pammtools package - RDocumentation

Learn R Programming

⚠️There's a newer version (0.7.4) of this package.Take me there.

`pammtools`: Piece-Wise Exponential Additive Mixed Modeling Tools

Installation

Install from CRAN or GitHub using:

# CRAN
install.packages("pammtools")
# GitHub
devtools::install_github("adibender/pammtools")

Overview

pammtools offers various utilities and convenience functions that facilitate working with Piece-wise exponential Additive Mixed Models (PAMMs).

To get started, see the Articles section.

An overview over the packages functionality is given in

Andreas Bender and Fabian Scheipl, 2018: "pammtools: Piece-wise exponential

Additive Mixed Modeling tools", arXiv eprint, 2018, https://arxiv.org/abs/1806.01042

For a tutorial-like introduction to PAMMs see:

Andreas Bender, Andreas Groll, and Fabian Scheipl. 2018. “A Generalized Additive Model Approach to Time-to-Event Analysis.” Statistical Modelling. https://doi.org/10.1177/1471082X17748083.

A general framework for the representation and estimation of cumulative effects (or exposure-lag-response associations) is described in:

Andreas Bender, Fabian Scheipl, Wolfgang Hartl, Andrew G Day, Helmut Küchenhoff; "Penalized estimation of complex, non-linear exposure-lag-response associations", Biostatistics, , kxy003, https://doi.org/10.1093/biostatistics/kxy003

Copy Link

Version

Install

install.packages('pammtools')

Monthly Downloads

5,663

Version

0.1.9

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/adibender/pammtools

Maintainer

Andreas Bender

Last Published

March 14th, 2019

Functions in pammtools (0.1.9)

Extract unique cut points when time-dependent covariates present

(Cumulative) (Step-) Hazard Plots.

Extract the partial effects of non-linear model terms

geom_stepribbon

Step ribbon plots.

Calculate survival probabilities

Calculate simulation based confidence intervals

Forrest plot of fixed coefficients

Calculate the modus

Transform data to Piece-wise Exponential Data (PED)

Add time-dependent covariate to a data set

Create nested data frame from data with time-dependent covariates

Add the contribution of a term to the linear predictor to data set

Calculate confidence intervals

Time-dependent covariates of the patient data set.

A formula special used to handle cumulative effect specifications

prep_concurrent

Extract information on concurrent effects

dplyr Verbs for ped-Objects

Extract cumulative coefficients (cumulative hazard differences)

get_cumu_coef_baseline

Extract cumulative coefficients (cumulative hazard differences)

compute_cumu_diff

Calculate difference in cumulative hazards and respective standard errors

Extract variables from the left-hand-side of a formula

get_cumu_hazard

Calculate cumulative hazard

Calculate (or plot) cumulative effect for all time-points of the follow-up

tidyr Verbs for ped-Objects

Stomach area tumor data

Plot Normal QQ plots for random effects

Expand time-dependent covariates to functionals

Extract time-dependent covariates from data set

Extract variables from the left-hand-side of a formula

Plot smooth 1d terms of gam objects

Plot 1D (smooth) effects

Plot tensor product effects

Function to transform data without time-dependent covariates into piece-wise exponential data format

Information on intervals in which times fall

warn_about_new_time_points

Warn if new t_j are used

Construct or extract data that represents a lag-lead window

Extract variables from the left-hand-side of a formula

Extract time-dependent covariates from data set

Extract partial effects for specified model terms

Create start/end times and interval information

Extract risk set information for each interval.

Create piece-wise exponential data in case of time-dependent covariates

Extract information of the sample contained in a data set

Extract variables from the left-hand-side of a formula

Extract 1d smooth objects in tidy data format.

Extract plot information for all special model terms

Plot Lag-Lead windows

Add predicted (cumulative) hazard to data set

Add survival probability estimates

Extract fixed coefficient table from model object

Visualize effect estimates for specific covariate combinations

Create matrix components for cumulative effects

Time until extubation

Obtain interval break points

Survival data of critically ill ICU patients

Calculate predicted hazard

Extract interval information and median/modus values for covariates

Construct a data frame suitable for prediction

Generate a sequence over the range of a vector

Simulate survival times from the piece-wise exponential distribution

pammtools: Piece-wise exponential Additive Mixed Modeling tools.

Simulated data with cumulative effects

Formula specials for defining time-dependent covariates

Extract random effects in tidy data format.

Extract 2d smooth objects in tidy format.

Create a data frame from all combinations of data frames