#Digital regress software

The EM algorithm uses the complete-data likelihood as if we have observed values for the latent class indicator variable. Class probability is also referred to in the literature as a “latent class probability”, “component probability”, “mixture component probability”, “mixing probability”, “mixing proportion”, “mixing weight”, or “mixture probability”.Įxpectation-maximization algorithm. In the context of FMM, an iterative procedure for refining starting values before maximizing the likelihood. FMM uses multinomial logistic regression to model class probabilities. Class model is also referred to in the literature as a “component model”, “component density”, or “component distribution”.Ĭlass probability. In the context of FMM, the probability of belonging to a given class. In the absence of covariates, the regression model reduces to a distribution function. Latent classes are identified with the levels of the categorical latent variables and may represent healthy and unhealthy individuals, consumers with different buying preferences, or different motivations for delinquent behaviour.Ĭlass model. A class model is a regression model that is applied to one component in a mixture model. I should note that there are also advanced variations of FMM which are extensions of factor analysis and structural equation modelling (SEM).įor those seeking a bit more technical detail regarding FMM, I’ve included some entries from the manual’s glossary on FMM below.Īny copy/paste and editing errors are mine.Ĭategorical latent variable. A categorical latent variable has levels that represent unobserved groups in the population. FMM often reveal unexplained heterogeneity that is either ignored or only partially revealed by multiple models for a priori groups.

We can estimate the proportion of the population in each latent class, and we can predict the probabilities that the observations in our sample belong to each latent class.”Ī common mistake in statistical modelling is to assume “one size fits all” or to run multiple models on subjectively pre-defined subgroups of consumers, for example. We can compare models with differing numbers of latent classes and different sets of constraints on parameters to determine the best fitting model.įor a given model, we can compare parameter estimates across classes. Each latent class can be fit with its own regression model, which may have a linear or generalized linear response function. “A finite mixture model (FMM) is a statistical model that assumes the presence of unobserved groups, called latent classes, within an overall population.

#Digital regress software

So, what are FMM? Here is a concise definition adapted from the Stata 16 software manual: They are complex, but no longer obscure, and have real practical value – I have used them for many years. It’s also unfortunate because FMM are useful tools for researchers and data analysts in many fields.

This is understandable since articles and online discussions about technical subjects such as FMM often confuse more than enlighten.

Many marketing researchers and data scientists may have heard of finite mixture models (FMM) but are not clear what they are.