Survreg | R Documentation |
Weibull, log-normal, log-logistic and other parametric models (not exclusively) for survival analysis
Survreg(formula, data, subset, weights, offset, cluster, na.action = na.omit,
dist = c("weibull", "logistic", "gaussian", "exponential", "rayleigh",
"loggaussian", "lognormal", "loglogistic"), scale = 0, ...)
formula |
an object of class |
data |
an optional data frame, list or environment (or object
coercible by |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
weights |
an optional vector of weights to be used in the fitting
process. Should be |
offset |
this can be used to specify an _a priori_ known component to
be included in the linear predictor during fitting. This
should be |
cluster |
optional factor with a cluster ID employed for computing clustered covariances. |
na.action |
a function which indicates what should happen when the data
contain |
dist |
character defining the conditional distribution of the (not necessarily positive) response, current choices include Weibull, logistic, normal, exponential, Rayleigh, log-normal (same as log-gaussian), or log-logistic. |
scale |
a fixed value for the scale parameter(s). |
... |
additional arguments to |
Parametric survival models reusing the interface of
survreg
. The parameterisation is, however, a little
different, see the package vignette.
The model is defined with a negative shift term. Large values of the linear predictor correspond to large values of the conditional expectation response (but this relationship is nonlinear). Parameters are log-hazard ratios comparing a reference with treatment (or a one unit increase in a numeric variable).
An object of class Survreg
, with corresponding coef
,
vcov
, logLik
, estfun
, summary
,
print
, plot
and predict
methods.
Torsten Hothorn, Lisa Moest, Peter Buehlmann (2018), Most Likely Transformations, Scandinavian Journal of Statistics, 45(1), 110–134, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/sjos.12291")}.
data("GBSG2", package = "TH.data")
library("survival")
survreg(Surv(time, cens) ~ horTh, data = GBSG2)
Survreg(Surv(time, cens) ~ horTh, data = GBSG2)
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