Description Usage Arguments Value Author(s) References See Also Examples
This function generates survival data according to the simulation scenarios considered in Section 4 of Wu, J., and Witten, D. (2019) Flexible and interpretable models for survival data. Cox model has the form
λ(t|x) = λ_0(t) exp(∑_{j=1}^p f_j(x))
. Failure time is generated by Weibull distribution with baseline hazard
λ_0(t) = scale * shape * t ^ {shape-1}
. In the paper, however, failure time is generated by a simplied weibull distribution: exponential(1) baseline hazard corresponding to shape=1
and scale=1
. Censoring time is generated independently by exponential distribution with intensity censoring.rate
. Thus the observed time is the minimum of failure time and censoring time. Each scenario has four covariates that have some non-linear association with the outcome. There is the option to also generate a user-specified number of covariates that have no association with the outcome.
1 |
n |
number of observations. |
scenario |
Simulation scenario. Options are 1, 2, 3, 4. Scenario 1 corresponds to piecewise constant functions, scenario 2 corresponds to smooth functions, scenario 3 corresponds to piecewise linear functions, and scenario 4 corresponds to functions that have varying degrees of smoothness. Each scenario has four covariates that have some non-linear association with the outcome. |
zerof |
Number of additional covariates that have no association with the outcome. The total number of covariates is |
scale |
scale parameter as in |
shape |
shape parameter as in |
censoring.rate |
censoring intensity. Censoring time is generated by exponential distribution with intensity |
n.discrete |
The number of binary covariates and default is zero binary covariate. |
time |
failure or censoring time whichever comes first. |
status |
censoring indicator. 1 denotes censoring and 0 denotes failure. |
X |
n x p covariate matrix. |
true_theta |
n x p matrix. |
Jiacheng Wu
Jiacheng Wu & Daniela Witten (2019) Flexible and Interpretable Models for Survival Data, Journal of Computational and Graphical Statistics, DOI: 10.1080/10618600.2019.1592758
1 2 3 4 5 | #generate data
set.seed(123)
dat = sim_dat(n=100, zerof=0, scenario=1)
#plot X versus the true theta
plot.sim_dat(dat)
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