Description Usage Arguments Value References See Also Examples

`dic.fit`

fits a parametric accelerated failure time
model to survival data. It was developed with the
application to estimating incubation periods of
infectious diseases in mind but is applicable to many
general problems. The data can be a mixture of doubly
interval-censored, single interval-censored or exact
observations from a single univariate distribution.
Currently, three distributions are supported: log-normal,
gamma, and Weibull. (The Erlang distribution is supported
in the `dic.fit.mcmc`

function, which implements an
MCMC version of this code.) We use a consistent (par1,
par2) notation for each distribution, they map in the
following manner:

*Log-normal(meanlog=par1,
sdlog=par2)*

*Gamma(shape=par1, scale=par2)*

*Weibull(shape=par1, scale=par2)*

Standard errors of
parameters can be computed using closed-form asymptotic
formulae or using a bootstrap routine for log-normal and
gamma models. Currently, bootstrap SEs are the only
option for the gamma models, which do not have a closed
form for the percentiles. `dic.fit()`

calculates
asymptotic SEs by default, or whenever the `n.boots`

option is set to 0. To compute bootstrap SEs, just set
`n.boots`

to be greater than zero.
`dic.fit.mcmc`

also allows for Markov Chain
Monte Carlo fitting of these three parametric models and
Erlang models as well.

1 2 3 4 5 6 |

`dat` |
a matrix with columns named "EL", "ER", "SL", "SR", corresponding to the left (L) and right (R) endpoints of the windows of possible exposure (E) and symptom onset (S). Also, a "type" column must be specified and have entries with 0, 1, or 2, corresponding to doubly interval-censored, single interval-censored or exact observations, respsectively. |

`start.par2` |
starting value for 2nd parameter of desired distribtution |

`opt.method` |
method used by optim |

`par1.int` |
the log-scale interval of possible median values (in the same units as the observations in dat). Narrowing this interval can help speed up convergence of the algorithm, but care must be taken so that possible values are not excluded or that the maximization does not return a value at an endpoint of this interval. |

`par2.int` |
the log-scale interval of possible dispersion values |

`ptiles` |
percentiles of interest |

`dist` |
what distribution to use to fit the data. Default "L" for log-normal. "G" for gamma, and "W" for Weibull. |

`n.boots` |
number of bootstrap resamples (0 means that asymptotic results are desired) |

`...` |
additional options passed to optim |

a cd.fit S4 object.

Reich NG et al. Statistics in Medicine. Estimating incubation periods with coarse data. 2009. http://www3.interscience.wiley.com/journal/122507367/abstract

1 2 | ```
data(fluA.inc.per)
dic.fit(fluA.inc.per, dist="L")
``` |

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