aftreg {eha} | R Documentation |
The accelerated failure time model with parametric baseline hazard(s). Allows for stratification with different scale and shape in each stratum, and left truncated and right censored data.
aftreg(formula = formula(data), data = parent.frame(), na.action = getOption("na.action"), dist = "weibull", init, shape = 0, id, param = c("lifeAcc", "lifeExp"), control = list(eps = 1e-08, maxiter = 20, trace = FALSE), singular.ok = TRUE, model = FALSE, x = FALSE, y = TRUE)
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
|
dist |
Which distribution? Default is "weibull", with the alternatives
"gompertz", "ev", "loglogistic" and "lognormal". A special case like the
|
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. If
zero or negative, the shape parameter is estimated. Stratification is now
regarded as a meaningful option even if |
id |
If there are more than one spell per individual, it is essential to keep spells together by the id argument. This allows for time-varying covariates. |
param |
Which parametrization should be used? The |
control |
a list with components |
singular.ok |
Not used. |
model |
Not used. |
x |
Return the design matrix in the model object? |
y |
Return the response in the model object? |
The parameterization is different from the one used by
survreg
, when param = "lifeAcc"
. The result
is then true acceleration of time. Then the model is
S(t; a, b, β, z) = S_0((t / \exp(b - zβ))^{\exp(a)})
S(t; a, b, beta, z) = S_0((t/exp(b - z beta))^exp(a))
where S_0 is some standardized
survivor function. The baseline parameters a and b are log shape
and log scale, respectively. This is for the default
parametrization.
With the lifeExp
parametrization, some signs are changed:
b - z beta
is changed to
b + z beta
. For the Gompertz distribution, the
base parametrization is canonical
, a necessity for consistency with
the shape/scale paradigm (this is new in 2.3).
A list of class c("aftreg", "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. |
wald.test |
The Wald 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. |
method |
The method. |
convergence |
Did the optimization converge? |
fail |
Did the optimization fail? (Is |
pfixed |
TRUE if shape was fixed in the estimation. |
param |
The parametrization. |
Göran Broström
data(mort) aftreg(Surv(enter, exit, event) ~ ses, param = "lifeExp", data = mort)