This is the output from `dic.fit()`

, which contains
the important bits of information about the model fit and
key options used.

`ests`

:Matrix of class

`"numeric"`

. This matrix summarizes the results of fitting the model. Rows correspond to the first parameter , the second parameter and then percentiles specified by the ptiles argument. Columns correspond to the point estimate, the lower and upper bounds on the 95% confidence interval and the standard error of the point estimate. If the maximization does not converge, this matrix is filled with NAs.`conv`

:Object of class

`"numeric"`

. A value of 1 indicates successful convergence; 0 indicates unsuccessful convergence.`MSG`

:Object of class

`"character"`

. The error message returned from`optim()`

if the routine fails to converge.`loglik`

:Object of class

`"numeric"`

. Value of the estimated maximum log-likelihood.`samples`

:Object of class

`"data.frame"`

. Data frame of bootstrap estimates of parameters (if bootstraps were performed).`data`

:Object of class

`"data.frame"`

. Original data used to fit model.`dist`

:Object of class

`"character"`

. Failure time distribution fit to data. "L" for log-normal, "G" for gamma, "W" for Weibull, and "E" for Erlang.`inv.hessian`

:Object of class

`"matrix"`

. The inverse of the hessian matrix for the likelihood surface at the MLE. Used to determine the standard errors for the percentiles. Note that optimization is done on a transformed scale with all parameters logged for all distributions except the first parameter of the log-normal distribution.`est.method`

:Object of class

`"character"`

. Method used for estimation.`ci.method`

:Object of class

`"character"`

. Method used for estimation of confidence/credible intervals.

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