Message/Author 

Andy Ross posted on Thursday, October 27, 2005  9:48 am



Hello I am running a standard mlogit regression model and need to get the following output: 1. The standard loglikelihood test so that i can see whether my final model is significantly better than a model with no predictors. 2. And the loglikelihood test for each of the predictor variables, so that i can see whether their inclusion significantly improves the model, or not. Many thanks for your support Andy 


How are you running this? The reason I ask is that you get a loglikelihood for the model being estimated automatically. Mplus does not provide number 2 automatically. 

Andy Ross posted on Friday, October 28, 2005  3:55 am



Many thanks for your reply. This is the input file for my model: VARIABLE: NAMES ARE u1u16; USEVARIABLES ARE u1u5; NOMINAL IS u2; CATEGORICAL ARE u1 u4; MODEL: u2#1 u2#2 u2#3 u2#4 u2#5 ON u1 u3 u4 u5; And the output: TESTS OF MODEL FIT Loglikelihood H0 Value 10483.204 Information Criteria Number of Free Parameters 27 Akaike (AIC) 21020.408 Bayesian (BIC) 21191.368 SampleSize Adjusted BIC 21105.573 (n* = (n + 2) / 24) Many thanks Andy 


So you get number 1 from this output and number 2 is not available. 

Andy Ross posted on Monday, November 07, 2005  5:12 am



Many thanks for your reply. Maybe i'm reading the output wrong but i was hoping for a significance test/pvalue telling me whether the full model was significantly better than the model with no predictors. Do i need to calculate this by hand from Loglikelihood HO value? And if so, how would i go about doing this? My apologies if this sounds a little elementary. I have asked around but everyone here is used to the standard SPSS/Stata output... Many thanks Andy 


You should run two models both with the covariates in the model: (1) a model with the slopes of the covariates free and (2) a model with the slopes of the covariates fixed to zero. You can then compute the loglikelihood difference and 2 time that is the chisquare difference. 

Manuel posted on Monday, January 09, 2006  6:00 am



Hello, I have a similar question: I am running a standard discrete time survival analysis. Without covariates I obtain a LL which is identical to that of other programs (i.e., Systat etc.). However, after including a single continuous covariate (proportional hazard assumption) the values differ vastly  how come (Mplus: 540.28 vs. SPSS/Systat:1167.46)? THANK YOU VERY MUCH IN ADVANCE!!! 


When there are covariates in the model, the loglikelihood values are on a different scale. This is why to compare nested models you need the covariates in both models. 

Manuel posted on Monday, January 09, 2006  7:35 am



thank you very much for your prompt reply! However, please let me rephrase my question: Should the chisquare diff test (effect vs. no effect of the covariate; covariate is part of both models but fixed to zero in the more restrictive model both models are nested) be identical to the chisquare diff test in the cox regression model (using any other program)? Part of the reason I am asking is that Muthén & Masyn (2005, p. 36) note that Mplus can handle continuous covariates, while the traditional loglinear framework only allows categorical variables. I was not aware of that fact  but that should be reflected in the LL, shoud not it? Thank you so much for your help! 


I don't know if the loglikelihoods would be the same. The Mplus loglikelihood is for y given x. If the cox regression loglikelihood is for y and x, then they would be different. I think the difference in the loglikelihoods would be the same however. Also, the Cox regression model usually means continuoustime survival. Mplus estimates a discretetime survival model. I am assuming you are doing a discretetime survival model. Yes, that should be reflected in the loglikelihood. 

Manuel posted on Monday, January 09, 2006  9:37 am



that helps  thank you very much! 

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