o_control

Function to control estimation of smooth offset

Regression models for functional data, i.e., scalar-on-function,
function-on-scalar and function-on-function regression models, are fitted
by a component-wise gradient boosting algorithm.
For a manual on how to use 'FDboost', see Brockhaus, Ruegamer, Greven (2017) <doi:10.18637/jss.v094.i10>.

David Ruegamer

FDboost

Boosting Functional Regression Models

Sarah Brockhaus

Almond Stoecker

Torsten Hothorn

with contributions by many others (see inst/CONTRIBUTIONS)

o_control function

<dl><dt>k_min</dt>
<dd>maximal number of k in s()</dd>
<dt>rule</dt>
<dd>which rule to use in approx() of the response before calculating the 
global mean, rule=1 means no extrapolation, rule=2 means to extrapolate the 
closest non-missing value, see <code><a href="/link/approx?package=FDboost&version=1.1-2" data-mini-rdoc="FDboost::approx">approx</a></code></dd>
<dt>silent</dt>
<dd>print error messages of model fit?</dd>
<dt>cyclic</dt>
<dd>defaults to FALSE, if TRUE cyclic splines are used</dd>
<dt>knots</dt>
<dd>arguments knots passed to <code><a href="/link/gam?package=FDboost&version=1.1-2" data-mini-rdoc="FDboost::gam">gam</a></code></dd></dl>

Arguments

Function to control estimation of smooth offset — o_control

<dl>

<dt>k_min</dt>
<dd>maximal number of k in s()</dd>


<dt>rule</dt>
<dd>which rule to use in approx() of the response before calculating the 
global mean, rule=1 means no extrapolation, rule=2 means to extrapolate the 
closest non-missing value, see <code><a href='https://rdrr.io/r/stats/approxfun.html'>approx</a></code></dd>


<dt>silent</dt>
<dd>print error messages of model fit?</dd>


<dt>cyclic</dt>
<dd>defaults to FALSE, if TRUE cyclic splines are used</dd>


<dt>knots</dt>
<dd>arguments knots passed to <code><a href='https://rdrr.io/pkg/mgcv/man/gam.html'>gam</a></code></dd>

</dl>

o_control: Function to control estimation of smooth offset

Description

Usage

Value

Arguments