| npcox {NPCox} | R Documentation |
Nonparametric and semiparametric Cox regression model.
Description
Estimation of proportional hazards (PH) model with time-varying coefficients.
Usage
npcox(cva, delta, obstime, SE = FALSE, bandwidth = FALSE, resamp = 100)
Arguments
cva |
Covariate Z in h(t) = h0(t)exp(b(t)'Z) |
delta |
Right censoring indicator for the model |
obstime |
The observed time = min(censoring time, observed failure time) |
SE |
Whether or not the estimation of standard error through resampling method will be done. The default value is FALSE. |
bandwidth |
Bandwidth for kernel function, which can be specified. The default value is FALSE and can be selected through least prediction error over all subjects. |
resamp |
Number of resampling for estimation of pointwise standard error. The default value is 100. |
Details
This is some description of this function.
'npcox' function is designed for PH model with time-varying coefficients, h(t) = h0(t)exp(b(t)'Z), providing estimation of b(t) and its pointwise standard errors on [bandwidth, max(obstime)-badwidth].
Value
a list that contain the estimation result of temporal coefficients, standard error estimation, selected or predesigned bandwidth, dataset, unconverged time points.
Examples
data(pbc)
pbc = pbc[(pbc$time < 3000) & (pbc$time > 800), ]
Z = pbc[,c("age","edema")]
colnames(Z) = c("age","edema")
del = pbc$status
tim = pbc$time
res = npcox(cva = Z,delta = del, obstime = tim, bandwidth = 500)