| coxsei-package {coxsei} | R Documentation |
Fit a Cox-type self-exciting intensity model (CoxSEI) to right-censored counting process data
Description
Fit the CoxSEI model using the partial likelihood method.
Details
To use the package, the data needs to be prepared into a data frame
containing a column named Y for observed event times in ascending
order of each individual process, a column named delta indicating
if the event is 'death' (1) or 'censoring' (0), a column named
id indicating the process id of each event time, and one or more
columns giving the value of any covariate variable at the observed event
times of each process. Then call the coxseiest function or the
identical but much faster coxseiest2 function to estimate the
parametric part of the model and then the coxseiInt function to
estimate the cumulative baseline intensity function.
Author(s)
Feng Chen <feng.chen@unsw.edu.au>
Maintainer: Feng Chen <feng.chen@unsw.edu.au>
References
Feng Chen and Kani Chen. (2014). Modeling Event Clustering Using the m-Memory Cox-Type Self-Exciting Intensity Model. International Journal of Statistics and Probability. 3(3): 126-137. doi:10.5539/ijsp.v3n3p126 URL: http://dx.doi.org/10.5539/ijsp.v3n3p126
Feng Chen and Kani Chen. (2014). Case-cohort analysis of clusters of recurrent events. 20(1): 1-15. doi: 10.1007/s10985-013-9275-3