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Regression for a parametric survival model

Usage

survreg(formula, data=sys.parent(), subset, na.action,
link=c("log", "identity"),
dist=c("extreme", "logistic", "gaussian", "exponential"),
fixed, eps=0.0001, init, iter.max=10, model=F, x=F, y=F, ...)

Arguments

formula a formula expression as for other regression models. See the documentation for lm and formula for details.
data optional data frame in which to interpret the variables occuring in the formula.
subset subset of the observations to be used in the fit.
na.action function to be used to handle any NAs in the data.
link transformation to be used on the y variable.
dist assumed distribution for the transformed y variable.
fixed a list of fixed parameters, most often just the scale. (When I implement the t-dist, it will include the degrees of freedom).
eps convergence criteria for the computation. Iteration continues until the relative change in log likelihood is less than eps.
init optional vector of initial values for the paramters.
iter.max maximum number of iterations to be performed.
model if TRUE, the model frame is returned.
x if TRUE, then the X matrix is returned.
y if TRUE, then the y vector (or survival times) is returned.
... all the optional arguments to lm, including singular.ok.

Value

an object of class survreg is returned, which inherits from class glm.

Computation

This routine is not as robust against nearly singular X matrices as lm(); the problem occurs when we explicitly invert the covariance matrix with solve(). This can sometimes be solved by subtracting the mean from all continuous covariates.

Examples

survreg(Surv(futime, fustat) ~ ecog.ps + rx, fleming, dist='extreme',
		link='log', fixed=list(scale=1))   #Fit an exponential