simDat: Simulated datasets for demonstration
| simDat | R Documentation |
Simulated datasets for demonstration
Description
The dataset was generated based on the proposed model h(t; \boldsymbol{Z}_i, {X}_i(\cdot))=h_{0}(t-t_{i,j-1}) \exp \left(\eta_{ij}\right),
where h_0(\cdot) is the baseline hazard function generated from a Weibull distribution. \eta_{ij} = \bm{\alpha}^{\top}\boldsymbol{Z}_i +\int_{t_{i, j-1}}^{t}{X}_{i}(s)\beta(s)ds + v_{ij}.
\bm{\alpha} is the fixed effect parameter associated with the time-invariant covariates \boldsymbol{Z}_i,
and \beta(t) is a time-varying coefficient that captures the effect of functional predictor X_{i}(t) on the hazard rate of recurrent events.
The simulated dataset is organized into two data frames:
a survival data frame (sdat) and a functional data frame (fdat).
The variables in each data frame are listed below:
Usage
data(simDat)
Format
A list with two data frame:
- sdat
Survival data; a data frame with xxx rows and xxx variables:
- id
Subjects identification
- event
A sequence of the number of events per subject
- t_start
Event starting time
- t_end
Event end time
- censoring_time
Event censoring time
- status
Event status:
status=1if event is observed andstatus=0if event is censored- z1
A univariate scalar covariates. Can be extended to multiple scalar covariates
- fdat
Functional data; a data frame with xxx rows and xxx variables:
- id
Subjects identification
- time
Time points for each longitudinal measurement
- x
Longitudinal measurements at distinct time points
Source
Simulated data
