Package 'afttest'

Title: Model diagnostics for accelerated failure time models
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.
Authors: Woojung Bae [aut, cre] , Dongrak Choi [aut] , Jun Yan [aut] , Sangwook Kang [aut]
Maintainer: Woojung Bae <[email protected]>
License: GPL (>= 3)
Version: 4.3.3
Built: 2024-11-02 04:43:00 UTC
Source: https://github.com/woojungbae/afttest

Help Index

afttest

Description

afttest

Usage

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

Arguments

formula

A formula expression, of the form response ~ predictors. The response is a Surv object object with right censoring. See the documentation of lm, coxph and formula for details.
data

An optional data frame in which to interpret the variables occurring in the formula.
path

An integer value specifies the number of approximated processes. The default is given by 200.
testType

A character string specifying the type of the test. The following are permitted:

omni

an omnibus test

link

a link function test

form

a functional form
eqType

A character string specifying the type of the estimating equation used to obtain the regression parameters. The readers are refered to the aftgee package for details. The following are permitted:

mns

Regression parameters are estimated by iterating the monotonic non-smoothed Gehan-based estimating equations.

mis

Regression parameters are estimated by iterating the monotonic smoothed Gehan-based estimating equations.
optimType

A character string specifying the type of the optimization method. The following are permitted:

DFSANE

See the documentation of BB packages for details.

Nelder-Mead

See the documentation of optim for details.

BFGS

See the documentation of optim for details.

CG

See the documentation of optim for details.

L-BFGS-B

See the documentation of optim for details.

SANN

See the documentation of optim for details.

Brent

See the documentation of optim for details.
form

A character string specifying the covariate which will be tested. The argument form is necessary only if testType is form. The default option for form is given by "1", which represents the first covariate in the formula argument.
pathsave

An integer value specifies he number of paths saved among all the paths. The default is given by 50. Note that it requires a lot of memory if save all sampled paths (N by N matrix for each path andso path*N*N elements)

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

object

is a afttest fit
path

A numeric value specifies the number of approximated processes plotted The default is set to be 100.
stdType

A character string specifying if the graph is based on the unstandardized test statistics or standardized test statistics The default is set to be "std".
quantile

A numeric vector specifies 5 of five quantiles within the range [0,1]. The default is set to be c(0.1,0.25,0.5,0.75,0.9).

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

x

is a afttest fit.
...

other options.

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

object

is a afttest fit.
...

other options.

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)