vcov.gcrq

Returns the variance-covariance matrix of the parameter estimates of a fitted gcrq model object.

regression

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

vcov.gcrq function

<dl> <dt>object</dt>
<dd>a fitted model object of class <code>"gcrq"</code> returned by <code>gcrq()</code>.</dd> <dt>term</dt>
<dd>if specified, the returned covariance matrix includes entries relevant to parameter estimates for that 'term' only. If missing, the returned matrices refer to all model parameter estimates. Currently <code>term</code> is not allowed.</dd> <dt>type</dt>
<dd>Which cov matrix should be returned? <code>'boot'</code> means case-resampling bootstrap (see <code>n.boot</code> in <code>gcrq()</code>), <code>'sandw'</code> mean via the 
sandwich formula.</dd> <dt>...</dt>
<dd>additional arguments.</dd></dl>

Arguments

Author

Variance-Covariance Matrix for a Fitted 'gcrq' Model — vcov.gcrq

<dl>

 <dt>object</dt>
<dd>a fitted model object of class <code>"gcrq"</code> returned by <code>gcrq()</code>.</dd>

 <dt>term</dt>
<dd>if specified, the returned covariance matrix includes entries relevant to parameter estimates for that 'term' only. If missing, the returned matrices refer to all model parameter estimates. Currently <code>term</code> is not allowed.</dd>

 <dt>type</dt>
<dd>Which cov matrix should be returned? <code>'boot'</code> means case-resampling bootstrap (see <code>n.boot</code> in <code>gcrq()</code>), <code>'sandw'</code> mean via the 
sandwich formula.</dd>

 <dt>...</dt>
<dd>additional arguments.</dd>

</dl>

vcov.gcrq: Variance-Covariance Matrix for a Fitted 'gcrq' Model

Description

Usage

Value

Arguments

Author

Details

See Also