coxsei {coxsei} | R Documentation |
CoxSEI model
Description
Fit a CoxSEI model to counting process data
Usage
coxsei(x,...)
## Default S3 method:
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 class 'coxsei'
print(x,...)
## S3 method for class 'coxsei'
plot(x,...)
## S3 method for class 'coxsei'
summary(object,...)
Arguments
x |
a covariate matrix, or an object of class |
y |
a vector of observed times |
delta |
a vector of event indicators: 1=event, 0=censoring |
id |
the individual/group id to which the event/censoring time correspond |
par.init |
initial parameter guess to start the iteration |
m |
lag parameter as in m-dependence |
mit |
max number of iteration |
tr |
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 the |
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 |
Author(s)
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
See Also
Examples
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)