strip_pamm_fit

Removes the per-observation slots (model frame, fitted values, residuals,
working weights, ...) and the <code>call</code> (which captures the full PED data),
none of which are needed for the downstream multiple-imputation pooling: the
pooled <code>add_*</code> methods only require each fit's <code>coefficients</code>,
<code>Vp</code>/<code>Ve</code> and the smooth/parametric structure used by
<code>predict(type = "lpmatrix")</code>. Stripping makes the stored size independent
of the data set size, so memory does not blow up with many imputations.

internal

The Piece-wise exponential (Additive Mixed) Model
(PAMM; Bender and others (2018) <doi: 10.1177/1471082X17748083>) is a
powerful model class for the analysis of survival (or time-to-event) data,
based on Generalized Additive (Mixed) Models (GA(M)Ms).
It offers intuitive specification and robust estimation of complex survival
models with stratified baseline hazards, random effects, time-varying effects,
time-dependent covariates and cumulative effects (Bender and others (2019)),
as well as support for left-truncated data as well as competing risks,
recurrent events and multi-state settings.
pammtools provides tidy workflow for survival analysis with PAMMs,
including data simulation, transformation and other functions for data
preprocessing and model post-processing as well as visualization.

Andreas Bender

pammtools

Piece-Wise Exponential Additive Mixed Modeling Tools for
Survival Analysis

Fabian Scheipl

Johannes Piller

Philipp Kopper

Lukas Burk

strip_pamm_fit function

<dl><dt>fit</dt>
<dd>A fitted <code>pamm</code>/<code>gam</code> object.</dd></dl>

Arguments

Slim down a fitted PAMM for storage inside a pamm_ic object — strip_pamm_fit

<dl>

<dt>fit</dt>
<dd>A fitted <code>pamm</code>/<code>gam</code> object.</dd>

</dl>

strip_pamm_fit: Slim down a fitted PAMM for storage inside a `pamm_ic` object

Description

Usage

Value

Arguments