Type:	Package
Title:	Model Diagnostics for Accelerated Failure Time Models
Version:	4.3.2.3
Date:	2024-01-15 EDT
Maintainer:	Woojung Bae <matt.woojung@gmail.com>
Description:	A collection of model checking methods for semiparametric accelerated failure time (AFT) models under the rank-based approach. For the (computational) efficiency, Gehan's weight is used. It provides functions to verify whether the observed data fit the specific model assumptions such as a functional form of each covariate, a link function, and an omnibus test. The p-value offered in this package is based on the Kolmogorov-type supremum test and the variance of the proposed test statistics is estimated through the re-sampling method. Furthermore, a graphical technique to compare the shape of the observed residual to a number of the approximated realizations is provided.
Imports:	survival, aftgee, ggplot2, gridExtra
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Depends:	R (≥ 3.4.0)
Config/testthat/edition:	3
License:	GPL (≥ 3)
URL:	https://github.com/WooJungBae/afttest
BugReports:	https://github.com/WooJungBae/afttest/issues
Encoding:	UTF-8
Language:	en-US
RoxygenNote:	7.3.0
NeedsCompilation:	yes
Packaged:	2024-01-16 01:34:01 UTC; woojung
Author:	Woojung Bae [aut, cre], Dongrak Choi [aut], Jun Yan [aut], Sangwook Kang [aut]
Repository:	CRAN
Date/Publication:	2024-01-16 16:30:05 UTC

afttest

Description

afttest

Usage

afttest(
  formula,
  data,
  path = 200,
  testType = "omni",
  eqType = "mns",
  optimType = "DFSANE",
  form = 1,
  pathsave = 50
)

Arguments

Value

afttest returns an object of class afttest. An object of class afttest is a list containing at least the following components:

beta

a vector of beta estimates based on aftsrr

SE_process

estimated standard error of the observed process

obs_process

observed process

app_process

approximated process

obs_std_process

standardized observed process

app_std_process

standardized approximated processes

p_value

obtained by the unstandardized test

p_std_value

obtained by the standardized test

DF

a data frame of observed failure time, right censoring indicator, covariates (scaled), time-transformed residual based on beta estimates

path

the number of sample paths

eqType

eqType

testType

testType

optimType

optimType

For an omnibus test, the observed process and the realizations are composed of the n by n matrix that rows represent the t and columns represent the x in the time-transformed residual order.The observed process and the simulated processes for checking a functional form and a link function are given by the n by 1 vector which is a function of x in the time-transformed residual order.

Examples

## Simulate data from an AFT model
library(afttest)
library(survival)
datgen <- function(n = 100) {
  z1 <- rbinom(n, 1, 0.5)
  z2 <- rnorm(n)
  e <- rnorm(n)
  tt <- exp(2 + z1 + z2 + e)
  cen <- runif(n, 0, 100)
  data.frame(Time = pmin(tt, cen), status = 1 * (tt < cen),
             z1 = z1, z2 = z2, id = 1:n)
}
set.seed(0)
simdata <- datgen(n = 20)
result <- afttest(Surv(Time, status) ~ z1 + z2, optimType = "DFSANE",
                  data = simdata, testType="link", eqType="mns")
summary(result)
# afttestplot(result)

afttestplot

Description

afttestplot

Usage

afttestplot(object, path = 50, stdType = "std", quantile = NULL)

Arguments

Value

afttestplot returns a plot based on the testType:

omni

an object of the omnibus test is the form of n by n matrix, some quantiles of x, which are used in weight, are plotted for graphs, i.e. 0%, 10%, 25%, 40%, 50%, 60%, 75%, 90%, and 100% are used.

link

an object of the link function test is the form of n by 1 matrix

form

an object of the functional form test is the form of n by 1 matrix

See the documentation of ggplot2 and gridExtra for details.\

Examples

## Simulate data from an AFT model
library(afttest)
library(survival)
datgen <- function(n = 100) {
  z1 <- rbinom(n, 1, 0.5)
  z2 <- rnorm(n)
  e <- rnorm(n)
  tt <- exp(2 + z1 + z2 + e)
  cen <- runif(n, 0, 100)
  data.frame(Time = pmin(tt, cen), status = 1 * (tt < cen),
             z1 = z1, z2 = z2, id = 1:n)
}
set.seed(0)
simdata <- datgen(n = 20)
result <- afttest(Surv(Time, status) ~ z1 + z2, optimType = "DFSANE",
                  data = simdata, testType="link", eqType="mns")
# summary(result)
afttestplot(result)

print.afttest

Description

print.afttest

Usage

## S3 method for class 'afttest'
print(x, ...)

Arguments

Value

print.afttest returns a summary of a afttest fit:

Examples

## Simulate data from an AFT model
library(afttest)
library(survival)
datgen <- function(n = 100) {
  z1 <- rbinom(n, 1, 0.5)
  z2 <- rnorm(n)
  e <- rnorm(n)
  tt <- exp(2 + z1 + z2 + e)
  cen <- runif(n, 0, 100)
  data.frame(Time = pmin(tt, cen), status = 1 * (tt < cen),
             z1 = z1, z2 = z2, id = 1:n)
}
set.seed(0)
simdata <- datgen(n = 20)
result <- afttest(Surv(Time, status) ~ z1 + z2, optimType = "DFSANE",
                  data = simdata, testType="link", eqType="mns")
summary(result)
# afttestplot(result)

summary.afttest

Description

summary.afttest

Usage

## S3 method for class 'afttest'
summary(object, ...)

Arguments

Value

summary.afttest returns a summary of a afttest fit:

Examples

## Simulate data from an AFT model
library(afttest)
library(survival)
datgen <- function(n = 100) {
  z1 <- rbinom(n, 1, 0.5)
  z2 <- rnorm(n)
  e <- rnorm(n)
  tt <- exp(2 + z1 + z2 + e)
  cen <- runif(n, 0, 100)
  data.frame(Time = pmin(tt, cen), status = 1 * (tt < cen),
             z1 = z1, z2 = z2, id = 1:n)
}
set.seed(0)
simdata <- datgen(n = 20)
result <- afttest(Surv(Time, status) ~ z1 + z2, optimType = "DFSANE",
                  data = simdata, testType="link", eqType="mns")
summary(result)
# afttestplot(result)