depCensoring (version 0.1.8)

QRdepCens: Estimate the model of D'Haen et al. (2025).

Description

This function estimates the parameters in the model of D'Haen et al. (2025).

Usage

QRdepCens(data, hp, var.estimate = FALSE, verbose = TRUE)

Arguments

data

Data on which the model should be estimated. Note that the data should be structured in a specific form: The observed times should be put in a column named "Y", the censoring indicators in a column named "Delta", and the covariates in columns named "X1", "X2", ... (in increasing order). The given data set cannot contain any other columns.

hp

List of hyperparameters to be used, the elements of which will overwrite the default settings. In particular, consider changing:

test_cop_name:

Copula to be used. Should be one of "frank", "gumbel", "clayton" or "indep".

homoscedastic:

Boolean flag indicating whether homoscedasticity can be assumed.

variance.bootstrap.iterations:

Number of bootstrap resamples to use during variance estimation. Consider increase if more precision is needed; consider decreasing to reduce computation time.

Other hyperparameters can be changed though it is not recommended. We refer to the source code for the available options.

var.estimate

Boolean value indicating whether the variance should be estimated (via bootstrap). This can take a considerable amount of time. Default is var.estimate = FALSE.

verbose

Verbosity flag (boolean) indicating whether the results should be printed to the console. Default is verbose = TRUE.

References

D'Haen, M., Van Keilegom, I. and Verhasselt, A. (2025). Quantile regression under dependent censoring with unknown association. Lifetime Data Analysis 31(2):253-299.

Examples

# \donttest{

 # Load the data
 data(liver)

 # Give standard column names (required!)
 colnames(liver) <- c("patient", "Y", "Delta", "X1", "X2", "X3", "X4")
 liver <- liver[, c(-1, -6, -7)]

 # Run the model
 hp <- list(
   homoscedastic = FALSE,
   test_cop_name = "frank"
 )
 QRdepCens(liver, hp, var.estimate = FALSE)
 # Takes a while if var.estimate = TRUE...
# }