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