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 = TRUE,
x = FALSE, y = TRUE, hazards = TRUE, boot = FALSE, efrac = 0,
geometric = FALSE, rs = NULL,
frailty = NULL, max.survs = NULL)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
Logical. If center = TRUE (default), the baseline
hazards are
calculated at the means of the
covariates and for the reference category for factors, otherwise at
the value zero. See Details.
Return the design matrix in the model object?
return the response in the model object?
Calculate baseline hazards? Default is TRUE.
Risk set?
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.
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.
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 componet is the maximized value.
The score test statistic (at the initial value).
The estimated linear predictors.
The martingale residuals.
The estimated baseline hazard, calculated at the means 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 risktimes, 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 estimated standard errors of the bootstrap replicates.
The call.
The method.
Did the optimization converge?
Did the optimization fail? (Is NULL if not).
The use of rs is dangerous, see note. It
can however speed up computing time considerably for huge data sets.
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.
If center = TRUE (default), graphs show the "baseline"
distribution at the
means of (continuous) covariates, and for the reference category in case
of factors (avoiding representing "flying pigs"). If center = FALSE
the baseline distribution is at the value zero of all covariates. It is
usually a good idea to use center = FALSE in combination with
"precentering" of covariates, that is, subtracting a reference value,
ideally close to the center of the covariate distribution. In that way,
the "reference" will be the same for all subsets of the data.
Brostr<U+00F6>m, G. and Lindkvist, M. (2008). Partial partial likelihood. Communications in Statistics: Simulation and Computation 37:4, 679-686.
# NOT RUN {
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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