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Cannot find "results in probability s... |
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Reem Saeed posted on Wednesday, October 16, 2013 - 6:09 am
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Hello, My sample size consists of 118 areas. These are considered IDs and I have included them in the analysis section as auxiliary. Although the best likelihood estimate is replicated and the model terminated normally. I still get this error: 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 -0.967D-16. PROBLEM INVOLVING PARAMETER 83. Can this message be ignored? I also cannot see the results in probability scale to be able to label the classes appropriately. Any help and advice is very much appreciated Thanks |
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Please send the output and your license number to support@statmodel.com. |
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Reem Saeed posted on Monday, October 21, 2013 - 5:12 am
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Dear Linda, I have now come up with results from my LPA. Please note that my variables are proportions, therefore they range between 0 and 1. I have done a 3 class and a 2 class model. The 3 class model has a better fit according to the BIC: 2 class model BIC --> -9965.662 3 class model BIC --> -10613.238 However, for both models I get the following 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 -0.140D-16. PROBLEM INVOLVING PARAMETER 16. My first question is: 1. Is it alright or normal to get negative AIC and BIC values? 2. Can this message be ignored? Please note that first I had a similar message involving parameter 22, I deleted it and re-ran the analysis. I still get the same message, but with a different parameter. 3. My variables are slightly skewed, could this be causing it? What are the implications of running a LPA with slightly skewed continous variables? and what would you suggest to solve this issue? Many thanks, Reem |
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1. Yes. 2. Please send the output and your license number to support@statmodel.com. 3. This is not a problem for mixture models. |
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