Anonymous posted on Friday, April 19, 2002 - 9:41 am
Dear Linda & Bengt,
I am wondering if it is legitimate to use a binary instead of a continuous variable as an outcome in LCA? Thanks.
bmuthen posted on Friday, April 19, 2002 - 6:11 pm
LCA is for categorical outcomes. LPA for continuous outcomes. Maybe you are thinking of something else?
Anonymous posted on Monday, April 22, 2002 - 6:37 am
sorry for the misunderstanding. What I meant to ask was the following: In a LCA, where I use a certain set of categorical latent class indicators (u's), is it legitimate to use a categorical instead of continuous variables as outcomes (y's). Example 25.9B on page 268 uses a continous outcome. I am looking forward to your reply.
bmuthen posted on Monday, April 22, 2002 - 9:35 am
I see what you mean. Yes, you can use a categorical "distal outcome" instead of a continuous one. This distal is then simply yet another indicator of the latent class variable because there is no difference statisticallly between this and regressing the distal on class. When you have more than one categorical distal, however, you need to watch out for the fact that the 2 distals become independent given class and that is maybe not what you want.
Anonymous posted on Monday, April 22, 2002 - 10:47 am
thank you for your reply. If I understand you correctly, the categorical outcome also needs to specified in the list of categorical variables. Additionally, instead of adding a line for the mean and variance (for continuous y's), I just provide start values for the threshold.
bmuthen posted on Tuesday, April 23, 2002 - 12:35 pm
Yes and yes.
Anonymous posted on Tuesday, March 09, 2004 - 9:59 am
I would like to model two periods, where in period 1 a person can be in one of three mutually exclusive states(c1,c2,c3) and in the second period they could be in one of the three mutually exclusive states. My interest is in predicting class membership in period 2 given class membership in period 1 and covariates. Is this possible in Mplus?
bmuthen posted on Wednesday, March 10, 2004 - 7:28 am
I think you are considering an unordered categorical (nominal) variable at two time points and want to relate these two variables. This can be done by letting the variables be represented by perfectly measured latent class variables, one at each time point. In Mplus 2.14 this is done by a single latent class variable with 3 x 3 = 9 classes (see paper #86 on the Mplus home page), while in the soon to be released Version 3 it is done by regressing the time 2 variable on the time 1 latent class variable.
Anonymous posted on Wednesday, July 28, 2004 - 7:32 am
Dear Linda & Bengt,
I would like to perform LCA, where each mixture is an IRT model (logistic regression model with random intercept). My question is whether Mplus is capable of handling such models.
BMuthen posted on Wednesday, July 28, 2004 - 9:01 am
Yes, this can be done in Mplus Version 3. Our experience to date shows that with binary observed variables, it can be hard to estimate such a model unless the mixture is very clear, while with ordered polytomous observed variables it is easier.
Anonymous posted on Wednesday, July 28, 2004 - 9:27 am
thank you very much for your prompt answer. I actually would like to fit discrete mixture models, where each mixture is a 2PL model, with random person parameter, and compare the models with different restrictions. (My intention is to do it with both dichotomously and polytomously scored items. But in separate analyses, not mixing the two types of scoring.)
bmuthen posted on Thursday, July 29, 2004 - 8:21 am
It will be interesting to see how this works out. I am looking for good examples to illustrate these new methods, so please let me know of any successes.
JISUN CHOI posted on Wednesday, August 25, 2010 - 12:40 pm
Hello. I am interested in using Mplus to do a mixture regress analysis and have a couple of basic questions.
1. I saw the example 7.1(mixture regression analysis for a continuous dependent variable).
My dependent variable is a binary variable. Can I test this model using a binary dependent variable instead continuous dependent variable?
2. I am also interested in looking at whether individuals in different latent classes vary in terms of their background (covariates - i.e. race and education level) on the relationship bewteen independent and dependent variable.
Can I get profile information (i.e. frequency or portion)of these covariates associated with each latent class membership?
or Can I get plot for the findings to see visibly different group membership?
I will appreciate if you tell me some references about that.
2. You can regress the latent class variable on a set of covariates to see which are related to class membership. You can also use the AUXILIARY (e) option. See the user's guide for details. I don't know how you would plot this information.
JISUN CHOI posted on Thursday, August 26, 2010 - 10:28 am
Thank you very much for responding very quick. Your response raised one more question.
I saw Example 7.3. In my understanding I can do this latent class analysis if all dependent variables are binary that refer to binary latent class indicators.
What I tried to do is a mixture regression analysis. I am interested in looking at the relationship between job satisfaction (a binary dependent variable) and several continuous covariates.
I think it might be closer to Example 7.2. And, I thought that I can use logistic regression with a categorical latent variable. Is it possible?
Thanks a lot for your time, advice, and suggestions.
I'm sorry. You should just add the CATEGORICAL option to Example 9.1 if your dependent variable is binary. For this model it is difficult to have slopes vary across classes. You may only be able to allow intercepts to vary.
JISUN CHOI posted on Thursday, August 26, 2010 - 1:02 pm
Hello, I have a 4 class model based on 13 binary indicators and have included some residual associations. I would like to have class predict binary distal outcomes. Is it valid to use auxiliary and (e)? If not, could you please direct me to an appropriate example? Many thanks!
May Chen posted on Sunday, May 28, 2017 - 12:01 pm
I would like to test whether class membership predicts a categorical distal outcome. Based on the table in Mplus webnote 21, I've gathered that the best way to do this is through the DCAT option. However, I would also like to include covariates so that the effect of latent class on the distal outcome is controlled for by those covariates. Do you have any suggestions on how to proceed? Can the manual versions of BCH or DU3STEP be used in this case?
Information about ex BIC says it should be low. Are the wrong values we look at or is the analysis incorrect? If the analysis is correct then should we report the AIC and BIC values when the analysis is to be described? Grateful for help Bo Rolander
Hi I have run an equality test of Means/Prob across classes for a 3 class LCA with binary indicators and 3 binary distal outcomes.
The Ref class 3 is the highest symptom class and the first 2 classes are being compared in relation to it. But I need to know how odds ratios for the outcome for the 2 higher symptom classes using the low symptom class as ref class I would like to change the ref class to Class 1 which is the low symptom class so that it will calculate ORs on the binary outcomes. How is this done. Thanks
If I have labelled my variable in the dataset as 0 = no 1 = yes 2 = other
where the variable refers to presence of mental health disorder, then 'category 1' will represent the probability of someone without the trait being in the class and 'category 2' will represent the probability of someone with the trait being in the latent class and category 3 will represent 'other'?
your Vis variable will be treated as "categorical", that is ordinal with more than 2 categories, not nominal. You might want to create 2 binary variables out of your 3-category nominal Vis variable and run one at a time.
I am running the LCA with a continuous variable. It is a measure of coping, where each subscale of the measure represents how often the participant engages in a particular coping strategy.
It suggests 3 classes is the best fit- is there a way I can see if the mean score for each coping sub-scale differs between the 3 classes? I can see that class one has the highest mean level of 'avoidance' for example, but how can I tell that this mean is statistically different from the lower means for this subscale in class two and three?
I am fitting an LCA with a categorical outcome variable (nonhon) with the Auxiliary DCAT option. I've used parameterization=rescov to allow for residual covariances for class indicator variables where local independence assumptions are violated. However, when I use this option, I am no longer able to obtain standard errors for model estimated probabilities for latent class indicators. Is there a way I can obtain these standard errors?
TITLE: LCA of discharge predictors 3 class; DATA: FILE IS C:DisPredictMplus.csv; VARIABLE: NAMES ARE ptsddx tbidx depdx subdx chronpdx speed carac drreck fight weap tobac duib duid duia arrestb arrestd arresta dvb dvd dva sxb sxd sxa pclt plccut phq9t phq9cut alct alctrd alccutre phq15t phq15cut male female nonhon id; USEVARIABLES ARE duib duid arrestb arrestd dvb dvd sxb sxd; AUXILIARY ARE (DCAT)nonhon; CATEGORICAL ARE duib duid arrestb arrestd dvb dvd sxb sxd; MISSING IS .; IDVARIABLE IS id; CLASSES = class(3);