Tag: expectation maximization

R code for fitting a finite mixture model to fatigue data

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.

fatigue limit model mixture

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R code for fitting a Finite Mixture Model to survival data

The following R code fits a Finite Mixture Model to survival (or reliability) data. The survival/reliability data may contain (right / interval) censored observations. However, it is also possible to fit the Finite Mixture Model to complete (uncensored) survival/reliability data.

The Finite Mixture Model is fitted by using the Expectation-Maximization (EM) algorithm. The fitted model assumes that the lifetime observations follow either a Weibull, lognormal, or Gaussian distribution.

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R code for fitting a mixture distribution to censored data

The following code fits a mixture distribution to (right / interval) censored or complete (uncensored) data in R. The mixture distribution is fitted by using the Expectation-Maximization (EM) algorithm.

The R code demonstrates how to fit (1) a mixture of Weibull distributions, (2) a mixture of lognormal distributions, and (3) a mixture of Gaussian distributions.

finite mixture model censored data

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