Functions to transform data sets in different formats to PED format, suitable to be fit as PAMMs.
as_ped() is.ped() as_ped_recurrent()
as_ped()
is.ped()
as_ped_recurrent()
Transform data to Piece-wise Exponential Data (PED)
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
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)
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_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
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()
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()
get_cumu_eff()
gg_cumu_eff()
Calculate (or plot) cumulative effect for all time-points of the follow-up
Data sets contained in pammtools
pammtools
tumor
Stomach area tumor data
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