lifedata.MLE: Parametric Fitting for Lifetime Data
In SPREDA: Statistical Package for Reliability Data Analysis

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

Fit parametric models (based on log-location-scale family of distributions) to right censored and/or left truncated data, with (dynamic) covariates.

1 2	Lifedata.MLE(formula, data, xt.dat = NULL, dist, method = "BFGS", subset, truncation, na.action, weights, ref_time = NULL, starts = NULL, ...)

`formula`	A formula object, which has a `Surv` object on the left of `~` and covariates on the right.
`data`	A data frame used to evaluate variables in the `formula`, `subset` and `weights` arguments.
`xt.dat`	A data frame contains dynamic covariates. The first and second columns must be sample id and time. If specified, cumulative exposure model will be used to fit the data.
`dist`	Distribution used in the model. Can be "weibull", "lognormal", "loglogistic", "frechet".
`method`	Default is "BFGS". This function calls optim to do optimization. Other options can be found in optim.
`subset`	This is an optional argument that specifies the subset of observations to be used in the fitting procedure.
`truncation`	An optional data frame contains truncation time and indicators.
`na.action`	Indicates what to be done when data contain missing values.
`weights`	An optional vector of weights for each observation.
`ref_time`	An optional vector of reference time (start time) of dynamic covariates. If it is `NULL`, `ref_time` is a vector of 0's.
`starts`	Initial values for parameters.
`...`	Further arguments can be passed to optim.

The default method for choosing start values can sometimes failed, the user may need to try several start values to make the function work.

This function returns an object of class "Lifedata.MLE". An "Lifedata.MLE" object is a list of

`call`	Called function.
`formula`	Formula argument in the input.
`coef`	Vector of coefficients.
`vov`	Variance-covariance matrix of `coef`.
`min`	`-loglikelihood` evaluated at `coef`.
`surv`	Survival probabilities.
`dat`	Dataset used to fit the model.

Yili Hong

Yili Hong and William Q. Meeker. Field-Failure Predictions Based on Failure-Time Data With Dynamic Covariate Information. Technometrics, 55(2), 135–149, 2013.

Surv, optim, summary.Lifedata.MLE. See methods(class="Lifedata.MLE") for all the functions related to "Lifedata.MLE" object.

#################################################################
### right censored data
data(shock)
res1=Lifedata.MLE(Surv(Distance, Censor)~1, data=shock, dist="weibull")
res1
summary(res1)

res2=Lifedata.MLE(Surv(Distance, Censor)~1, data=shock, dist="lognormal")
res2
summary(res2)

#################################################################
### left truncated right censoring data
data(testdata)
test=Lifedata.MLE(Surv(truncation.age, age, failure)~1, data=testdata,dist="weibull", 
method="Nelder-Mead", subset=(group=="MC_Old"))
summary(test)  


#################################################################
### dynamix covariates
data(Prod2.xt.dat)
data(Prod2.fai.dat)


test=Lifedata.MLE(Surv(failure.time, delta)~1, data=Prod2.fai.dat,xt.dat=Prod2.xt.dat,
starts=c(12, 3.5, log(2)), dist="weibull")
summary(test)