Function reference • pammtools

Transform data to Piece-wise Exponential Data (PED) format Functions to transform data sets in different formats to PED format, suitable to be fit as PAMMs.
`as_ped()` `is.ped()` `as_ped_multistate()`	Transform data to Piece-wise Exponential Data (PED)
PED helper functions Functions that help extract (interval-specific) summary information from PED and create newdata, e.g., for prediction and plotting.
`int_info()`	Create start/end times and interval information
`ped_info()`	Extract interval information and median/modus values for covariates
`make_newdata()`	Construct a data frame suitable for prediction
`get_intervals()`	Information on intervals in which times fall
Extract information from PAMMs Functions that help extract information from fitted model objects, e.g., smooth effects for plotting
`tidy_fixed()`	Extract fixed coefficient table from model object
`tidy_smooth()`	Extract 1d smooth objects in tidy data format.
`tidy_smooth2d()`	Extract 2d smooth objects in tidy format.
`tidy_re()`	Extract random effects in tidy data format.
`get_cumu_coef()`	Extract cumulative coefficients (cumulative hazard differences)
`as.data.frame(<crps>)`	Transform crps object to data.frame
`predictSurvProb(<pamm>)`	S3 method for pamm objects for compatibility with package pec
Augment functions Functions that augment a data set by different quantaties like the hazard rate.
`add_term()`	Embeds the data set with the specified (relative) term contribution
`add_hazard()` `add_cumu_hazard()`	Add predicted (cumulative) hazard to data set
`add_surv_prob()`	Add survival probability estimates
`add_cif()`	Add cumulative incidence function to data
Convenience functions for visualization of model outputs. Functions that facilitate effect plots (smooth effects, etc.)
`gg_fixed()`	Forrest plot of fixed coefficients
`gg_smooth()`	Plot smooth 1d terms of gam objects
`gg_tensor()`	Plot tensor product effects
`gg_re()`	Plot Normal QQ plots for random effects
`gg_slice()`	Plot 1D (smooth) effects
`gg_partial()` `gg_partial_ll()` `get_partial_ll()`	Visualize effect estimates for specific covariate combinations
`gg_laglead()`	Plot Lag-Lead windows
`get_cumu_eff()` `gg_cumu_eff()`	Calculate (or plot) cumulative effect for all time-points of the follow-up
`geom_hazard()` `geom_stephazard()` `geom_surv()`	(Cumulative) (Step-) Hazard Plots.
`geom_stepribbon()`	Step ribbon plots.
`get_plotinfo()`	Extract plot information for all special model terms
Data sets Data sets contained in `pammtools`
`tumor`	Stomach area tumor data
`patient`	Survival data of critically ill ICU patients
`daily`	Time-dependent covariates of the `patient` data set.
`staph`	Time until staphylococcus aureaus infection in children, with possible recurrence
`simdf_elra`	Simulated data with cumulative effects
Utilities Utility functions
`seq_range()`	Generate a sequence over the range of a vector
`sim_pexp()`	Simulate survival times from the piece-wise exponential distribution
`get_laglead()`	Construct or extract data that represents a lag-lead window
`add_tdc()`	Add time-dependent covariate to a data set
`pammtools`	pammtools: Piece-wise exponential Additive Mixed Modeling tools.
`get_terms()`	Extract the partial effects of non-linear model terms

Reference

Transform data to Piece-wise Exponential Data (PED) format

PED helper functions

Extract information from PAMMs

Augment functions

Convenience functions for visualization of model outputs.

Data sets

Utilities