residuals.survstan

residuals method for survstan models

Parametric survival regression models under the maximum likelihood approach via 'Stan'. Implemented regression models include accelerated failure time models, proportional hazards models, proportional odds models, accelerated hazard models, Yang and Prentice models, and extended hazard models. Available baseline survival distributions include exponential, Weibull, log-normal, log-logistic, gamma, generalized gamma, rayleigh, Gompertz and fatigue (Birnbaum-Saunders) distributions. References: Lawless (2002) <ISBN:9780471372158>; Bennett (1982) <doi:10.1002/sim.4780020223>; Chen and Wang(2000) <doi:10.1080/01621459.2000.10474236>; Demarqui and Mayrink (2021) <doi:10.1214/20-BJPS471>.

Fabio Demarqui

survstan

Fitting Survival Regression Models via 'Stan'

Andrew Johnson

residuals.survstan function

<dl><dt>object</dt>
<dd>a fitted model object of the class survstan.</dd>
<dt>type</dt>
<dd>type of residuals desired: coxsnell (default), martingale and deviance.</dd>
<dt>...</dt>
<dd>further arguments passed to or from other methods.</dd></dl>

Arguments

residuals method for survstan models — residuals.survstan

<dl>

<dt>object</dt>
<dd>a fitted model object of the class survstan.</dd>


<dt>type</dt>
<dd>type of residuals desired: coxsnell (default), martingale and deviance.</dd>


<dt>...</dt>
<dd>further arguments passed to or from other methods.</dd>

</dl>

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residuals.survstan: residuals method for survstan models

Description

Usage

Value

Arguments

Details

Examples