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Problems with parameter estimation |
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When I run a growth mixture model I get figures of 999.000 in the Std and StdYX columns. Everything else looks OK. And if I'd run the program without the command Output: Standardized; I'd never have come across these figures. Does this indicate a problem or am I worrying without cause?? Peter Elliott |
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bmuthen posted on Friday, November 29, 2002 - 6:51 am
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999 means that it was not possible to compute the quantity. For example, if a variance is zero or negative, dividing by the square root of the variance in a standardization is not possble. |
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jbond posted on Tuesday, September 30, 2003 - 1:30 pm
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Hello. I am following example 25.1 in the mplus users guide, except it is a 4 class model and there are two continuous outcome variables, each measured at 4 time points. The problem seems to be with identification (the iterations terminate due to an ill-conditioned fisher information matrix). I've tried several combinations even with using the exact analog of example 25.1, with only volume of consumption as the time varying variable (that is, removing the aavariable) but all seemed to have the same problem. The distributions of volume and # aa meetings (applying the USEOBSERVATIONS selection criteria) although skewed, still have quite a bit of variation. Is there anything obviously wrong in the specification or is it likely a numerical identification problem? Thanks much for any input, Jason ----------------------------------- The syntax used was (sorry if this is not the best way to provide the info): TITLE: LCA For Number of AA Meetings; DATA: FILE IS "G:\Trajectories\AA Careers\aacareer.dat"; VARIABLE: NAMES = id dataset2 White Black Hisp Gender Age Income dsm4alc dsm4alc6 volcapt1 volcapt2 volcapt3 volcapt4 aapstyr1 aapstyr2 aapstyr3 aapstyr4 havspon1 havspon2 havspon3 havspon4 isspons1 isspons2 isspons3 isspons4 readlit1 readlit2 readlit3 reatlit4 spirawk1 spirawk2 spirawk3 spirawk4 aacapdt1 aacapdt2 aacapdt3 aacapdt4 aasca4t1 aasca4t2 aasca4t3 aasca4t4 aasca2t1 aasca2t2 aasca2t3 aasca2t4 absalct2 absalct3 absalct4 yrfq35t1 yrfq35t2 yrfq35t3 yrfq35t4 rg67 rh67 rq80b rg63 rh63; USEVARIABLES ARE aacapdt1 aacapdt2 aacapdt3 aacapdt4 volcapt1 volcapt2 volcapt3 volcapt4 id; USEOBSERVATIONS = dsm4alc EQ 1 and not(aacapdt1 == 0 and aacapdt2 == 0 and aacapdt3 == 0 and aacapdt4 == 0); Classes = C(4); MISSING ARE ALL (-9); IDvariable = id; ANALYSIS: TYPE = Mixture Missing; OUTPUT: TECH1; MODEL: %OVERALL% aacapdt1 with aacapdt2 aacapdt3 aacapdt4; aacapdt2 with aacapdt3 aacapdt4; aacapdt3 with aacapdt4; volcapt1 with volcapt2 volcapt3 volcapt4; volcapt2 with volcapt3 volcapt4; volcapt3 with volcapt4; %C#2% [aacapdt1-aacapdt4*50]; [volcapt1*2200 volcapt2*930 volcapt3*760 volcapt4*610]; %C#3% [aacapdt1-aacapdt4*100]; [volcapt1*2200 volcapt2*940 volcapt3*640 volcapt4*590]; %C#4% [aacapdt1-aacapdt4*200]; [volcapt1*2500 volcapt2*940 volcapt3*780 volcapt4*660]; and the corresponding output is: PROPORTION OF DATA PRESENT FOR Y Covariance Coverage AACAPDT1 AACAPDT2 AACAPDT3 AACAPDT4 VOLCAPT1 ________ ________ ________ ________ ________ AACAPDT1 0.980 AACAPDT2 0.709 0.719 AACAPDT3 0.693 0.634 0.703 AACAPDT4 0.634 0.578 0.614 0.644 VOLCAPT1 0.980 0.719 0.703 0.644 1.000 VOLCAPT2 0.733 0.719 0.653 0.594 0.743 VOLCAPT3 0.695 0.632 0.697 0.616 0.705 VOLCAPT4 0.632 0.576 0.612 0.642 0.642 Covariance Coverage VOLCAPT2 VOLCAPT3 VOLCAPT4 ________ ________ ________ VOLCAPT2 0.743 VOLCAPT3 0.653 0.705 VOLCAPT4 0.592 0.614 0.642 THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ILL-CONDITIONED FISHER INFORMATION MATRIX. CHANGE YOUR MODEL AND/OR STARTING VALUES. THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO A NON-POSITIVE DEFINITE FISHER INFORMATION MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.513D-13. THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THIS IS OFTEN DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. CHANGE YOUR MODEL AND/OR STARTING VALUES. PROBLEM INVOLVING PARAMETER 21. FINAL CLASS COUNTS AND PROPORTIONS OF TOTAL SAMPLE SIZE BASED ON ESTIMATED POSTERIOR PROBABILITIES Class 1 242.57932 0.49006 Class 2 188.33055 0.38047 Class 3 37.29576 0.07534 Class 4 26.79438 0.05413 CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY CLASS MEMBERSHIP Class Counts and Proportions Class 1 245 0.49495 Class 2 192 0.38788 Class 3 32 0.06465 Class 4 26 0.05253 Average Class Probabilities by Class 1 2 3 4 Class 1 0.911 0.073 0.013 0.003 Class 2 0.091 0.882 0.024 0.003 Class 3 0.045 0.035 0.921 0.000 Class 4 0.014 0.001 0.000 0.985 MODEL RESULTS Estimates CLASS 1 AACAPDT1 WITH AACAPDT2 490.326 AACAPDT3 115.399 AACAPDT4 73.934 AACAPDT2 WITH AACAPDT3 1719.602 AACAPDT4 845.029 AACAPDT3 WITH AACAPDT4 438.563 VOLCAPT1 WITH VOLCAPT2 ********* VOLCAPT3 ********* VOLCAPT4 ********* VOLCAPT2 WITH VOLCAPT3 ********* VOLCAPT4 ********* VOLCAPT3 WITH VOLCAPT4 ********* Means AACAPDT1 14.872 AACAPDT2 55.274 AACAPDT3 25.149 AACAPDT4 22.902 VOLCAPT1 1128.434 VOLCAPT2 697.542 VOLCAPT3 653.748 VOLCAPT4 588.990 Variances AACAPDT1 996.789 AACAPDT2 9602.278 AACAPDT3 2501.427 AACAPDT4 2405.750 VOLCAPT1 ********* VOLCAPT2 ********* VOLCAPT3 ********* VOLCAPT4 ********* CLASS 2 AACAPDT1 WITH AACAPDT2 490.326 AACAPDT3 115.399 AACAPDT4 73.934 AACAPDT2 WITH AACAPDT3 1719.602 AACAPDT4 845.029 AACAPDT3 WITH AACAPDT4 438.563 VOLCAPT1 WITH VOLCAPT2 ********* VOLCAPT3 ********* VOLCAPT4 ********* VOLCAPT2 WITH VOLCAPT3 ********* VOLCAPT4 ********* VOLCAPT3 WITH VOLCAPT4 ********* Means AACAPDT1 14.907 AACAPDT2 77.355 AACAPDT3 20.009 AACAPDT4 14.987 VOLCAPT1 3686.941 VOLCAPT2 1085.814 VOLCAPT3 1148.876 VOLCAPT4 801.556 Variances AACAPDT1 996.789 AACAPDT2 9602.278 AACAPDT3 2501.427 AACAPDT4 2405.750 VOLCAPT1 ********* VOLCAPT2 ********* VOLCAPT3 ********* VOLCAPT4 ********* CLASS 3 AACAPDT1 WITH AACAPDT2 490.326 AACAPDT3 115.399 AACAPDT4 73.934 AACAPDT2 WITH AACAPDT3 1719.602 AACAPDT4 845.029 AACAPDT3 WITH AACAPDT4 438.563 VOLCAPT1 WITH VOLCAPT2 ********* VOLCAPT3 ********* VOLCAPT4 ********* VOLCAPT2 WITH VOLCAPT3 ********* VOLCAPT4 ********* VOLCAPT3 WITH VOLCAPT4 ********* Means AACAPDT1 40.630 AACAPDT2 196.997 AACAPDT3 253.225 AACAPDT4 225.177 VOLCAPT1 2686.859 VOLCAPT2 1538.320 VOLCAPT3 563.325 VOLCAPT4 582.173 Variances AACAPDT1 996.789 AACAPDT2 9602.278 AACAPDT3 2501.427 AACAPDT4 2405.750 VOLCAPT1 ********* VOLCAPT2 ********* VOLCAPT3 ********* VOLCAPT4 ********* CLASS 4 AACAPDT1 WITH AACAPDT2 490.326 AACAPDT3 115.399 AACAPDT4 73.934 AACAPDT2 WITH AACAPDT3 1719.602 AACAPDT4 845.029 AACAPDT3 WITH AACAPDT4 438.563 VOLCAPT1 WITH VOLCAPT2 ********* VOLCAPT3 ********* VOLCAPT4 ********* VOLCAPT2 WITH VOLCAPT3 ********* VOLCAPT4 ********* VOLCAPT3 WITH VOLCAPT4 ********* Means AACAPDT1 216.803 AACAPDT2 114.169 AACAPDT3 55.023 AACAPDT4 20.042 VOLCAPT1 2505.316 VOLCAPT2 1437.330 VOLCAPT3 1040.784 VOLCAPT4 1229.785 Variances AACAPDT1 996.789 AACAPDT2 9602.278 AACAPDT3 2501.427 AACAPDT4 2405.750 VOLCAPT1 ********* VOLCAPT2 ********* VOLCAPT3 ********* VOLCAPT4 ********* LATENT CLASS REGRESSION MODEL PART Means C#1 2.203 C#2 1.950 C#3 0.331 TECHNICAL 1 OUTPUT PARAMETER SPECIFICATION FOR CLASS 1 NU AACAPDT1 AACAPDT2 AACAPDT3 AACAPDT4 VOLCAPT1 ________ ________ ________ ________ ________ 1 1 2 3 4 5 NU VOLCAPT2 VOLCAPT3 VOLCAPT4 ________ ________ ________ 1 6 7 8 THETA AACAPDT1 AACAPDT2 AACAPDT3 AACAPDT4 VOLCAPT1 ________ ________ ________ ________ ________ AACAPDT1 9 AACAPDT2 10 11 AACAPDT3 12 13 14 AACAPDT4 15 16 17 18 VOLCAPT1 0 0 0 0 19 VOLCAPT2 0 0 0 0 20 VOLCAPT3 0 0 0 0 22 VOLCAPT4 0 0 0 0 25 THETA VOLCAPT2 VOLCAPT3 VOLCAPT4 ________ ________ ________ VOLCAPT2 21 VOLCAPT3 23 24 VOLCAPT4 26 27 28 PARAMETER SPECIFICATION FOR CLASS 2 NU AACAPDT1 AACAPDT2 AACAPDT3 AACAPDT4 VOLCAPT1 ________ ________ ________ ________ ________ 1 29 30 31 32 33 NU VOLCAPT2 VOLCAPT3 VOLCAPT4 ________ ________ ________ 1 34 35 36 THETA AACAPDT1 AACAPDT2 AACAPDT3 AACAPDT4 VOLCAPT1 ________ ________ ________ ________ ________ AACAPDT1 9 AACAPDT2 10 11 AACAPDT3 12 13 14 AACAPDT4 15 16 17 18 VOLCAPT1 0 0 0 0 19 VOLCAPT2 0 0 0 0 20 VOLCAPT3 0 0 0 0 22 VOLCAPT4 0 0 0 0 25 THETA VOLCAPT2 VOLCAPT3 VOLCAPT4 ________ ________ ________ VOLCAPT2 21 VOLCAPT3 23 24 VOLCAPT4 26 27 28 |
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bmuthen posted on Tuesday, September 30, 2003 - 2:35 pm
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You are trying to do a mixture model with class-invariant covariance matrix and class-varying means. In line with Everitt-Hand's book referred to under the Mplus section with classic mixture examples, this is a difficult model to work with (unequal covariance matrices would be even harder). The LPA model in contrast, fixes the off-diagonal covariance matrix elements to zero. I notice that several of your variables are on a very high scale - your analysis might be simpler if you scale your variables down to variances in the 1-10 range. |
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Hello Bengt and Linda, I have been attempting to estimate a model in line with Example 7.26 (CFA with a non-parametric representation of a non-normal factor distribution) in the Mplus manual. I have tried estimating the model (which has 3 latent classes) exactly as with example 7.26, and I have also attempted to alter the factor means/intercepts for each of the latent classes using contrasts (e.g., -1, 0, 1) or by setting the first mean/intercept to "0" and freely estimating the rest. The models are estimating without error, but all of the models are returning standardized estimates of 999 for all loadings, means, variances, etc. I am aware that 999 = indeterminate, but I am not so sure that 999 = a problem with my models given your post on 11/02 in this thread: "999 means that it was not possible to compute the quantity. For example, if a variance is zero or negative, dividing by the square root of the variance in a standardization is not possble." Because Example 7.26 specifies the variance of the latent factor to be 0 in the overall model, does this mean that all models based on this example will not have estimable standardized coefficients? If, alternatively, all of the 999 values are indicative of a problem with my model, are my fit estimates still reliable/interpretable (e.g., BIC) given that standardized estimates are not estimable? Thanks, Jim |
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The 999's come from the fact that the variance of f is zero. These values are computed after model estimation and do not reflect anything about your model. This does not indicate any other problem. |
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Hello, I was trying to run a latent profile analysis for nestled models with six continuous indicator variables. I want to compare models with 2 to 5 classes with different parameter specifications. While using starting values 2000 200, running a model with within-and between-class-varying means and variances, but covariances set to zero worked fine. The only difficulty was that the best loglikelihood-value couldnt be replicated. When I try running a model with within-varying covariances (but not varying across classes) or a model with varying variances and covariances within and between classes (the most unrestricted model), I get this error message: THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.249D-10. PROBLEM INVOLVING PARAMETER 1. ONE OR MORE PARAMETERS WERE FIXED TO AVOID SINGULARITY OF THE INFORMATION MATRIX. THE SINGULARITY IS MOST LIKELY BECAUSE THE MODEL IS NOT IDENTIFIED, OR BECAUSE OF EMPTY CELLS IN THE JOINT DISTRIBUTION OF THE CATEGORICAL VARIABLES IN THE MODEL. THE FOLLOWING PARAMETERS WERE FIXED: {....} |
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THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ILL-CONDITIONED FISHER INFORMATION MATRIX. CHANGE YOUR MODEL AND/OR STARTING VALUES. THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO A NON-POSITIVE DEFINITE FISHER INFORMATION MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.237D-10. THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. {...} In the section "model results" or "model command with final estimates used as starting values", respectively, I received estimates for every parameter. no missing spots or -999-values or ******-symbols. Unfortunately, there is no Tech11, Tech13 and Tech14 Ouput and no model fit statistics like BIC to evaluate my model. Thank you for your help! Best regards Clarissa |
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