Residuals for Accelerated failure time model models

Compute several types of residuals (censored Z-residuals, Cox–Snell, martingale, and deviance) for accelerated failure time models fitted with survreg.

Usage

residual.survreg(
  survreg_fit,
  newdata,
  residual.type = c("censored Z-residual", "Cox-Snell", "martingale", "deviance")
)

Arguments

survreg_fit: A fitted survreg model object. The model should be a right-censored survival regression model (i.e., with a Surv(time, status) response), using one of the supported distributions.
newdata: A data.frame containing the variables required by survreg_fit$terms, including the Surv() response and all covariates. Residuals are evaluated for the observations in newdata.
residual.type: Character string specifying the type of residual to compute. Must be one of "censored Z-residual", "Cox-Snell", "martingale", or "deviance". The default is the full vector c("censored Z-residual", "Cox-Snell", "martingale", "deviance"), but in practice a single value should be supplied.

Value

A numeric matrix of dimension $n \times 1$, where $n$ is the number of observations in newdata. The single column is named according to residual.type. Several attributes are attached:

Survival.Prob: vector of survival probabilities $S_i(t_i)$.
linear.pred: vector of linear predictors $\eta_i$.
covariates: model matrix of covariates used in the linear predictor.
censored.status: event indicator (1 = event, 0 = censored).
object.model.frame: the model.frame constructed from survreg_fit$terms and newdata.

Examples

if (FALSE) { # \dontrun{
  library(survival)

  data(lung)

  ## Weibull survival regression
  fit_weib <- survreg(Surv(time, status) ~ age + sex,
                      data = lung, dist = "weibull")

  ## Censored Z-residuals
  r_z <- residual.survreg(fit_weib, newdata = lung,
                          residual.type = "censored Z-residual")

  ## Cox–Snell residuals
  r_cs <- residual.survreg(fit_weib, newdata = lung,
                           residual.type = "Cox-Snell")

  ## Martingale residuals
  r_m <- residual.survreg(fit_weib, newdata = lung,
                          residual.type = "martingale")

  ## Deviance residuals
  r_d <- residual.survreg(fit_weib, newdata = lung,
                          residual.type = "deviance")
} # }