I'm afraid this information does not tell me why you don't get standard errors. Please send your input, data, output, and license number to email@example.com.
Hao Duong posted on Saturday, October 18, 2008 - 11:02 am
Dr. Muthen, I am confused about interpretations for ib sb on w, and c on w in example 10.9 in Mplus user's guide in 2007. Would you please explain them for me? Thank you I appreciate all your help! Hao Duong
I have two questions. I am attempting to establish a GMM with four time points. However, the time points are not equally spaced. I do have an indicator of time (age at each assessment).
1. Would this preclude me from using a traditional GMM? I was under the impression that I would have to use a two level model, rather than LGCM because of the unequal time between interviews, and regress my outcome on my time variable.
2. Also, if multilevel modeling is required, would it still be possible to examine classes of trajectories?
I should clarify as this is unclear. I have been able to run these models using the TYPE = RANDOM command for the individually varying time scores. I am wondering if there is a way, using this option, to examine classes of trajectories? If so, how would I specify this model in MPlus?
I'm doing multilevel latent class analysis with 12 items which have 4 categorical options. I tried few times but keep pop up some error message. And I'm not sure what's gone wrong. Hope someone can advise me on this. many thanks.
my input instruction: VARIABLE: NAMES ARE wel mor dif enj str qui bor lik hel oth uni job schid ; USEVARIABLES = wel mor dif enj str qui bor lik hel oth uni job ; CATEGORICAL ARE wel mor dif enj str qui bor lik hel oth uni job ; MISSING ARE all (9); CLASSES=C(3); CLUSTER=schid; WITHIN=wel mor dif enj str qui bor lik hel oth uni job ; ANALYSIS: TYPE = MIXTURE TWOLEVEL ; STARTS = 20 10; PROCES = 8 (STARTS); MODEL: %WITHIN% %OVERALL% %BETWEEN% %OVERALL% C#1; C#2; C#1 WITH C#2;
*** ERROR Categorical variable WEL contains 158 categories. This exceeds the maximum allowed of 10.
It sounds like you are reading your data incorrectly. Perhaps the number of variable names is not the same as the number of columns in your data set or you have blanks in your data. If you can't figure this out, send your files and license number to firstname.lastname@example.org.
I am implementing a IRT multilevel mixture model with item bias effects in Mplus. My structure has items nested in individuals, nested in countries.
I would like to code the mixtures nominal, yielding discrete random effects. For identification I use effect-coding for the mixtures. But when using the following syntax, there are no differences for the latent means across mixtures; both are zero.
Am I applying the correct syntax for effect-coding of mixtures?
Hello, I'm running Multilevel Latent Class model similar to model presented in Henry&Muthén(2010). The core of the model is multilevel logistic model: resp on negd1 negd2 negd3; and 2 latent classes are specified. Estimation of the model works when I've two regression in each of the latent class:
MODEL: %WITHIN% %OVERALL% resp on negd1 negd2 negd3; cw#1 on time ; %CW#1% resp on negd1 negd2 negd3 ; %CW#2% resp on negd1 negd2 negd3 ; %BETWEEN% %OVERALL% resp ; [resp$1] ; %CW#1% resp ; [resp$1] ; %CW#2% resp ; [resp$1] ;
But the problem appears when I want to have empty logistic model in class 2. i.e model without explanatory variables. I was trying several specifications. None of them worked. While I'm declaring ON statement in %OVERALL% part I get ON statements in all classes. When ON statement is not declared in %OVERALL% part I'm not allowed to specify it for class 1. Is there a way to specify logistic model with explanatory variables in class 1 and empty model in class 2?
Hello, I plan to use UG Example 10.12 (two-level LTA with a covariate) for my analyses. I have students nested in schools. I understand the code in the example, except I want to clarify one aspect of it.
In the code below, why are the indicators for the latent classes modeled at the between level? If these reflect individual responses (such as from individual students), wouldn't those be on the within level?
Or is it that because we are estimating probabilities (or mean responses) for the items for persons, conditional on class, this becomes an average across persons--no longer on the within level? Thank you.
MODEL: %WITHIN% %OVERALL% c2 ON c1 x; c1 ON x; %BETWEEN% %OVERALL% c1#1 ON w; c2#1 ON c1#1 w; c1#1 c2#1;
MODEL c1: %BETWEEN% %c1#1% [u11$1-u14$1] (1-4); %c1#2% [u11$1-u14$1] (5-8); MODEL c2: %BETWEEN% %c2#1% [u21$1-u24$1] (1-4); %c2#2% [u21$1-u24$1] (5-8);
OK, may I request your input on the following questions regarding this section of the code: %BETWEEN% %OVERALL% c1#1 ON w; c2#1 ON c1#1 w; c1#1 c2#1;
Understanding the code above: 1) Why is c2#2 ON c1#2 (and other combinations such as c2#2 ON c1#1) not above, similar to the second line? I believe that the code above regresses the cluster-level (average or intercept) latent status for class 1 of c2 on that for class 1 of c1. This is part of the random intercept setup. But wouldn't regressing the second class of each latent class variable make sense to do as well?
2) By the same token, why does the above code not show "c1#2 c2#2;" as well? Is it that by allowing intercepts for the first class for each latent class variable to vary across clusters, these are already free to differ from those for the second latent classes?
Altering the code for my data: 3) If I have 3 latent class variables rather than 2, and want to have random intercepts, I would also model c3#1 ON c2#1, right?
4) Finally, if I try a model without random intercepts, would I remove both the "c2#1 ON c1#1", and "c1#1 c2#1;" sections of code?
Dear Muthen I am writing this post to ask MLCA analysis. is it possible to run "multilevel LCA" with covariates and distal outcome simultaneously? the number of individual cases are 580 nested with 30 organizations.
I'm now considering to calculate the MOR proposed by Larsen & Merlo (2005). But due to my poor statistical ability, I cannot understand the mathematical expression shown in page 83, 1st line. Could anyone give me some example with actual number to calculate MOR in this article?
Some segments of Example 10.1, 'Two-level mixture regression for a continuous dependent variable,' are below.
I'd like to modify the program for two objectives: (1) Incorporate the measurement error table output from step 1/step 2 of manual 3-step estimation, i.e., "Logits for the Classification Probabilities for the Most Likely..." (2) Temporarily omit the regressions from the overall model in the between part of the model.
The example does not mention %C#2% in the within part of the model. Is it necessary to omit %C#2%? If so, then how could the parameter N#1@ be used for %C#2%?
VARIABLE: CLASSES = C(2); WITHIN = X1 X2 N; ! N is my addition ! BETWEEN = W; ! Not using for now CLUSTER = CLUS; NOMINAL = N;
ANALYSIS: TYPE = TWOLEVEL MIXTURE; STARTS=0;
MODEL: %WITHIN% %OVERALL% Y ON X1 X2; C ON X1; %C#1% [Nemail@example.com]; ! As per Webnote 15, appendix E, step 3 of ! manual 3-step estimation Y ON X2; Y;
%BETWEEN% %OVERALL% ! Y ON W; ! No between-level regression ! C#1 ON W; ! No between-level regression ! C#1*1; ! A starting value is not needed %C#1% [Y*2]; OUTPUT: TECH1 TECH8;
Dear Muthen, Is it possible to have a twolevel mixture model in mplus where LCA classes are predictor variables, outcome is binary observed variable & the covariates for the model include a continous latent factor? i.e Y = Classes + continous latent factor + covariates
The code below is an attempt to do this but I'm having a difficulty keeping the binary Y separate from observed categorical that are used in LCA classes. Is that possible?how do I achive that? ## UseVariables are Dailies Radio TV Maln Brstfed Eat_Freq Diet_Div C_Age Urban Wealth M_Age Female sexXage Edu; Classes=HLTH_BHV(2); Categorical are Brstfed Eat_Freq Diet_Div; WITHIN = C_Age Female sexXage; BETWEEN = Dailies Radio TV; CLUSTER = Comm_Hse; Auxiliary = (r)Wealth Urban Edu M_Age; Define: sexXage=C_Age*Female; CENTER C_Age(GRANDMEAN); Analysis: Type=TWOLEVEL MIXTURE; Starts=4000 40; Processors = 4; Algorithm=integration; Model: %WITHIN% %OVERALL% HLTH_BHV ON Female C_Age sexXage;
%BETWEEN% %OVERALL% MED_USE BY Dailies Radio TV; Maln ON HLTH_BHV MED_USE; HLTH_BHV ON MED_USE;
How do I ensure the outcome variable is treated as binary when LCA classes are regressed on it?
You don't say which variable the binary outcome variable is. Perhaps it is "Maln" but it is not declared categorical.
You say "when LCA classes are regressed on it". If an outcome is a function of the latent class variable, you should say "the outcome is regressed on the latent class variable", not the other way around. But note that in Mplus you don't say "y ON c", but instead let the default change of y means/thresholds be the effect of c on y.
See also our 3-step papers on the website such as Web Note 21.
I want to change the reference class from being class 3 to class 2. Would I use the outputted values of class 2 as user-specified starting values for class 3 in the next run? Where do I insert the desired starting values in the code below? Thanks.
USEVARIABLES = y x1 x2 w n; NOMINAL = n ; CATEGORICAL = y;
CLASSES = C(3); CLUSTER = clus; WEIGHT = wgt; WITHIN = n x1 x2; BETWEEN= w;
Youmi Suk posted on Monday, February 27, 2017 - 11:24 am
In multilevel mixture regression, if we do not use no particular indicators for latent classes and have a model with categorical and continuous variables (covariates), do we have to specify the variable type of covariates?
In the example 10.1, NAMES ARE y x1 x2 w1 w2 class clus; USEVARIABLES = y x1 x2 w1 w2; CLASSES = c (2); WITHIN = x1 x2; BETWEEN = w1 w2; CLUSTER = clus;
If x1 (a within level variable) w1 (a between-level variable) are binary variables, should we specify x1 w1 as categorical variables (CATEGORICAL = x1 w1)?
NAMES ARE y x1 x2 w1 w2 class clus; USEVARIABLES = y x1 x2 w1 w2; CATEGORICAL = x1 w1; CLASSES = c (2); WITHIN = x1 x2; BETWEEN = w1 w2; CLUSTER = clus;
If I specify them using ¡°CATEGORICAL,¡± I get threshold information, relating to indicators.
You should not declare a variable type such as categorical for a covariate.
Youmi Suk posted on Monday, February 27, 2017 - 7:48 pm
Thanks much for the quick reply.
I have one more question regarding a variable type for a dependent variable in the same situation above except for the variable type of the dependent variable (a continuous dependent variable -> a binary dependent variable).
Given the fact that we cannot declare a variable type, cannot we use a categorical dependent variable for multilevel mixture (logistic) regression?
I guess that if we cannot specify a categorical variable as a dependent variable in Mplus, do we use the linear-probability model (i.e., standard regression), rather than using logistic regression model?
You can declare a dependent variable (DV) as categorical or anything else - I don't know why you think you cannot. I was referring to a covariate, not a DV.
Youmi Suk posted on Friday, March 10, 2017 - 12:56 pm
(1) We are trying to fit 2-class multilevel models with Level 1 latent classes
(2) With a binary DV, adding "DV;" provides class-specific variance estimates for the random intercept. It worked.
VARIABLE: NAMES ARE y x w clus; USEVARIABLES = y x w; CLASSES = cw (2); CATEGORICAL = y; WITHIN = x; BETWEEN = w; CLUSTER = clus;
MODEL: %WITHIN% %OVERALL% y on x; %cw#1% y on x; %cw#2% y on x;
%BETWEEN% %OVERALL% y on w; %cw#1% y on w; %cw#2% y on w; y;
(3) However, with a continuous DV, adding "DV;" did not work. The program stopped with the error message.
VARIABLE: NAMES ARE y x w clus; USEVARIABLES = y x w; CLASSES = cw (2); WITHIN = x; BETWEEN = w; CLUSTER = clus;
MODEL: %WITHIN% %OVERALL% y on x; %cw#1% y on x; %cw#2% y on x; y;
%BETWEEN% %OVERALL% y on w; %cw#1% y on w; %cw#2% y on w; y;
The error message is as follows: ------------- *** FATAL ERROR CLASS-SPECIFIC BETWEEN VARIABLE PROBLEM. ------------- We would like to allow class-specific random intercept variances with a continuous DV, identifying Level-1 latent classes. Could you help me out with this problem?