coxseiInt {coxsei} | R Documentation |
It takes the paramter of the parametric part (or its theorized value) and calculate the values of the estimator at the jump times; it also gives the values of the estimator for the variance of the intensity estimator.
coxseiInt(dat, parest, hessian=NULL, vcovmat=solve(hessian), m = 2, gfun = function(x, pa) { ifelse(x <= 0, 0, pa[1] * pa[2] * exp(-pa[2] * x)) }, gfungrd = function(x, pa){ if(length(x)==0)return(matrix(0,2,0)); rbind(pa[2]*exp(-pa[2]*x), pa[1]*exp(-pa[2]*x)*(1-pa[2]*x) ) })
dat |
a data frame containing the right-censored counting process data |
parest |
the estimate of parameter of the parametric part of the CoxSEI model |
hessian |
the hessian matrix returned by the optimization procedure in the estimation of the parametric part based on partial likelihood |
vcovmat |
the variance-covariance matrix of the estimator of the the parametric components; defaulted to the inverse of the hessian matrix |
m |
autoregressive order in the excitation part of the intensity |
gfun |
the excitation function; defaults to the exponential decay function |
gfungrd |
derivative/gradient function of the excitation function |
a list giving the jump times and values at these of the estimator of the cumulative baseline intensity function.
x |
the ordered death/event times |
y |
the value of the estimator of the intensity function at the observed death/event times |
varest |
the value of the estimator of the variance of the estimator of the intensity function, at the jump times |
The step function can be obtained using stepfun
, and plotted by setting
type="s"
in the plot
function.
Currently doesn't compute the standard error or variance estimator of the baseline cumulative intensity estimator.
Feng Chen <feng.chen@unsw.edu.au>
data("dat") est <- coxseiest3(dat,c(0.2,0.4,0.6,log(0.06),log(5))) pe <- est$par; pe[4:5] <- exp(pe[4:5]); ve <- diag(pe) %*% solve(est$hessian, diag(pe)); cintest <- coxseiInt(dat,pe,vcovmat=ve) plot(cintest,type="s")