confidence_band

<code>confidence_band</code> computes the lower and upper limits of smooth
terms fitted with <code>intensity_pspline</code>.

Fitting the intensity function of point
processes on geometric networks. The method makes use of generalized
additive models (GAM). The method is described in Marc Schneble, Göran Kauermann.
"Intensity estimation on geometric networks with penalized splines." The
Annals of Applied Statistics, 16(2) 843-865 June 2022.
<doi:10.1214/21-AOAS1522>.
A new class for representing linear networks as
geometric networks is the core of the package.

Marc Schneble

geonet

Intensity Estimation on Geometric Networks with Penalized
Splines

confidence_band function

<dl><dt>theta</dt>
<dd>The estimated coefficients which corresponds to the smooth
term.</dd>
<dt>V</dt>
<dd>The covariance matrix of the estimated coefficients <code>theta</code>.</dd>
<dt>X</dt>
<dd>The design matrix of the model which corresponds to the smooth
term.</dd>
<dt>q</dt>
<dd>The quantile. Default to <code>q = 0.05</code> which corresponds to
95% confidence bands.</dd>
<dt>R</dt>
<dd>The number of replications in the simulation process.</dd></dl>

Arguments

Confidence Bands of Smooth Terms — confidence_band

<dl>

<dt>theta</dt>
<dd>The estimated coefficients which corresponds to the smooth
term.</dd>


<dt>V</dt>
<dd>The covariance matrix of the estimated coefficients <code>theta</code>.</dd>


<dt>X</dt>
<dd>The design matrix of the model which corresponds to the smooth
term.</dd>


<dt>q</dt>
<dd>The quantile. Default to <code>q = 0.05</code> which corresponds to
95% confidence bands.</dd>


<dt>R</dt>
<dd>The number of replications in the simulation process.</dd>

</dl>

confidence_band: Confidence Bands of Smooth Terms

Description

Usage

Value

Arguments