Synthetic.4: Synthetic Dataset #4:
Description
Dataset from simulated regression survival model #4 as described in Dazard et al. (2015).
Here, the regression function uses 1/10 of informative predictors in a \(p > n\) situation with \(p = 1000\) and \(n = 100\).
The rest represents non-informative noisy covariates, which are not part of the design matrix.
Survival time was generated from an exponential model with rate parameter \(\lambda\) (and mean \(\frac{1}{\lambda}\))
according to a Cox-PH model with hazard exp(eta), where eta(.) is the regression function.
Censoring indicator were generated from a uniform distribution on [0, 2].
In this synthetic example, all covariates are continuous, i.i.d. from a multivariate standard normal distribution.Format
Each dataset consists of a numeric matrix containing \(n=100\) observations (samples)
by rows and \(p=1000\) variables by columns, not including the censoring indicator and (censored) time-to-event variables.
It comes as a compressed Rda data file.References
- Dazard J-E., Choe M., LeBlanc M. and Rao J.S. (2015).
"Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods."
Statistical Analysis and Data Mining (in press).
- Dazard J-E., Choe M., LeBlanc M. and Rao J.S. (2014).
"Cross-Validation of Survival Bump Hunting by Recursive Peeling Methods."
In JSM Proceedings, Survival Methods for Risk Estimation/Prediction Section. Boston, MA, USA.
American Statistical Association IMS - JSM, p. 3366-3380.
- Dazard J-E., Choe M., LeBlanc M. and Rao J.S. (2015).
"R package PRIMsrc: Bump Hunting by Patient Rule Induction Method for Survival, Regression and Classification."
In JSM Proceedings, Statistical Programmers and Analysts Section. Seattle, WA, USA.
American Statistical Association IMS - JSM, (in press).
- Dazard J-E. and J.S. Rao (2010).
"Local Sparse Bump Hunting."
J. Comp Graph. Statistics, 19(4):900-92.