compute_marginalAPCeffects

Internal helper function to add lower and upper confidence boundaries
pointwise

Age-Period-Cohort (APC) analyses are used to differentiate relevant drivers for long-term developments.
The 'APCtools' package offers visualization techniques and general routines to simplify the workflow of an APC analysis.
Sophisticated functions are available both for descriptive and regression model-based analyses.
For the former, we use density (or ridgeline) matrices and (hexagonally binned) heatmaps as innovative visualization
techniques building on the concept of Lexis diagrams.
Model-based analyses build on the separation of the temporal dimensions based on generalized additive models,
where a tensor product interaction surface (usually between age and period) is utilized
to represent the third dimension (usually cohort) on its diagonal.
Such tensor product surfaces can also be estimated while accounting for
further covariates in the regression model.
See Weigert et al. (2021) <doi:10.1177/1354816620987198> for methodological details.

Alexander Bauer

APCtools

Routines for Descriptive and Model-Based APC Analysis

Maximilian Weigert

Hawre Jalal

compute_marginalAPCeffects function

<dl><dt>dat</dt>
<dd>Dataset containing predicted effects for a grid of all APC
dimensions and covariates used in the model.</dd>
<dt>model</dt>
<dd>Model fitted with <code><a href="/link/gam?package=APCtools&version=1.0.8" data-mini-rdoc="APCtools::gam">gam</a></code> or <code><a href="/link/bam?package=APCtools&version=1.0.8" data-mini-rdoc="APCtools::bam">bam</a></code>.</dd>
<dt>variable</dt>
<dd>One of <code>c("age","period","cohort")</code>, specifying the
temporal dimension for which the partial effect plots should be created.</dd>
<dt>plot_CI</dt>
<dd>Indicator if 95% confidence intervals for marginal APC effects
should be computed. Defaults to FALSE.</dd></dl>

Arguments

Internal helper to compute marginal APC effects and their
confidence intervals — compute_marginalAPCeffects

<dl>

<dt>dat</dt>
<dd>Dataset containing predicted effects for a grid of all APC
dimensions and covariates used in the model.</dd>


<dt>model</dt>
<dd>Model fitted with <code><a href='https://rdrr.io/pkg/mgcv/man/gam.html'>gam</a></code> or <code><a href='https://rdrr.io/pkg/mgcv/man/bam.html'>bam</a></code>.</dd>


<dt>variable</dt>
<dd>One of <code>c("age","period","cohort")</code>, specifying the
temporal dimension for which the partial effect plots should be created.</dd>


<dt>plot_CI</dt>
<dd>Indicator if 95% confidence intervals for marginal APC effects
should be computed. Defaults to FALSE.</dd>

</dl>

compute_marginalAPCeffects: Internal helper to compute marginal APC effects and their confidence intervals

Description

Usage

Arguments

Details