coxreg.fit: Cox regression

Description

Called by coxreg, but a user can call it directly.

Usage

coxreg.fit(X, Y, rs, weights, t.offset = NULL,
strats, offset, init, max.survs,
method = "breslow", center = TRUE,
boot = FALSE, efrac = 0, calc.hazards = TRUE,
calc.martres = TRUE, control, verbose = TRUE)

Arguments

The design matrix.

The survival object.

The risk set composition. If absent, calculated.

weights

Case weights; time-fixed or time-varying.

t.offset

Case offset; time-varying.

strats

The stratum variable. Can be absent.

offset

Offset. Can be absent.

init

Start values. If absent, equal to zero.

max.survs

Sampling of risk sets? If so, gives the maximum number of survivors in each risk set.

method

Either "efron" (default) or "breslow".

center

See coxreg.

boot

Number of bootstrap replicates. Defaults to FALSE, no bootstrapping.

efrac

Upper limit of fraction failures in 'mppl'.

calc.hazards

Should estimates of baseline hazards be calculated?

calc.martres

Should martingale residuals be calculated?

control

See coxreg

verbose

Should Warnings about convergence be printed?

Value

A list with components
coefficientsEstimated regression parameters.
varCovariance matrix of estimated coefficients.
loglikFirst component is value at init, second at maximum.
scoreScore test statistic, at initial value.
linear.predictorsLinear predictors.
residualsMartingale residuals.
hazardEstimated baseline hazard. At value zero of 'design' variables.
meansMeans of the columns of the design matrix.
bootstrapThe bootstrap replicates, if requested on input.
converTRUE if convergence.
f.converTRUE if variables converged.
failTRUE if failure.
iterNumber of performed iterations.

Details

rs is dangerous to use when NA's are present.

Examples

Run this code

X <- as.matrix(data.frame(
                x=     c(0, 2,1,4,1,0,3),
                sex=   c(1, 0,0,0,1,1,1)))
 time <- c(1,2,3,4,5,6,7)
 status <- c(1,1,1,0,1,1,0)
 stratum <- rep(1, length(time))

 coxreg.fit(X, Surv(time, status), strats = stratum, max.survs = 6,
     control = list(eps=1.e-4, maxiter = 10, trace = FALSE))