Proportional hazards model with baseline hazard(s) from the Weibull family of distributions. Allows for stratification with different scale and shape in each stratum, and left truncated and right censored data.
weibreg(
formula = formula(data),
data = parent.frame(),
na.action = getOption("na.action"),
init,
shape = 0,
control = list(eps = 1e-04, maxiter = 10, trace = FALSE),
singular.ok = TRUE,
model = FALSE,
x = FALSE,
y = TRUE,
center = TRUE
)A list of class c("weibreg", "coxreg") with components
Fitted parameter estimates.
Covariance matrix of the estimates.
Vector of length two; first component is the value at the initial parameter values, the second componet is the maximized value.
The score test statistic (at the initial value).
The estimated linear predictors.
Means of the columns of the design matrix.
Weighted (against exposure time) means of covariates; weighted relative frequencies of levels of factors.
Number of spells in indata (possibly after removal of cases with NA's).
Number of events in data.
Used by extractor functions.
Used by extractor functions.
The Wald test statistic (at the initial value).
The Surv vector.
Logical vector indicating the covariates that are factors.
The covariates.
Total Time at Risk.
List of levels of factors.
The calling formula.
The call.
The method.
Did the optimization converge?
Did the optimization fail? (Is NULL if not).
TRUE if shape was fixed in the estimation.
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.
a data.frame in which to interpret the variables named in the formula.
a missing-data filter function, applied to the model.frame,
after any subset argument has been used. Default is
options()$na.action.
vector of initial values of the iteration. Default initial value is zero for all variables.
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.
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).
Not used.
Not used.
Return the design matrix in the model object?
Return the response in the model object?
Deprecated, and not used. Will be removed in the future.
The print method print.weibreg doesn't work
if threeway or higher order interactions are present.
Note further that covariates are internally centered, if center =
TRUE, by this function, and this is not corrected for in the output. This
affects the estimate of \(\log(scale)\), but nothing else. If
you don't like this, set center = FALSE.
Göran Broström
The parameterization is the same as in coxreg and
coxph, but different from the one used by
survreg. The model is $$h(t; a, b, \beta, z) =
(a/b) (t/b)^{a-1} exp(z\beta)$$ This is in correspondence with Weibull. To
compare regression coefficients with those from survreg you need to
divide by estimated shape (\(\hat{a}\)) and change sign. The p-values
and test statistics are however the same, with one exception; the score test
is done at maximized scale and shape in weibreg.
This model is a Weibull distribution with shape parameter \(a\) and scale parameter \(b \exp(-z\beta / a)\)
phreg, coxreg,
print.weibreg
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))
weibreg( Surv(time, status) ~ x + strata(sex), data = dat) #stratified model
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