coxsei: CoxSEI model

Description

Fit a CoxSEI model to counting process data

Usage

coxsei(x,...)
# S3 method for default
coxsei(x,y,delta,id,par.init,m=2,mit=1000,tr=TRUE,
       method="L-BFGS-B",lower=c(rep(-Inf,ncol(x)),-Inf,0),
       upper=rep(Inf,ncol(x) + 2),...)
# S3 method for coxsei
print(x,...)
# S3 method for coxsei
plot(x,...)
# S3 method for coxsei
summary(object,...)

Arguments

a covariate matrix, or an object of class coxsei

a vector of observed times

delta

a vector of event indicators: 1=event, 0=censoring

the individual/group id to which the event/censoring time correspond

par.init

initial parameter guess to start the iteration

lag parameter as in m-dependence

mit

max number of iteration

whether to trace the optimization or not

method

method used in optimization

lower

the lower bound of the parameter space if the L-BFGS-B method of optimization is used.

upper

the upper bound of the paramter space if the L-BFGS-B methodof optimaization is used.

...

further arguments to plot.stepfun

object

an object of the class coxsei

Value

an object of class coxsei, basically a list of the following components

coefficients

a numeric vector of coefficients

vcov

the variance-covariance matrix

zval

the vector of z-value of the Wald test statistic

pval

the vector of p-values

details.par

a list returned by theoptim routine

cintfn

a step function as the estimated cumulative baseline intensity function

cintvar

a step function as the variance of the cumulative baseline intensity function estimator

details.cint

a list containing more details about the cint

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

Examples

Run this code

# NOT RUN {
data(dat,package="coxsei")
acoxsei <- coxsei(dat[,3:5],dat[,1],dat[,2],dat[,6],
                  c(0.2*1:3,log(0.07),log(10)))
summary(acoxsei)
plot(acoxsei,do.points=FALSE)
# }