38 votes
Accepted

Latent Class Analysis vs. Cluster Analysis - differences in inferences?

Latent Class Analysis is in fact an Finite Mixture Model (see here). The main difference between FMM and other clustering algorithms is that FMM's offer you a "model-based clustering" approach that ...
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  • 114k
9 votes
Accepted

Inferring the number of topics for gensim's LDA - perplexity, CM, AIC, and BIC

Counter intuitively, it appears that the log_perplexity function doesn't output a $perplexity$ after all (the documentation of the function wasn't clear enough for ...
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  • 381
6 votes

Modelling longitudinal data where the effect of time varies in functional form between individuals

Four years after asking this question, I've learnt a few things, so perhaps I should add a few ideas. I think Bayesian hierarchical modelling provides a flexible approach to this problem. Software: ...
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5 votes
Accepted

LCA not returning the same results with the same data

Yes: it is perfectly normal. The algorithm used to find the ML estimation of a LCA can stop to a local maximum of the likelihood depending on the starting values. (This is a quite general problem in ...
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  • 411
5 votes

Favored methods for overcoming selection bias (special attention to healthcare fields)?

There is no single magic bullet to estimate treatment effects in the context of confounding (note: "selection bias" can mean something else). There is also no agreement in the field about the best ...
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  • 22.4k
4 votes

Latent Class Analysis vs. Cluster Analysis - differences in inferences?

A latent class model (or latent profile, or more generally, a finite mixture model) can be thought of as a probablistic model for clustering (or unsupervised classification). The goal is generally the ...
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  • 3,683
4 votes

Latent Class Analysis vs. Cluster Analysis - differences in inferences?

The difference is Latent Class Analysis would use hidden data (which is usually patterns of association in the features) to determine probabilities for features in the class. Then inferences can be ...
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  • 1,018
4 votes

Latent Class Models

Latent growth curve models are a kind of structural equation model (but they can also be thought of as a multilevel model). Latent class models and (most) structural equation models are a kind of ...
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  • 14.8k
4 votes

Latent class analysis order classes

What you're describing isn't a "problem" per se. Since the three latent classes are unordered, their labeling is completely arbitrary. In any particular run of the poLCA function, it's normal for the ...
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  • 41
4 votes
Accepted

Difference Between Latent Class Analysis and Mixture Models

Latent class analysis (LCA) is a discrete finite mixture model. Finite mixture model is a model-based clustering algorithm, that treats the distribution of the data $f$ as a mixture of $k$ ...
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  • 114k
3 votes
Accepted

How does the EM algorithm operate when group label may be missing?

Your model can be formalised as follows: $$\eqalign{ X_{1i}|Z_i=k&\sim\mathcal{N}(\mu_{1k},\sigma_1^2)\\ X_{2i}|Z_i=k&\sim\mathcal{N}(\mu_{2k},\sigma_2^2)\\ B_i|Z_i=k &\sim \mathcal{B}(...
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  • 92.6k
3 votes

Latent class multinomial logit model doubt

Your question has several unspoken threads. Your question is really about the design of the choice setting leading to a latent class model, not the modeling itself. The fact is that a single, classic, ...
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  • 9,822
3 votes
Accepted

Need help with Latent Transition Analysis

I've spent some time on this same question. I never found an R implementation, and I had a good deal of trouble uncovering the details of the SAS implementation. I think I finally found it in a ...
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  • 11.5k
3 votes

LCA number of parameters & degrees of freedom

As has been noted, poLCA only handles categorical data. So what does that mean for how it processes your data? From the help documentation for ...
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  • 2,105
3 votes
Accepted

LCA number of parameters & degrees of freedom

polca and mclust both performs Model-based cluster analysis, based on finite mixture models. However, polca is designed for Latent Class Analysis (LCA) which is the name for a particular class of ...
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  • 411
3 votes

In LDA, how to interpret the meaning of topics?

What LDA does, and what it can answer Consider this snippet from the paper introducing supervised LDA: Most topic models, such as latent Dirichlet allocation (LDA), are unsupervised: only the ...
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  • 8,424
3 votes

Convergence of Latent Class Linear Mixed Model with LCMM in R

The LCMM package in R has a huge bug which cause the model not to converge. If you use large numbers (e.g., years) as time points, the model do not converge. I think changing the time points to small ...
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3 votes

Latent Class Analysis - negative degrees of freedom

According to the documentation by Lanza et al http://www.methodology.psu.edu/ra/lca/model-faq/: A model will have negative degrees of freedom when the model is trying to estimate more parameters than ...
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  • 53
3 votes
Accepted

Why covariates are used in Latent class analysis (LCA)

What are indicators of the latent class? First, let's distinguish between indicators and predictors with a heuristic example. I know for a fact that many latent class analysis models have been ...
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  • 1,253
3 votes

How can you implement latent class analysis with distal outcomes in R?

I don't know of many statistical software packages in general that implement latent class analysis with distal outcomes. What are distal outcomes? (Compare to latent class regression) Just to ...
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  • 1,253
3 votes
Accepted

latent class model on choice data with gmnl - choosing starting values

Latent class models have likelihoods that are multi-modal. Best practice appears to be to repeatedly fit models with randomly selected start values, and choose the solution with the highest ...
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  • 1,253
3 votes
Accepted

How do we correctly select the final number of classes in latent class analysis?

Kathryn Masyn wrote an excellent introduction in 2013 with worked examples of latent class/profile analysis. She covers several model selection indicators, one of them being the Bayesian Information ...
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  • 1,253
3 votes

How to correctly address "ALERT: Iterations finished, maximum likelihood not found" in poLCA?

Increasing the number of maximum iterations (maxiter) solved the problem. This is because the number of iterations required increases as the number of latent ...
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2 votes

In LDA, how to interpret the meaning of topics?

LDA is an unsupervised learning method that maximizes the probability of word assignments to one of K fixed topics. The topic meaning is extracted by interpreting the top N probability words for a ...
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2 votes

Latent Class Analysis vs Rasch Analysis

Latent class analysis is a latent mixture modeling technique that is interested in finding latent subgroups in multivariate categorical data. It quite naturally is a way to classify individuals, which ...
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  • 342
2 votes

Choice based conjoint latent class analysis in R

flexmix would do the job but (so far I remember) only if you model binary (Yes/No) or pairwise (A vs B) choices (Last time I checked the authors were working on an extension to multinomial (MNL) ...
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  • 919
2 votes
Accepted

latent class analysis which modifies "given a certain class, probability for a respondent to show observed response"

Latent class models have a nearly 70 year provenance. The original work is due to Paul Lazarsfeld, the late Columbia sociologist, and dates back to the post-WWII era. His approach amounts to an ...
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  • 9,822
2 votes
Accepted

What is the best way to deal with missing data in latent class models?

Your model will be better for including the incomplete data. It will increase your sample size, which is good. But, even better, is that it will mean that your model is assuming the data is Missing At ...
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  • 3,265
2 votes
Accepted

Mixing variable types in latent class/profile analysis?

Yes - below is an image taken from a LCA presentation by Chuck Huber from Stata. Note inclusion of a covariate. You may be able to access the entire presentation from this link: https://www.stata.com/...
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  • 316
2 votes

Convergence of Latent Class Linear Mixed Model with LCMM in R

You have highlighted one important issue: estimates on the boundaries of the parameter space can cause convergence issues. From the documentation: the program may not converge and reach the maximum ...
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