gam_sandwich_cluster

Computes a cluster-robust (CR1) sandwich covariance matrix for a fitted
GAM, clustering by curve. Handles both heteroskedasticity and within-curve
autocorrelation, requiring only that curves are independent.

internal

Methods for regression for functional
data, including function-on-scalar, scalar-on-function, and
function-on-function regression. Some of the functions are applicable to
image data.

Julia Wrobel

refund

Regression with Functional Data

Jeff Goldsmith

Fabian Scheipl

Lei Huang

Chongzhi Di

Jonathan Gellar

Jaroslaw Harezlak

Bruce Swihart

Luo Xiao

Ciprian Crainiceanu

Yakuan Chen

Sonja Greven

Lan Huo

So Young Park

Ana-Maria Staicu

Erjia Cui

Ruonan Li

Zheyuan Li

gam_sandwich_cluster function

<dl><dt>b</dt>
<dd>Fitted GAM object (must not have class `"pffr"`).</dd>
<dt>cluster_id</dt>
<dd>Integer vector of length `nrow(model.matrix(b))` mapping
each vectorized observation to its curve.</dd>
<dt>freq</dt>
<dd>If `TRUE`, use frequentist sandwich (`B2 = 0`).
If `FALSE` (default), use Bayesian sandwich (`B2 = Vp - Ve`).</dd></dl>

Arguments

Cluster-robust sandwich covariance estimator — gam_sandwich_cluster

<dl>

<dt>b</dt>
<dd>Fitted GAM object (must not have class `"pffr"`).</dd>


<dt>cluster_id</dt>
<dd>Integer vector of length `nrow(model.matrix(b))` mapping
each vectorized observation to its curve.</dd>


<dt>freq</dt>
<dd>If `TRUE`, use frequentist sandwich (`B2 = 0`).
If `FALSE` (default), use Bayesian sandwich (`B2 = Vp - Ve`).</dd>

</dl>

Cluster-robust sandwich covariance estimator

gam_sandwich_cluster: Cluster-robust sandwich covariance estimator

Description

Usage

Value

Arguments