That's correct. I estimated class-invariant covariance matrix (diagonal), as I intended. However, the residual covariance matrices (Observed - estimated) vary across classes, which led me think that observed covariance matrices differ across classes. Is that right?
Here "Observed" is a class-specific covariance matrix computed by weighting the raw data with the individuals' posterior probabilities as estimated from the model. This means that the "observed" covariance matrix does change over the classes. This is the closest to observed that one can get with unknown mixture classes.
Anonymous posted on Friday, May 14, 2004 - 11:28 am
I am new to Mplus and I need help on my problem below. I have a structural model has two exogenous latent models effecting one endogenous latent variable. One of the exogenous latent variables has four categorical manifest variables and the other exogenous latent variable has three manifest variables. The endogenous latent variable is continuous and has 4 continuous manifest variables. I am considering using latent profile analysis and the Mplus program. Am I right and how do I go about it. Thanks so much for your assistance.
Anonymous posted on Friday, May 14, 2004 - 12:35 pm
Sorry I did not preview my first message. Below is the corrected version. I am new to latent variable modeling (not SEM) and Mplus. I need help to solve a modeling problem. I have a model that has two exogenous latent variables effecting one endogenous latent variable. The latter has four continous manifest variables. One of the exogenous latent variables has four categorical manifest variables and the other has three continous manifest variables. I want to use latent profile analysis and Mplus. Am I right in using LPA? Can Mplus perform the analysis? Thanks
There are two papers on the homepage at www.statmodel.com that describe such models using Mplus. The first author is Lubke.
Scott Weaver posted on Wednesday, August 02, 2006 - 11:30 pm
I am conducting a latent profile analysis. I have specified tech11 to get the LMR test, but am having difficulty figuring out how to specify the start values such that the first class is the smallest class. I tried specifying the start value for the latent class mean (C#1*-2 in a 2 class model) but that does not seem to work to make class 1 be the smallest class.
You should be specifying starting values so that the largest class is last. You do this by using the parameter values of the means of the latent class indicators not the means of the categorical latent variable.
I tried what you suggested, but it does not seem to be working either. In my initial run of a 2 class model, the largest class is first. So I used the estimated means from the largest class as start values for the last class (%c#2%) and ran the model with starts = 0 0. I verified that my specified start values were used with tech1. However, the results still are such where the largest class is first. Any suggestions? Thanks!
any opinions about the difference between Latent Profile Analysis and Latent Class Analysis? From the Lubke and Muthen's paper about factor mixture models, I think for LPA, the latent class indicators are continuous variables, but for LCA, the latent class indicators are categorical or ordinal variables? Am I right?
This is a question regarding the output file from a mixture model.
I am saving the output from a mixture model with the cprobs for segment membership- but I would also like to get the id's of the observations - in order to do some additional post hoc analysis using other variables not used in the mplus models. Is there a way that I can do this? In other words, can I get mplus to carry forward an id variable from the input data file into the output data file
Hi I have pre and post measures on five cognitive domains for a large sample of healthy older adults with a family history of Alzheimer's disease. The five pre-and post measures were adjusted for age, gender, and IQ using data from a control sample We are interested in identifying 3 possible groups in terms of cognitive performance at both time points: improver, stable, or decliner. I have thought about 3 possible approaches to analyze the data in MPLUS 1. Include all 10 measures (5pre and 5post) as indicators of class membership. Do latent Profile analysis. 2. Compute five change scores: post - pre, in which case a negative score would indicate decline, and then use both the five change scores and also the Time 1 scores as indicators of class membership. (Including performance at time 1 and also change would capture those with low initial performance and also a decline.) 3. Assign a score to each individual on each of the five cognitive dimension according to the following scheme: Assign 2 to those performing 1SD or more above Controls Assign 1 to those performing within +/-1SD relative to controls Assign 0 to those performing 1SD or more below Controls. Use these 10 categories in a latent Class Analysis.
What would be the best approach to identify decliners, stables, and "improvers"?
How about Latent Transition Analysis, where you have a latent class model for the 5 outcomes at each of the two time points? With say 2 classes at each time point you would have a chance to get the decliners, stables, and improvers.
Degrees of freedom are not relevant for LPA because there is no unrestricted set of sample statistics to test against. I know of no guidelines for the minimum number of continuous indicators for latent profile analysis.
I'm using Latent Profile Analysis on the data collected from three different sample sources. I could try to use sample sources are a covariate, but am wondering if there are any other ways to take different sample sources into account in the analysis.
Hello Dr.'s Muthen, I have run an LPA on three continuous indcators and have found a good 4 class model. I would now like to use that Latent class variable as 'predictor,' if you will, in another LV model that has dependent variable that is defined by 3 binary indicators and ther observed covariates. Is there any syntax or examples about how to do this? Thank you, ***Melissa
Dear Dr.'s Muthen-I am running a LPA with decision making variables. However, theoretically I should include indicator variables that "overlap" -for instance, benefits of taking a risk, costs of taking a risk, and "depth of processing" (the sum of all benefits and costs), and benefit-to reward-ratio (benefits/costs). This would be an issue in regression, I believe, according to Cohen, Cohen, West, & Aiken. From your view, is there a similar issue in LPA? The solution converges in Mplus but my concern is that variables with "overlap" may be less likely to "drive" the differences across profiles. Thank you very much for your time.
In regression you worry about collinearity (too high correlation) among the predictors (covariates; x's; IVs), but in LPA your variables are outcomes (y's; DVs) so that issue isn't involved. Still, overlapping indicators may create residual covariances in LPA which may cause BIC to point to too many latent classes. If this is a concern, you can also do mixture modeling with all covariances in the model in line with UG ex 7.22.