# dsurvreg

##### Distributions available in survreg.

Density, cumulative distribution function, quantile function and random
generation for the set of distributions
supported by the `survreg`

function.

- Keywords
- distribution

##### Usage

```
dsurvreg(x, mean, scale=1, distribution='weibull', parms)
psurvreg(q, mean, scale=1, distribution='weibull', parms)
qsurvreg(p, mean, scale=1, distribution='weibull', parms)
rsurvreg(n, mean, scale=1, distribution='weibull', parms)
```

##### Arguments

- x
vector of quantiles. Missing values (

`NA`

s) are allowed.- q
vector of quantiles. Missing values (

`NA`

s) are allowed.- p
vector of probabilities. Missing values (

`NA`

s) are allowed.- n
number of random deviates to produce

- mean
vector of linear predictors for the model. This is replicated to be the same length as

`p`

,`q`

or`n`

.- scale
vector of (positive) scale factors. This is replicated to be the same length as

`p`

,`q`

or`n`

.- 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.

##### Details

Elements of `q`

or
`p`

that are missing will cause the corresponding
elements of the result to be missing.

The `location`

and `scale`

values are as they would be for `survreg`

.
The label "mean" was an unfortunate choice (made in mimicry of qnorm);
since almost none of these distributions are symmetric it will not
actually be a mean, but corresponds instead to the linear predictor of
a fitted model.
Translation to the usual parameterization found in a textbook is not
always obvious.
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 using a standard parameterization in terms of
\(a\) and \(p\),
the survreg location corresponds to \(-\log(a)\) and the scale
to \(1/p\)
(Kalbfleisch and Prentice, section 2.2.2).

##### Value

density (`dsurvreg`

),
probability (`psurvreg`

),
quantile (`qsurvreg`

), or
for the requested distribution with mean and scale
parameters `mean`

and
`sd`

.

##### References

Kalbfleisch, J. D. and Prentice, R. L. (1970).
*The Statistical Analysis of Failure Time Data*
Wiley, New York.

##### See Also

##### Examples

```
# NOT RUN {
# List of distributions available
names(survreg.distributions)
# }
# NOT RUN {
[1] "extreme" "logistic" "gaussian" "weibull" "exponential"
[6] "rayleigh" "loggaussian" "lognormal" "loglogistic" "t"
# }
# 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)
# }
```

*Documentation reproduced from package survival, version 3.1-8, License: LGPL (>= 2)*