eha (version 1.2-18)

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 adap

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
• 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.
• call
  The call.
• bootstrap
  The bootstrap sample, if requested on input.
• sigma
  Present if a frailty model is fitted. Equals the estimated frailty standard deviation.
• sigma.sd
  The standard error of the estimated frailty standard deviation.
• method
  The method.
• convergence
  Did the optimization converge?
• fail
  Did the optimization fail? (Is NULL if not).

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

Brostr�m{Brostrom}, 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