In one of my previous posts I demonstrated how to fit a Finite Mixture Fatigue Limit Model to fatigue data.
The following R code computes the quantiles of a mixture fitted by the Finite Mixture Fatigue Limit Model.
Continue reading R code for computing quantiles of a Finite Mixture Fatigue Limit Model
The following R code fits a Finite Mixture Fatigue Limit Model to fatigue data. The fatigue data may contain right, left, and interval censored observations. These censored observations are also referred to as runouts.
The Finite Mixture Fatigue Limit Model is fitted by using the Expectation-Maximization (EM) algorithm. The fitted model assumes that the fatigue observations follow either a Weibull, lognormal, or Gaussian distribution.
Continue reading R code for fitting a finite mixture model to fatigue data
The following R code implements a fatigue-limit model. Details of the implemented fatigue-limit model can be found in this 1997 paper by Pascual and Meeker, and in Meeker and Escobar’s 1998 book Statistical Methods for Reliability Data (pp. 593-597).
The implemented model assumes that the fatigue-limit is fixed (i.e., identical fatigue-limit for all specimens). But in some practical cases it may be more realistic to assume that the fatigue-limits of individual specimens are considerably different. Under such circumstances a random fatigue-limit model should be used, which allows for a different fatigue-limit for each specimen. A random fatigue-limit model is described by Pascual and Meeker in this 1998 paper. However, the implemented fixed fatigue-limit model can still be applied when the differences in individual fatigue limits between specimens are small.