| dsurvreg {survival} | R Documentation |
Density, cumulative probability, and quantiles for the set of distributions
supported by the survreg function.
dsurvreg(x, mean, scale=1, distribution='weibull', parms) psurvreg(q, mean, scale=1, distribution='weibull', parms) qsurvreg(p, mean, scale=1, distribution='weibull', parms)
x |
vector of quantiles.
Missing values (NAs) are allowed.
|
q |
vector of quantiles.
Missing values (NAs) are allowed.
|
p |
vector of probabilities.
Missing values (NAs) are allowed.
|
mean |
vector of means.
This is replicated to be the same length as p
or q.
|
scale |
vector of (positive) scale factors.
This is replicated to be the same length as p
or q.
|
distribution |
character string giving the name of the distribution. This must be one
of the elements of survreg.distributions
|
parms |
optional parameters, if any, of the distribution. For the t-distribution this is the degrees of freedom. |
Elements of q or
p that are missing will cause the corresponding
elements of the result to be missing.
The mean and scale
values are as they would be for survreg.
In particular, if
the distribution is one that involves a transformation, then they are the
mean and scale of the transformed distribution.
For example, the Weibull distribution is fit using the
Extreme value distribution along with a log transformation.
Letting F(t) = 1 - exp(-(at)^p)
be the cumulative distribution of the
Weibull, the mean corresponds to -log(a) and the scale
to 1/p
(Kalbfleish and Prentice, section 2.2.2).
density (dsurvreg),
probability (psurvreg),
quantile (qsurvreg), or
for the requested distribution with mean and scale
parameters mean and
sd.
Kalbfleish, J. D. and Prentice, R. L. (1970). The Statistical Analysis of Failure Time Data Wiley, New York.
# List of distributions available names(survreg.distributions) ## Not run: [1] "extreme" "logistic" "gaussian" "weibull" "exponential" [6] "rayleigh" "loggaussian" "lognormal" "loglogistic" "t" ## End(Not run) # Compare results all.equal(dsurvreg(1:10, 2, 5, dist='lognormal'), dlnorm(1:10, 2, 5)) # Hazard function for a Weibull distribution x <- seq(.1, 3, length=30) haz <- dsurvreg(x, 2, 3)/ (1-psurvreg(x, 2, 3)) ## Not run: plot(x, haz, log='xy', ylab="Hazard") #line with slope (1/scale -1) ## End(Not run)