splineCox.reg2 {splineCox} | R Documentation |
Fitting the five-parameter spline Cox model with a specified shape, selecting the best fit
Description
splineCox.reg2
estimates the parameters of a five-parameter spline Cox model for multiple specified shapes
and selects the best fitting model based on the minimization of the log-likelihood function.
The function calculates the estimates for the model parameters (beta) and the baseline hazard scale parameter (gamma), using non-linear optimization.
Usage
splineCox.reg2(
t.event,
event,
Z,
xi1 = min(t.event),
xi3 = max(t.event),
model = names(shape.list),
p0 = rep(0, 1 + ncol(as.matrix(Z)))
)
Arguments
t.event |
a vector for time-to-event |
event |
a vector for event indicator (=1 event; =0 censoring) |
Z |
a matrix for covariates; nrow(Z)=sample size, ncol(Z)=the number of covariates |
xi1 |
lower bound for the hazard function; the default is min(t.event) |
xi3 |
upper bound for the hazard function; the default is max(t.event) |
model |
A character vector specifying which model shapes to consider for the baseline hazard.
Available options are:
"increase", "constant", "decrease", "unimodal1", "unimodal2", "unimodal3", "bathtub1", "bathtub2", "bathtub3".
Default is |
p0 |
Initial values to maximize the likelihood (1 + p parameters; baseline hazard scale parameter and p regression coefficients) |
Value
A list containing the following components:
model |
A character string indicating the shape of the baseline hazard function used. |
parameter |
A numeric vector of the parameters defining the baseline hazard shape. |
beta |
A named vector with the estimates, standard errors, and 95% confidence intervals for the regression coefficients |
gamma |
A named vector with the estimate, standard error, and 95% confidence interval for the baseline hazard parameter |
loglik |
A named vector containing the log-likelihood ( |
other_models |
A data frame containing the log-likelihood ( |
Examples
# Example data
library(joint.Cox)
data(dataOvarian)
t.event = dataOvarian$t.event
event = dataOvarian$event
Z = dataOvarian$CXCL12
M = c("constant", "increase", "decrease")
reg2 <- splineCox.reg2(t.event, event, Z, model = M)
print(reg2)