Performs Cox regression with some special attractions, especially sampling of risksets and the weird bootstrap.
coxreg(formula = formula(data), data = parent.frame(), weights,
subset, t.offset, na.action = getOption("na.action"), init = NULL, method =
c("efron", "breslow", "mppl", "ml"), control = list(eps = 1e-08, maxiter =
25, trace = FALSE), singular.ok = TRUE, model = FALSE, center = NULL, x =
FALSE, y = TRUE, hazards = NULL, boot = FALSE, efrac = 0, geometric = FALSE,
rs = NULL, frailty = NULL, max.survs = NULL, coxph = TRUE)A list of class c("coxreg", "coxph") with components
Fitted parameter estimates.
Covariance matrix of the estimates.
Vector of length two; first component is the value at the initial parameter values, the second component is the maximized value.
The score test statistic (at the initial value).
The estimated linear predictors.
The martingale residuals.
The estimated baseline hazards, calculated at the value zero of the covariates (rather, columns of the design matrix). Is a list, with one component per stratum. Each component is a matrix with two columns, the first contains risk times, the second the corresponding hazard atom.
Means of the columns of
the design matrix corresponding to covariates, if center = TRUE.
Columns corresponding to factor levels gice a zero in the corresponding
position in means. If center = FALSE, means are all
zero.
Weighted (against exposure time) means of covariates; weighted relative frequencies of levels of factors.
Number of spells in indata (possibly after removal of cases with NA's).
Number of events in data.
Used by extractor functions.
Used by extractor functions.
The Surv vector.
Logical vector indicating the covariates that are factors.
The covariates.
Total Time at Risk.
List of levels of factors.
The calling formula.
The (matrix of) bootstrap replicates, if requested on input. It is up to the user to do whatever desirable with this sample.
The call.
The method.
Number of strata.
Did the optimization converge?
Did the optimization fail? (Is NULL if not).
a formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object as returned by the Surv function.
a data.frame in which to interpret the variables named in the formula.
Case weights; time-fixed or time-varying.
An optional vector specifying a subset of observations to be used in the fitting process.
Case offsets; time-varying.
a missing-data filter function, applied to the model.frame,
after any subset argument has been used. Default is
options()$na.action.
vector of initial values of the iteration. Default initial value is zero for all variables.
Method of treating ties, "efron" (default), "breslow", "mppl" (maximum partial partial likelihood), or "ml" (maximum likelihood).
a list with components eps (convergence criterion),
maxiter (maximum number of iterations), and silent (logical,
controlling amount of output). You can change any component without mention
the other(s).
Not used
Not used
deprecated. See Details.
Return the design matrix in the model object?
return the response in the model object?
deprecated. Was: Calculate baseline hazards? Default is TRUE. Calculating hazards is better done separately, after fitting. In most cases.
Number of boot replicates. Defaults to FALSE, no boot samples.
Upper limit of fraction failures in 'mppl'.
If TRUE, forces an 'ml' model with constant riskset probability. Default is FALSE.
Risk set?
Grouping variable for frailty analysis. Not in use (yet).
Sampling of risk sets? If given, it should be (the upper limit of) the number of survivors in each risk set.
Logical, defaults to TRUE. Determines if standard work
should be passed to coxph via entry points.
The use of rs is dangerous, see note. It can
however speed up computing time considerably for huge data sets.
Göran Broström
The default method, efron, and the alternative, breslow, are
both the same as in coxph in package
survival. The methods mppl and ml are maximum
likelihood, discrete-model, based.
Broström, G. and Lindkvist, M. (2008). Partial partial likelihood. Communications in Statistics: Simulation and Computation 37:4, 679-686.
coxph, risksets
dat <- data.frame(time= c(4, 3,1,1,2,2,3),
status=c(1,1,1,0,1,1,0),
x= c(0, 2,1,1,1,0,0),
sex= c(0, 0,0,0,1,1,1))
coxreg( Surv(time, status) ~ x + strata(sex), data = dat) #stratified model
# Same as:
rs <- risksets(Surv(dat$time, dat$status), strata = dat$sex)
coxreg( Surv(time, status) ~ x, data = dat, rs = rs) #stratified model
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