View source: R/BoundingCovariateEffects.R
pi.surv | R Documentation |
This function estimates bounds on the coefficients the single-
index model \Lambda(x^\top \beta(t))
for the conditional cumulative
distribution function of the event time.
pi.surv(
data,
idx.param.of.interest,
idxs.c,
t,
par.space,
search.method = "GS",
add.options = list(),
verbose = 0,
picturose = FALSE,
parallel = FALSE
)
data |
Data frame containing the data on which to fit the model. The columns should be named as follows: 'Y' = observed timed, 'Delta' = censoring indicators, 'X0' = intercept column, 'X1' - 'Xp' = covariates. |
idx.param.of.interest |
Index of element in the covariate vector for
which the identified interval should be estimated. It can also be specified
as |
idxs.c |
Vector of indices of the continuous covariates. Suppose the
given data contains 5 covariates, of which 'X2' and 'X5' are continuous, this
argument should be specified as |
t |
Time point for which to estimate the identified set of
|
par.space |
Matrix containing bounds on the space of the parameters. The first column corresponds to lower bounds, the second to upper bounds. The i'th row corresponds to the bounds on the i'th element in the parameter vector. |
search.method |
The search method to be used to find the identified
interval. Default is |
add.options |
List of additional options to be specified to the method.
Notably, it can be used to select the link function |
verbose |
Verbosity level. The higher the value, the more verbose the
method will be. Default is |
picturose |
Picturosity flag. If |
parallel |
Flag for whether or not parallel computing should be used.
Default is |
Matrix containing the identified intervals of the specified coefficients, as well as corresponding convergence information of the estimation algorithm.
Willems, I., Beyhum, J. and Van Keilegom, I. (2024+). Partial identification for a class of survival models under dependent censoring. (In preparation).
# Clear workspace
rm(list = ls())
# Load the survival package
library(survival)
# Set random seed
set.seed(123)
# Load and preprocess data
data <- survival::lung
data[, "intercept"] <- rep(1, nrow(data))
data[, "status"] <- data[, "status"] - 1
data <- data[, c("time", "status", "intercept", "age", "sex")]
colnames(data) <- c("Y", "Delta", "X0", "X1", "X2")
# Standardize age variable
data[, "X1"] <- scale(data[, "X1"])
## Example:
## - Link function: AFT link function (default setting)
## - Number of IF: 5 IF per continuous covariate (default setting)
## - Search method: Binary search
## - Type of IF: Cubic spline functions for continuous covariate, indicator
## function for discrete covariate (default setting).
# Settings for main estimation function
idx.param.of.interest <- 2 # Interest in effect of age
idxs.c <- 1 # X1 (age) is continuous
t <- 200 # Model imposed at t = 200
search.method <- "GS" # Use binary search
par.space <- matrix(rep(c(-10, 10), 3), nrow = 3, byrow = TRUE)
add.options <- list()
picturose <- TRUE
parallel <- FALSE
# Estimate the identified intervals
pi.surv(data, idx.param.of.interest, idxs.c, t, par.space,
search.method = search.method, add.options = add.options,
picturose = picturose, parallel = parallel)
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