# dsurvreg {survival}

### Description

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

function.

### 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 (Kalbfleish and Prentice, section 2.2.2).

### Values

density (`dsurvreg`

), probability (`psurvreg`

), quantile (`qsurvreg`

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

and `sd`

.

### References

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

### See Also

`survreg`

, `Normal`

### Examples

# 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)

Documentation reproduced from package survival, version 2.39-4. License: LGPL (>= 2)