mlefit: Maximum likelihood regression on weibull and lognormal...

In abremDebias: Maximum Likelihood Estimation Models with Bias Adjustment Options for Reliability Analysis

Description

Maximum likelihood regression on weibull and lognormal distributions

Usage

mlefit(x, dist="weibull", debias=NULL, optcontrol=NULL)

Arguments

x	A dataframe such as generated by mleframe with column names 'left', 'right' and optionally 'qty'. Exact failure data (occurrences) have same time entered in both 'left' and 'right' columns. Suspension data has last known life[time] entered in 'left' column and -1 entered in 'right' column. The left(early) interval bound for left-censored data must be entered as zero. (NA is not accepted).
dist	A string defining a distribution to be fit. Implemented distributions are "weibull" (default),"lognormal", "weibull3p" and "lognormal3p".
debias	An optional string argument indicating the adjustment to be applied to the shape or standard deviation parameter of the fitted data. Adjustments recognized are: "rba" (default), "mean", and "hirose-ross". Any entry for debias with the lognormal distribution will be processed as "rba" with a warning if it was not specified.
optcontrol	An optional list of arguments for control of the MLE optimization. The strict naming convention for the list items are: 'vstart', a vector holding starting estimate for parameters in the order used by the underlying R distribution [CAUTION: for weibull the order of parameters is (shape, scope)], 'limit', the convergence limit, 'maxit', the maximum number of interations permitted in the optimization, and 'listout' a logical indicating whether output should list both the fit and the optimization progress as a dataframe.

Details

The negative log-likelihood is minimized by the Nelder-Meade, simplex, algorithm. This algorithm requires a reasonable starting point for the estimate. Such a reasonable estimate is attempted by default, but in some cases this may be inadequate for proper function. the optcontrol argument has been provided to give debug developers a means of altering and examining the progress of the optimization.

Value

Returns a named vector with parameters of the distribution fit and goodness of fit in terms of log likelihood.

References

Dr. Robert B. Abernethy, (2008) "The New Weibull Handbook, Fifth Edition"

Examples

failures<-c(90,96,30,49,82)
suspensions<-c(100,45,10)
weibull_fit<-mlefit(mleframe(failures,suspensions))

abremDebias documentation built on May 2, 2019, 5:17 p.m.