fsai11Precond.GEevalOnThetaGrid: Virtual age model for Bayesian estimation
In didiergirard/CGEMEV: Conditional Gibbs Energy Mean - Empirical Variance

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/fsai11Precond.GEevalOnThetaGridfinalFinal.R

fsai11Precond.GEevalOnThetaGrid is used to define a virtual age model for Corrective Maintenance (CM) and planned Preventive Maintenance (PM). The object define with fsai11Precond.GEevalOnThetaGrid can be used to compute the Maximum Likelihood Estimator (MLE) of the parameters thanks to the run.fsai11Precond.GEevalOnThetaGrid method.

1	fsai11Precond.GEevalOnThetaGrid(formula, data)

formula

a symbolic description of the virtual age model and observations, or a fsai11Precond.GEevalOnThetaGrid class object for which the estimation method has been launched at least one time. When formula is fsai11Precond.GEevalOnThetaGrid object, the model considered corresponds to the plug in estimator, that is to say the output of the formula.fsai11Precond.GEevalOnThetaGrid function. Otherwise, the details of formula specifications are given under ‘Details’.

data

a data frame or possibly a list (when several system are considered together) containing the observations.

The symbolic description of the model done in formula has the form response ~ model. response is a symbolic description of the data considered. The specifications are the same as those of model.vam function. model is a symbolic description of the virtual age model considered. The specifications are the same as those of sim.vam function. In this case the PM policy is useless, so it has not to be necessarily defined. The parameter values specify in model for the maintenance effect models, and the time to failure distribution of the new unmaintained system, are used as initialization values for the first run of the likelihood maximization method.

The function produces an object of class fsai11Precond.GEevalOnThetaGrid which contains the virtual age model considered and the corresponding observations.

R. Drouilhet

run.fsai11Precond.GEevalOnThetaGrid to compute the Bayesian method. coef.fsai11Precond.GEevalOnThetaGrid to extract the parameters value of the Bayesian method.

simARAInf<-sim.vam(  ~ (ARAInf(.4) | Weibull(.001,2.5)))
simData<-simulate(simARAInf,30)
bayesARAInf <- fsai11Precond.GEevalOnThetaGrid(Time & Type ~ (ARAInf(~Unif(0,1)) | Weibull(~Unif(1,1.5),~Unif(2,4))),data=simData)
coef(bayesARAInf)