eha (version 1.4)

phreg: Parametric Proportional Hazards Regression

Description

Proportional hazards model with parametric baseline hazard(s). Allows for stratification with different scale and shape in each stratum, and left truncated and right censored data.

Usage

phreg(formula = formula(data), data = parent.frame(),
na.action = getOption("na.action"), dist = "weibull", init, shape = 0,
control = list(eps = 1e-08, maxiter = 20, trace = FALSE),
singular.ok = TRUE, model = FALSE, x = FALSE, y = TRUE, center = 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.

dist

Which distribution? Default is "weibull", with the alternatives "loglogistic" and "lognormal". A special case like the exponential can be obtained by choosing "weibull" in combination with shape = 1.

init

vector of initial values of the iteration. Default initial value is zero for all variables.

shape

If positive, the shape parameter is fixed at that value (in each stratum). If zero or negative, the shape parameter is estimated. If more than one stratum is present in data, each stratum gets its own estimate.

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.

x

Return the design matrix in the model object?

y

Return the response in the model object?

center

Now deprecated and not used. Baseline parameters are now always calculated at the means of the columns of the design matrix.

Value

• A list of class c("phreg", "coxreg") 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.
• 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.
• 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.
• method
  The method.
• convergence
  Did the optimization converge?
• fail
  Did the optimization fail? (Is NULL if not).
• pfixed
  TRUE if shape was fixed in the estimation.

Warning

Note that covariates are internally centered by this function, and this is not corrected for in the output. This affects the estimate of $\log(scale)$, but nothing else.

Details

The parameterization is the same as in coxreg and coxph, but different from the one used by survreg. The model is $$S(t; a, b, \beta, z) = S_0((t/b)^a)^{\exp((z-mean(z))\beta)}$$ where S0 is some standardized survivor function.

See Also

coxreg, check.dist, link{aftreg}.

Examples

data(mort)
fit <- phreg(Surv(enter, exit, event) ~ ses, data = mort)
fit
plot(fit)
fit.cr <- coxreg(Surv(enter, exit, event) ~ ses, data = mort)
check.dist(fit.cr, fit)