ks_test: Goodness-of-fit diagnostics

In longevity: Statistical Methods for the Analysis of Excess Lifetimes

Goodness-of-fit diagnostics

Description

Warning: EXPERIMENTAL Compute the Kolmogorov-Smirnov test statistic and compare it with a simulated null distribution obtained via a parametric bootstrap.

Usage

ks_test(
  time,
  time2 = NULL,
  event = NULL,
  thresh = 0,
  ltrunc = NULL,
  rtrunc = NULL,
  type = c("right", "left", "interval", "interval2"),
  family = c("exp", "gp", "gomp", "gompmake", "weibull", "extgp", "gppiece",
    "extweibull", "perks", "beard", "perksmake", "beardmake"),
  B = 999L,
  arguments = NULL,
  ...
)

Arguments

time	excess time of the event of follow-up time, depending on the value of event
time2	ending excess time of the interval for interval censored data only.
event	status indicator, normally 0=alive, 1=dead. Other choices are TRUE/FALSE (TRUE for death). For interval censored data, the status indicator is 0=right censored, 1=event at time, 2=left censored, 3=interval censored. Although unusual, the event indicator can be omitted, in which case all subjects are assumed to have experienced an event.
thresh	vector of thresholds
ltrunc	lower truncation limit, default to NULL
rtrunc	upper truncation limit, default to NULL
type	character string specifying the type of censoring. Possible values are "right", "left", "interval", "interval2".
family	string; choice of parametric family
B	number of bootstrap simulations
arguments	a named list specifying default arguments of the function that are common to all elife calls
...	additional parameters, currently ignored

Value

a list with elements

• stat the value of the test statistic

• pval p-value obtained via simulation

Note

The bootstrap scheme requires simulating new data, fitting a parametric model and estimating the nonparametric maximum likelihood estimate for each new sample. This is computationally intensive in large samples.

longevity documentation built on Nov. 12, 2023, 5:07 p.m.