contrast.mle.vam | R Documentation |
contrast.mle.vam
computes the contrast corresponding to the maximum likelihood estimation method for a virtual age model with Corrective Maintenance (CM) and planned Preventive Maintenance (PM).
The difference between the log-likelihood logLik.mle.vam
and the contrast is due to the parameter
\alpha
which represents the scale parameter of the time to failure distribution of the new unmaintained system, h(t)
(see sim.vam
for more details). In fact, the value of \alpha
that maximizes the log-likelihood has a closed-form solution, function of the others parameters values. The contrast corresponds to the value of the log-likelihood evaluated at this particular value of \alpha
. Consequently, the contrast does not depend on \alpha
, that is to say of par0[1]
.
contrast.mle.vam(obj,par0,with_value=TRUE,with_gradient=FALSE,with_hessian=FALSE)
obj |
an object of class |
par0 |
an optional argument specifying the parameter values at which the contrast is computed.
If |
with_value |
a logical which indicates if the value of the contrast has to be computed. |
with_gradient |
a logical which indicates if the gradient of the contrast has to be computed. |
with_hessian |
a logical which indicates if the hessian of the contrast has to be computed. |
If only with_value
is TRUE
, the method produces the contrast value.
If only with_gradient
is TRUE
, the method produces a vector corresponding to the gradient of the contrast,
If only with_hessian
is TRUE
, the method produces a matrix corresponding to the hessian of the contrast.
Otherwise, the method produces a list of the contrast characteristics for which the corresponding argument is TRUE
.
L. Doyen and R. Drouilhet
run.mle.vam
to compute the MLE.
coef.mle.vam
to extract the parameters value of the MLE.
logLik.mle.vam
to compute the log-likelihood.
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.
simARAInf<-sim.vam( ~ (ARAInf(.4) | Weibull(.001,2.5)))
simData<-simulate(simARAInf,30)
mleARAInf <- mle.vam(Time & Type ~ (ARAInf(0.5) | Weibull(1,3)),data=simData)
Est<-coef(mleARAInf)
contrast(mleARAInf)
contrast(mleARAInf,Est,c(TRUE,TRUE,TRUE))
logLik(mleARAInf,Est,c(TRUE,TRUE,TRUE))
require(rgl)
rhos<-seq(0,1,0.1)
betas<-seq(0.1,6,0.1)
lnL<-c()
for (rho in rhos){
for (beta in betas)
{
lnL<-c(lnL,contrast(mleARAInf,c(1,beta,rho)))
}
}
if(require(rgl)) {
lnL<-matrix(data=lnL,nrow=length(rhos),ncol=length(betas),byrow=TRUE)
persp3d(rhos, betas, lnL, col = 'skyblue',zlim=c(-100,max(lnL)))
grid3d(c("rho", "b", "lnL"))
spheres3d(Est[3],Est[2],contrast(mleARAInf,c(Est[1],Est[2],Est[3])),r=0.4,alpha=0.5,color="red",add=TRUE)
indMax<-which(lnL==max(lnL), arr.ind = TRUE)
spheres3d(rhos[indMax[1]],betas[indMax[2]],contrast(mleARAInf,c(1,betas[indMax[2]],rhos[indMax[1]])),r=0.3,color="black",add=TRUE)
}
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