mle.vam: Virtual age model for maximum likelihood estimation

In rcqls/VAM: Virtual Age Models

Virtual age model for maximum likelihood estimation

Description

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

Usage

mle.vam(formula, data)

Arguments

formula	a symbolic description of the virtual age model and observations, or a mle.vam class object for which the estimation method has been launched at least one time. When formula is mle.vam object, the model considered corresponds to the plug in estimator, that is to say the output of the formula.mle.vam 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.

Details

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.

Value

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

Author(s)

L. Doyen and R. Drouilhet

See Also

run.mle.vam to compute the MLE. coef.mle.vam to extract the parameters value of the MLE. formula.mle.vam to extract the original and estimated model. plot.mle.vam for plotting characteristics of the model. update.mle.vam to change the associated data set. contrast.mle.vam to compute the contrast associated to the MLE. logLik.mle.vam to compute the log-likelihood.

Examples

simARAInf<-sim.vam(  ~ (ARAInf(.4) | Weibull(.001,2.5)))
simData<-simulate(simARAInf,30)
mleARAInf <- mle.vam(Time & Type ~ (ARAInf(.5) | Weibull(1,3)),data=simData)
coef(mleARAInf)

simCMPM_Multi<-sim.vam(  ~ (ARAInf(.3) | Weibull(.001,2.5)) & (ARAInf(.6)+ARAInf(-.2) | Periodic(12,prob=c(0.6,0.4))))
simData_Multi<-simulate(simCMPM_Multi,5000,nb.system=5)
mleCMPM_Multi <- mle.vam(System & Time & Type ~  (ARAInf(.5) | Weibull(1,3)) & (ARAInf(.5)+ARAInf(.5)),data=simData_Multi)
coef(mleCMPM_Multi)

rcqls/VAM documentation built on Jan. 14, 2024, 9:07 p.m.