Description Usage Arguments Details Value References See Also Examples

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

function.

1 2 3 4 | ```
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)
``` |

`x` |
vector of quantiles.
Missing values ( |

`q` |
vector of quantiles.
Missing values ( |

`p` |
vector of probabilities.
Missing values ( |

`n` |
number of random deviates to produce |

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

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

`distribution` |
character string giving the name of the distribution. This must be one
of the elements of |

`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 `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).

density (`dsurvreg`

),
probability (`psurvreg`

),
quantile (`qsurvreg`

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

and
`sd`

.

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
# 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)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.