| Title: | Cox Regression (Proportional Hazards Model) with Multiple Causes and Mixed Effects |
|---|---|
| Description: | A high performance package estimating Cox Model when an even has more than one causes. It also supports random and fixed effects, tied events, and time-varying variables. |
| Authors: | Jing Peng |
| Maintainer: | Jing Peng <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.1.1 |
| Built: | 2024-11-20 06:25:16 UTC |
| Source: | CRAN |
A high performance package estimating Proportional Hazards Model when an even can have more than one causes, including support for random and fixed effects, tied events, and time-varying variables.
fastCox(head, formula, par = list(), data = NULL)fastCox(head, formula, par = list(), data = NULL)
head |
A data frame with 4~5 columns: start, stop, event, weight, strata (optional). |
formula |
A formula specifying the independent variables |
par |
A optional list of parameters controlling the estimation process |
data |
The dataset, a data frame containing observations on the independent variables |
A list containing the estimated parameters
1. Jing Peng, Ashish Agarwal, Kartik Hosanagar, and Raghuram Iyengar. Towards Effective Information Diffusion on Social Media Platforms: A Dyadic Analysis of Network Embeddedness. Working Paper.
2. Jing Peng, Ashish Agarwal, Kartik Hosanagar, and Raghuram Iyengar. Toward Effective Social Contagion: A Micro Level Analysis of the Impact of Dyadic Network Relationship. In Proceedings of the 2014 International Conference on Information Systems.
# Simulate a dataset. lam=exp(x), suvtime depends on lam x = rnorm(5000) suvtime = -log(runif(length(x)))/exp(x) # Censor 80% of events thd = quantile(suvtime, 0.2) event = as.numeric(suvtime <= thd) suvtime[suvtime>thd] = thd # The estimates of beta should be very close to 1, the true value head = cbind(start=0,stop=suvtime,event=event,weight=1) est = fastCox(head,~x) print(est$result)# Simulate a dataset. lam=exp(x), suvtime depends on lam x = rnorm(5000) suvtime = -log(runif(length(x)))/exp(x) # Censor 80% of events thd = quantile(suvtime, 0.2) event = as.numeric(suvtime <= thd) suvtime[suvtime>thd] = thd # The estimates of beta should be very close to 1, the true value head = cbind(start=0,stop=suvtime,event=event,weight=1) est = fastCox(head,~x) print(est$result)