WJCAO posted on Tuesday, September 20, 2011 - 7:16 am
Hi, I'm trying to use Montecarlo in Mplus to generate 40 categorical variables with 6 factors.Then MLR were used to analysis these data.But I got error: *** ERROR in MODEL command EFA factors are not allowed with ALGORITHM=INTEGRATION. EFA factors are declared with (*label). Montecarlo: names are y1-y40; nobservations = 500; nreps = 500; seed = 12345; generate = y1-y40(3 p); categorical = y1-y40;
Model population: f1 by y1-y5* .4; f2 by y6-y13* .5; f3 by y14-y21* .6; f4 by y22-y27* .7; f5 by y28-y33* .8; f6 by y34-y40* .9; y1-y40* 1; f1-f6*1;
ANALYSIS: ESTIMATOR = MLR; Model: f1 by y1-y5* .4 y6-y40*0 (*1); f2 by y1-y5*0 y6-y13* .5 y13-y40*0(*1); f3 by y1-y13*0 y14-y21* .6 y21-y40*0(*1); f4 by y1-y21*0 y22-y27* .7 y28-y40*0(*1); f5 by y1-y27*0 y28-y33* .8 y34-y40*0(*1); f6 by y1-y33*0 y34-y40* .9(*1); y1-y40* 1; f1-f6*1; OUTPUT: TECH9;
I think this can be done only with the WLSMV estimator. Try that.
WJCAO posted on Tuesday, September 20, 2011 - 6:27 pm
Thank you.But I know we can use MLR with categorical outcomes for regular EFA or for CFA.And in this simmulation, the aim of our research is to recover the difference between MLR and WLSMV. So, under this scenario, what can I do?
You would need to generate the data and save the data sets in one step and analyze each data set separately and put the results together yourself. Mplusautomation may be helpful. See the left-hand column fo the website for further information.
WJCAO posted on Wednesday, September 21, 2011 - 8:18 am
Thank you. I have do as you said.But I cannot generate data sucessfully.And the EXAMPLE 11.5 in Mplus User's Guide cannot do too. The data I got is not a four category indicators.
You need to give threshold population parameter values in MODEL POPULATION. See mcex7.6 where three-category variables are generated.
Ai Ye posted on Sunday, February 21, 2016 - 8:48 am
Hi, I want to conduct a simulation to evaluate the estimation of covariance effect in LTA under varying conditions: measurement quality,effect sizes, etc. I started with a simply model with 2 classes over 2 time point. Now I want to ask about how to specify the conditions in my model: 1.If I want to monitor the entropy level (0.4, 0.6, 0.8), should I change the value I used for conditional probability threshold? Is it that if I used a bigger value, say u11$1*50 versus u11$1*1, will I get a bigger entropy? Also, would the covariance effect on the latent class (c1#1 on x*.5) and the transition (c2#1 ON c1#1*2.5) matters to entropy? 2.In my model population, can I specify the transition probability of the simulated model, or the transitions supposed to be pretty random? As you see, I want to monitor the transition in a way that a large chunk of observation will transition from class 1 to class 2 whereas class 2 will stay at class 2, and I used the code “c2#1 ON c1#1*2.5”. When I specify as well c2#2 ON c1#2*2.5, I would get an error message, so I guess I only need to specify one transition of the four possibilities? But anyway, from the analysis output I got using this simulated model, the transition patterns and probabilities are so random (with almost equal percentage of pattern 1 1, 1 2, 2 2, 2 1). Did I do anything wrong in the code or it is impossible to control the transition pattern? Thank you!
Ai Ye posted on Sunday, February 21, 2016 - 8:50 am
I want to post the code I use to my previous question: Montecarlo: Names are u11-u16 u21-u26 x; Generate = u11-u16 u21-u26 (1); Categorical = u11-u16 u21-u26 ; Genclasses = c1(2) c2(2); Classes = c1(2) c2(2) ; Nobservations = 300; Seed = 891107; Nrep = 500; Repsave = ALL; Save = sim1*.dat; Analysis: Type = Mixture; Starts = 0; UCELLSIZE = 0; ALGORITHM=INTEGRATION; PROCESSORS=4; Model Population: %Overall% [x@0]; x@1; c1#1 on x*.5; [c1#1*0]; !Regression of transitions c2#1 ON c1#1*2.5; ! c1#1 is normative Model c1: %c1#1% !Specifying the class specific item thresholds c2#1 ON x*.5; [u11$1*1 u12$1*1 u13$1*1 u14$1*1 u15$1*1 u16$1*1]; %c1#2% c2#1 ON x*-5; [u11$1*1 u12$1*1 u13$1*1 u14$1*-1 u15$1*-1 u16$1*-1]; Model c2: %c2#1% [u11$1*1 u12$1*1 u13$1*1 u14$1*1 u15$1*1 u16$1*1]; %c2#2% [u11$1*1 u12$1*1 u13$1*1 u14$1*-1 u15$1*-1 u16$1*-1];
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Ai Ye posted on Monday, February 22, 2016 - 6:40 pm
Hi, one question regarding simulation in Latent transition analysis. If I want to get simulated data for a simple LTA scenario: two classes across two time points. How could I control the entropy level if measurement quality is one of the conditions I want to simulate? I read in another discussion board that it is through trial-and-error, but how so please? Also, in my model population, can I specify the transition probability of the simulated model, or the transitions supposed to be pretty random? Many thanks!
Entropy is directly influenced by the separation of the means/thresholds between the classes.
The transition probs are determined by the logit values you choose for c on c. See for instance web note 13.
Ai Ye posted on Friday, February 26, 2016 - 1:32 pm
Thanks for your response. I am able to generate a simulated model with a entropy level of medium (around 0.6) and high (around 0.8), by modifying the separation of thresholds between classes as well as covarianace effect on the class membership and transition. But I spent an incredibly long time to figure out a problem and I could not: why in the "model" output, the 95% coverage of the covariance effect is not even close to 1, and p-value is never significant no matter how large value I tried for the covariance coefficient?
Hi, I am trying to run a MC simulation of a multinomial regression because I have a very small number of cases and 'd like to estimate the bias. Although the model on the real data converges and have no warning message, the simulation gives me a FATAL ERROR: