Reliability and Survival Methods > Reliability Growth > Reliability Growth Platform Options
Publication date: 08/13/2020

Reliability Growth Platform Options

The Reliability Growth red triangle menu has the following options:

Fit Model

If the Time to Event Format, Dates Format, or Concurrent Systems data formats are specified in the launch window, this menu contains options to fit various Non-Homogeneous Poisson Process (NHPP) models. These options are described in detail below. Depending on the choices made in the launch window, the possible options are:

Crow AMSAA

Crow AMSAA with Modified MLE

Fixed Parameter Crow AMSAA

Piecewise Weibull NHPP

Reinitialized Weibull NHPP

Piecewise Weibull NHPP Change Point Detection

Fit Parallel System Model

If the Parallel Systems data format is specified in the launch window, this menu contains options to fit various models for multiple-prototype data. The possible options in this menu are dependent on choices made in the launch window.

See Redo Menus and Save Script Menus in Using JMP for more information about the following options:

Redo

Contains options that enable you to repeat or relaunch the analysis. In platforms that support the feature, the Automatic Recalc option immediately reflects the changes that you make to the data table in the corresponding report window.

Save Script

Contains options that enable you to save a script that reproduces the report to several destinations.

Save By-Group Script

Contains options that enable you to save a script that reproduces the platform report for all levels of a By variable to several destinations. Available only when a By variable is specified in the launch window.

Model List

Once a model is fit, the Model List report appears. This report provides various statistical measures that describe the fit of the model. As additional models are fit, they are added to the Model List, which provides a convenient summary for model comparison. The models are sorted in ascending order based on AICc. The Model List report contains the following statistics:

Nparm

The number of parameters in the model.

-2Loglikelihood

The likelihood function is a measure of how probable the observed data are, given the estimated model parameters. In a general sense, the higher the likelihood, the better the model fit. It follows that smaller values of twice the negative of the log-likelihood (-2Loglikelihood) indicate better model fits.

AICc

The Corrected Akaike’s Information Criterion.

BIC

The Bayesian Information Criterion.

See Likelihood, AICc, and BIC in Fitting Linear Models for more information about -2Loglikelihood, AICc, and BIC.

Want more information? Have questions? Get answers in the JMP User Community (community.jmp.com).
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