get_term

A data frame containing variables used to fit the model. Only
first row will be used.

data

A fitted object of class <code><a rd-options="mgcv" href="/link/gam?package=pammtools&version=0.5.7&to=mgcv" data-mini-rdoc="mgcv::gam">gam</a></code>.

The (non-linear) model term of interest.

term

Specify the output sequence either by supplying the
length of the sequence with <code>n</code>, or the spacing between value
with <code>by</code>. Specifying both is an error.
I recommend that you name these arguments in order to make it clear to
the reader.

Further arguments passed to <code><a rd-options="" href="/link/seq_range?package=pammtools&version=0.5.7" data-mini-rdoc="pammtools::seq_range">seq_range</a></code>.

Extract partial effects for specified model terms

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, competing risks and recurrent events data.
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

Philipp Kopper

get_term function

A fitted object of class <code><a rd-options='mgcv' href='gam'>gam</a></code>.

Further arguments passed to <code><a rd-options='' href='seq_range'>seq_range</a></code>.

get_term: Extract partial effects for specified model terms

Description

Usage

Arguments