Log likelihood for a fitted SCAM, for AIC

Description

Usage

Arguments

Value

Details

References

See Also