residuals.scam

This function is a clone of the <code>mgcv</code> library code <code><a href="/link/residuals.gam?package=scam&version=1.2-17" data-mini-rdoc="scam::residuals.gam">residuals.gam</a></code>.
It returns residuals for a fitted <code>scam</code> model
 object. Pearson, deviance, working and response residuals are
 available.

models

regression

smooth

Generalized additive models under shape
constraints on the component functions of the linear predictor.
Models can include multiple shape-constrained (univariate
and bivariate) and unconstrained terms. Routines of the
package 'mgcv' are used to set up the model matrix, print,
and plot the results. Multiple smoothing parameter
estimation by the Generalized Cross Validation or similar.
See Pya and Wood (2015) <doi:10.1007/s11222-013-9448-7>
for an overview. A broad selection of shape-constrained
smoothers, linear functionals of smooths with shape constraints,
and Gaussian models with AR1 residuals.

Natalya Pya

scam

Shape Constrained Additive Models

residuals.scam function

<dl> <dt>object</dt>
<dd>a <code>scam</code> fitted model object.</dd> <dt>type</dt>
<dd>the type of residuals wanted.</dd> <dt>...</dt>
<dd>other arguments.</dd></dl>

Arguments

Natalya Pya &lt;nat.pya@gmail.com&gt;

Author

This function is a clone of the <code>mgcv</code> library code <code><a href='https://rdrr.io/pkg/mgcv/man/residuals.gam.html'>residuals.gam</a></code>.
It returns residuals for a fitted <code>scam</code> model
 object. Pearson, deviance, working and response residuals are
 available.

SCAM residuals — residuals.scam

<dl>

 <dt>object</dt>
<dd>a <code>scam</code> fitted model object.</dd>

 <dt>type</dt>
<dd>the type of residuals wanted.</dd>

 <dt>...</dt>
<dd>other arguments.</dd>

</dl>

residuals.scam: SCAM residuals

Description

Usage

Value

Arguments

Author

Details

See Also