aftsem {aftsem}R Documentation

Accelerated Failure Time Semiparametric Model

Description

Accelerated Failure Time Semiparametric Model

Usage

aftsem(
  formula,
  data,
  control = aftsem.control(),
  method = "buckley",
  binit = "auto",
  ties = NULL,
  na.action = na.omit,
  subset = NULL,
  resample = 0,
  ...
)

Arguments

formula

A formula expression, of the form response ~ predictors. Response must be a Surv object

data

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

control

Control parameters for the AFT model.

method

A character string specifying the method to be used (buckley,jin,gehan,gehan-heller,gehan-poly).

binit

Initial values for the regression parameters.

ties

A method to handle ties in the failure times. If ties = NULL only warning will be printed. If ties = jitter, the data will be augumented

na.action

A method to deal with missing values (na.fail)

subset

An optional vector specifying a subset of observations to be used in the fitting process.

resample

Number of resamples for variance estimation for gehan and jin methods.

...

Additional arguments.

Value

A list representing the fit - 'call': Call of the function - 'cnames': Column names - 'method': Method of estimation - 'nobs': Number of observations - 'censored': Number of censored observations - 'betafirst': Initial beta - 'epsilon': Epsilon in convergence criterion - 'max_iterations': Max iterations for buckley and jin method - 'resample': Resample number - 'objects from aftsem.fit': All the object from fit function

Examples

# Generating example data
library(survival)
set.seed(123) # for reproducibility
n <- 100 # number of observations
Z <- matrix(rnorm(n*2), ncol = 2) # two covariates
beta <- c(0.5, -0.25) # true coefficients
times <- exp(Z %*% beta + rnorm(n)) # simulated survival times
censoring <- runif(n,0,30)
observed_times <- times
delta <- 1 * (times<=censoring)

# Fit the model


fit <- aftsem(Surv(log(observed_times), delta) ~ Z[,1] + Z[,2],
              method = "buckley",
              binit = "auto",
              ties = "NULL",
              na.action = na.omit,
              subset = NULL
)

# Print the summary
summary(fit)



[Package aftsem version 1.0 Index]