fc_fit {failCompare}R Documentation

Fitting one or a set of failure time models

Description

Routines for fitting a common failure time model or models

Usage

fc_fit(time, model, SEs = TRUE, censorID = NULL, rc.value = NULL, ...)

Arguments

time

numeric vector of failure times

model

character string specififying the model(s) to be fit

SEs

logical for whether standard errors should be estimated

censorID

binary or logical variable the same length as time indicating censored observations, with zeros or FALSE indicating a cenosored observation

rc.value

rc.value right-censoring cutoff value (i.e.,only observations with times > rc.value are censored due to termination of the experiment or study)

...

additional arguments passed to optimizer

Details

This is a model fitting routine used to fit one or a set of failure time models:

Details on the parameterization of these distributions can be found in the appendix of the failCompare user manual . If a single model is specified, a "fc_obj" is created, which can be used to adjust a CJS model in the "cbrATLAS" package.

If multiple models are specified, a "fc_list" is created containing output from all models that could be fit with default optimizer settings. A warning will appear if any of the models could not be fit, in which case the user should either remove the model from consideration or specifiy initial parameter values.

Objects of class fc_list may serve as an input in the fc_rank() function, which ranks the performance of the model using the Skalski and Whitlock (2020) GOF measure.

Printing a fc_obJ will display parameter estimates and accompanying standard errors, if available.

Each fc_obJ is a list of the following extractable objects:

Value

Returns failure model object of class "fc_obj" if one model specified OR a failure model list object of class "fc_list" if multiple models are specified.

References

Kaplan, E.L., and Meier, P. 1958. Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53(282):457-481.

Li, T., and Anderson, J.J. 2009. The vitality model: a way to understand population survival and demographic heterogeneity. Theoretical Population Biology 76(2):118-131.

Li, T., and Anderson, J.J. 2013. Shaping human mortality patterns through intrinsic and extrinsic vitality processes. Demographic Research 28:341-372.

Prentice, R. L. 1974. A Log Gamma Model and Its Maximum Likelihood Estimation. Biometrika: 61(3):539-544.

Skalski, J. R., and S. L. Whitlock. 2020. Vitality models found useful in modeling tag-failure times in acoustic-tag survival studies. Animal Biotelemetry 8(1):1-10.DOI:10.1186/s40317-020-00213-z.


[Package failCompare version 1.0.0 Index]