eha (version 2.4-4)

mlreg: ML proportional hazards regression

Description

Maximum Likelihood estimation of proportional hazards models. Is deprecated, use coxreg instead.

Usage

mlreg(formula = formula(data), data = parent.frame(),
na.action = getOption("na.action"), init=NULL, method = c("ML", "MPPL"),
control = list(eps = 1e-08, maxiter = 10, n.points = 12, trace = FALSE),
singular.ok = TRUE, model = FALSE, center = TRUE,
x = FALSE, y = TRUE, boot = FALSE, geometric = FALSE,
rs=NULL, frailty = NULL, max.survs=NULL)

Arguments

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, "ML", the default, means pure maximum likelihood, i.e, data are treated as discrete. The choice "MPPL" implies that risk sets with no tied events are treated as in ordinary Cox regression. This is a cameleont that adapts to data, part discrete and part continuous.

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

Should covariates be centered? Default is TRUE

x

Return the design matrix in the model object?

y

return the response in the model object?

boot

No. of bootstrap replicates. Defaults to FALSE, i.e., no bootstrapping.

geometric

If TRUE, the intensity is assumed constant within strata.

rs

Risk set? If present, speeds up calculations considerably.

frailty

A grouping variable for frailty analysis. Full name is needed.

max.survs

Sampling of risk sets?

Value

A list of class c("mlreg", "coxreg", "coxph") with components

Warning

The use of rs is dangerous, see note above. It can however speed up computing time.

Details

Method ML performs a true discrete analysis, i.e., one parameter per observed event time. Method MPPL is a compromize between the discrete and continuous time approaches; one parameter per observed event time with multiple events. With no ties in data, an ordinary Cox regression (as with coxreg) is performed.

References

Broström, G. (2002). Cox regression; Ties without tears. Communications in Statistics: Theory and Methods 31, 285--297.

See Also

coxreg, risksets

Examples

 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))
 mlreg( Surv(time, status) ~ x + strata(sex), data = dat) #stratified model
 # Same as:
 rs <- risksets(Surv(dat$time, dat$status), strata = dat$sex)
 mlreg( Surv(time, status) ~ x, data = dat, rs = rs) #stratified model