coxreg {eha} | R Documentation |
Performs Cox regression with some special attractions, especially sampling of risksets and the weird bootstrap.
coxreg(formula = formula(data), data = parent.frame(), 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, boot = FALSE, efrac = 0, geometric = FALSE, rs = NULL, frailty = NULL, max.survs = NULL)
formula |
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. |
data |
a data.frame in which to interpret the variables named in the formula. |
na.action |
a missing-data filter function, applied to the
model.frame, after any subset argument has been used.
Default is options()$na.action . |
init |
vector of initial values of the iteration. Default initial value is zero for all variables. |
method |
Method of treating ties, "efron" (default), "breslow", "mppl" (maximum partial partial likelihood), or "ml" (maximum likelihood). |
control |
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). |
singular.ok |
Not used |
model |
Not used |
center |
If TRUE, the hazards are calculated at the means of the covariates. If FALSE, at zero. |
x |
Return the design matrix in the model object? |
y |
return the response in the model object? |
rs |
Risk set? |
boot |
Number of boot replicates. Defaults to FALSE, no boot samples. |
efrac |
Upper limit of fraction failures in 'mppl'. |
geometric |
If TRUE, forces an 'ml' model with constant riskset probability. Default is FALSE. |
frailty |
Grouping variable for frailty analysis. Not in use yet. |
max.survs |
Sampling of risk sets? If given, it should be (the upper limit of) the number of survivors in each risk set. |
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 based.
A list of class c("coxreg", "coxph")
with components
coefficients |
Fitted parameter estimates. |
var |
Covariance matrix of the estimates. |
loglik |
Vector of length two; first component is the value at the initial parameter values, the second componet is the maximized value. |
score |
The score test statistic (at the initial value). |
linear.predictors |
The estimated linear predictors. |
residuals |
The martingale residuals. |
hazard |
The estimated baseline hazard. |
means |
Means of the columns of the design matrix. |
w.means |
Weighted (against exposure time) means of covariates; weighted relative frequencies of levels of factors. |
n |
Number of spells in indata (possibly after removal of cases with NA's). |
events |
Number of events in data. |
terms |
Used by extractor functions. |
assign |
Used by extractor functions. |
wald.test |
The Walt test statistic (at the initial value). |
y |
The Surv vector. |
isF |
Logical vector indicating the covariates that are factors. |
covars |
The covariates. |
ttr |
Total Time at Risk. |
levels |
List of levels of factors. |
formula |
The calling formula. |
bootstrap |
The (matrix of) bootstrap replicates, if requested on input. It is up to the user to do whatever desirable with this sample. |
boot.sd |
The estimated standard errors of the bootstrap replicates. |
call |
The call. |
method |
The method. |
convergence |
Did the optimization converge? |
fail |
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.
This function starts by creating risksets, if no riskset is
supplied via rs
, with the aid of
risksets
. Supplying output from risksets
via rs
fails if there are any NA's in the data! Note also
that it depends on stratification, so rs
contains information
about stratification. Giving another strata variable in the formula is
an error. The same is ok, for instance to supply stratum
interactions.
Göran Broström
Broström, G. and Lindkvist, M. (2008). Partial partial likelihood. Communications in Statistics: Simulation and Computation 37:4, 679-686.
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