print.gcrq

Printing the most important feautures of a gcrq model.

 models

Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms, including varying coefficients, via B-splines with L1-norm difference penalties.
Random intercepts and variable selection are allowed via the lasso penalties.
The smoothing parameters are estimated as part of the model fitting, see Muggeo and others (2021) <doi:10.1177/1471082X20929802>. Monotonicity and concavity
constraints on the fitted curves are allowed, see Muggeo and others (2013) <doi:10.1007/s10651-012-0232-1>,
and also <doi:10.13140/RG.2.2.12924.85122> or <doi:10.13140/RG.2.2.29306.21445> some code examples.

Vito MR Muggeo

quantregGrowth

Non-Crossing Additive Regression Quantiles and Non-Parametric
Growth Charts

print.gcrq function

<dl> <dt>x</dt>
<dd>object of class <code>gcrq</code></dd> <dt>digits</dt>
<dd>number of digits to be printed</dd> <dt>...</dt>
<dd>arguments passed to other functions</dd></dl>

Arguments

Author

Print method for the gcrq class — print.gcrq

<dl>

 <dt>x</dt>
<dd>object of class <code>gcrq</code></dd>

 <dt>digits</dt>
<dd>number of digits to be printed</dd>

 <dt>...</dt>
<dd>arguments passed to other functions</dd>

</dl>

print.gcrq: Print method for the gcrq class

Description

Usage

Arguments

Author

See Also