Finite Mixture Distribution Models
ANOVA Tables for Mixture Model Objects
Grouped Binomial Data
Starting Values of Parameters for the Binomial Data Set
Cassie's Length-Frequency Example
Extract Mixture Model Coefficients
Add Conditional Data to Grouped Data
A Mixture Data of Three Exponential Distributions
A Mixed Data with Fifteen Normal Components
Compute Mixture Model Fitted Values
Estimate Parameters of One-Component Mixture Distribution
Compute Probabilities of an Observation Falling into a Grouping Interv...
Estimate Parameters of Mixture Distributions
Construct Constraints on Parameters
Mixed Data
Construct Grouped Data from Raw Data
Find the Parameters to be Estimated
Construct Starting Values for Parameters
Compute All of Parameters from the Estimated Parameters
Scale Mixture Data with Three Normal Components
Karl Pearson's Crab Data
Starting Values of Parameters for the Pearson's Data
Heming Lake Pike Data
Length-Frequency Data for Heming Lake Pike
Length-Frequency Data with Subsamples for Heming Lake Pike
Starting Values of Parameters for the Pike Data
A Sample of Pike Lengths
Mix Object Plotting
Mixdata Object Plotting
Grouped Poisson Data
Starting Values of Parameters for the Poisson Data Set
Print Mix Object
Summarizing Mixture Model Fits
Check Constraints
Check Parameters
Compute Shape and Scale Parameters for Weibull Distribution
Compute the Mean and Standard Deviation of Weibull Distribution
Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm.