Dear Statalist,
I'm having trouble to derive marginal effects after computing a Generalized Ordered Logit (correcting by the parallel-lines assumption violated using the gologit2 command).
I have three levels (1, 2 and 3) of my response variable, then, after running the gologit2 command with the autofit lrforce option, the output gives me the estimates of the cumulative effects (1 vs 2 and 3; and 1 and 2 vs 3).
This is a sample of how the output looks like:
Finally, to derive the marginal effects, I'm using the margins command as follows:
My question is: How can I derive the corresponding marginal effects of the Generalized Ordered Logit model of the first table, e.g., two different sets of marginal per explanatory variable (1 vs 2 and 3; and 1 and 2 vs 3)? As far as I can see the margins command (second table) is computing average values which are the same for every level of the response variable.
Thanks in advance.
I'm having trouble to derive marginal effects after computing a Generalized Ordered Logit (correcting by the parallel-lines assumption violated using the gologit2 command).
I have three levels (1, 2 and 3) of my response variable, then, after running the gologit2 command with the autofit lrforce option, the output gives me the estimates of the cumulative effects (1 vs 2 and 3; and 1 and 2 vs 3).
This is a sample of how the output looks like:
HTML Code:
gologit2 response pop_density pp_college pp_no_citizen pp_no_car income_Gini, autofit lrforce ------------------------------------------------------------------------------ Testing parallel lines assumption using the .05 level of significance... Step 1: Constraints for parallel lines imposed for pp_no_citizen (P Value = 0.8867) Step 2: Constraints for parallel lines imposed for income_Gini (P Value = 0.7025) Step 3: Constraints for parallel lines are not imposed for pop_density (P Value = 0.04162) pp_college (P Value = 0.00746) pp_no_car (P Value = 0.02939) Wald test of parallel lines assumption for the final model: ( 1) [1]pp_no_citizen - [2]pp_no_citizen = 0 ( 2) [1]income_Gini - [2]income_Gini = 0 chi2( 2) = 0.17 Prob > chi2 = 0.9203 An insignificant test statistic indicates that the final model does not violate the proportional odds/ parallel lines assumption If you re-estimate this exact same model with gologit2, instead of autofit you can save time by using the parameter pl(pp_no_citizen income_Gini) ------------------------------------------------------------------------------ Generalized Ordered Logit Estimates Number of obs = 1182 LR chi2(8) = 223.80 Prob > chi2 = 0.0000 Log likelihood = -1056.791 Pseudo R2 = 0.0957 ( 1) [1]pp_no_citizen - [2]pp_no_citizen = 0 ( 2) [1]income_Gini - [2]income_Gini = 0 ------------------------------------------------------------------------------- response | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- 1 | pop_density | -.0005147 .0002311 -2.23 0.026 -.0009676 -.0000619 pp_college | -.0821576 .0159082 -5.16 0.000 -.1133371 -.0509781 pp_no_citizen | .0412044 .0049467 8.33 0.000 .031509 .0508997 pp_no_car | .0312774 .0307727 1.02 0.309 -.0290359 .0915907 income_Gini | 2.607472 2.304695 1.13 0.258 -1.909648 7.124592 _cons | -2.476667 .8911029 -2.78 0.005 -4.223196 -.7301372 --------------+---------------------------------------------------------------- 2 | pop_density | -.0033817 .0014051 -2.41 0.016 -.0061356 -.0006278 pp_college | -.1726428 .0338568 -5.10 0.000 -.239001 -.1062847 pp_no_citizen | .0412044 .0049467 8.33 0.000 .031509 .0508997 pp_no_car | -.0691822 .0464367 -1.49 0.136 -.1601966 .0218321 income_Gini | 2.607472 2.304695 1.13 0.258 -1.909648 7.124592 _cons | -3.368429 .9342685 -3.61 0.000 -5.199562 -1.537297 -------------------------------------------------------------------------------
Finally, to derive the marginal effects, I'm using the margins command as follows:
HTML Code:
margins, dydx(*) predict(xb) Average marginal effects Number of obs = 1182 Model VCE : OIM Expression : Linear prediction, response==1, predict(xb) dy/dx w.r.t. : pop_density pp_college pp_no_citizen pp_no_car income_Gini ------------------------------------------------------------------------------- | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- pop_density | -.0005147 .0002311 -2.23 0.026 -.0009676 -.0000619 pp_college | -.0821576 .0159082 -5.16 0.000 -.1133371 -.0509781 pp_no_citizen | .0412044 .0049467 8.33 0.000 .031509 .0508997 pp_no_car | .0312774 .0307727 1.02 0.309 -.0290359 .0915907 income_Gini | 2.607472 2.304695 1.13 0.258 -1.909648 7.124592 -------------------------------------------------------------------------------
My question is: How can I derive the corresponding marginal effects of the Generalized Ordered Logit model of the first table, e.g., two different sets of marginal per explanatory variable (1 vs 2 and 3; and 1 and 2 vs 3)? As far as I can see the margins command (second table) is computing average values which are the same for every level of the response variable.
Thanks in advance.
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