Hi love this project! I had a question if StepMix works with Time Series/Longitudinal Data? I have a dataset of trajectories of longitudinal values over scattered missing occurrences. I'm building a two step hybrid model. The first step involves using these repeated measurements as indicators for the latent profile analysis (LPA). You would conduct separate LPA for each variable, plotting the conditional means derived from LPA of each variable over time. This plot would represent the estimated mean of each variable within a latent LPA class at each measurement point. The interpretation of latent class membership would then be based on the trajectory of these mean estimates over time. This would identify two classes dependent on the variable. In the second stage, you use the estimated class membership (based on the highest posterior probability of class membership from the first LPA) for each variable as indicators for the latent class analysis (LCA). In this case, you have five categorical indicator variables from the LPA. You can then plot the conditional probabilities of class membership from the LCA, representing the likelihood of belonging to a particular LPA trajectory class given an LCA class membership. The meaning of the LCA classes would be determined by the composition of LPA trajectory classes within each LCA class. The 2nd stage LCA does not have time-dependent class switching problem, because the indicators for the LCA are measure-specific LPA trajectory profiles. It is not like you are doing separate LCA at different time points.
Currently comparing if wide format or long format works better? Also according the documentation gaussian_nan (first snippet) and continuous_nan (second snippet) are essentially the same for StepMix? Should I be using something else? Any other hyperparameters would be useful here?
Hi love this project! I had a question if StepMix works with Time Series/Longitudinal Data? I have a dataset of trajectories of longitudinal values over scattered missing occurrences. I'm building a two step hybrid model. The first step involves using these repeated measurements as indicators for the latent profile analysis (LPA). You would conduct separate LPA for each variable, plotting the conditional means derived from LPA of each variable over time. This plot would represent the estimated mean of each variable within a latent LPA class at each measurement point. The interpretation of latent class membership would then be based on the trajectory of these mean estimates over time. This would identify two classes dependent on the variable. In the second stage, you use the estimated class membership (based on the highest posterior probability of class membership from the first LPA) for each variable as indicators for the latent class analysis (LCA). In this case, you have five categorical indicator variables from the LPA. You can then plot the conditional probabilities of class membership from the LCA, representing the likelihood of belonging to a particular LPA trajectory class given an LCA class membership. The meaning of the LCA classes would be determined by the composition of LPA trajectory classes within each LCA class. The 2nd stage LCA does not have time-dependent class switching problem, because the indicators for the LCA are measure-specific LPA trajectory profiles. It is not like you are doing separate LCA at different time points.
Currently comparing if wide format or long format works better? Also according the documentation gaussian_nan (first snippet) and continuous_nan (second snippet) are essentially the same for StepMix? Should I be using something else? Any other hyperparameters would be useful here?