Hello,
I am a little confused about the "nocons" command. As I understand, it will drop the constant term. I am using ibn. since I want keep all the variables in the model, bu the requires using "noconstant". When I run this command:
I get the following results which do not have a _cons, but the user-created "asdoc" command reports this term: lns1_1_1:_cons. However, I do not understand what this means, and if it needs to be reported or not.
On a final note, I have heard it is important to include a constant term in the results table, but if I am using "nocons", what should I do? Also, does it matter if the constant term is not significant although the predictors and the model's overall fit are significant?
Any thoughts and/or suggestions are appreciated.
Cheers,
Ray
Stata 16MP
I am a little confused about the "nocons" command. As I understand, it will drop the constant term. I am using ibn. since I want keep all the variables in the model, bu the requires using "noconstant". When I run this command:
Code:
global country "lngdppc_lag ibn.polity_demo2" global individual "male age age_sq married divorced religious income college_edu" global opt "" asdoc meqrlogit fight $individual $country $opt, noconstant || ccodecow:, cov(un), nested save(H3 - The Effect of Level of Democraticness on Willingness to Fight) replace setstars(***@.001, **@.01, *@.05) tzok dec(3) cnames(Model 1) title(H3 - The Effect of Level of Democraticness on Willingness to Fight)
Code:
Mixed-effects logistic regression Number of obs = 310,418
Group variable: ccodecow Number of groups = 92
Obs per group:
min = 371
avg = 3,374.1
max = 13,637
Integration points = 7 Wald chi2(12) = 9503.87
Log likelihood = -162742.26 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
fight | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
male | .6906793 .0088812 77.77 0.000 .6732725 .7080861
age | .0137731 .0013196 10.44 0.000 .0111867 .0163595
age_sq | -.0002313 .0000141 -16.36 0.000 -.000259 -.0002036
married | .2968113 .0887262 3.35 0.001 .1229111 .4707115
divorced | .175669 .0887919 1.98 0.048 .0016401 .3496979
religious | -.6418332 .0166147 -38.63 0.000 -.6743975 -.609269
income | -.0023552 .0016195 -1.45 0.146 -.0055294 .0008189
college_edu | -.0719106 .0100945 -7.12 0.000 -.0916955 -.0521258
lngdppc_lag | -.6262017 .0173671 -36.06 0.000 -.6602406 -.5921628
|
polity_demo2 |
1 | 6.699032 .2104093 31.84 0.000 6.286637 7.111426
2 | 6.590388 .2128285 30.97 0.000 6.173252 7.007524
3 | 6.451505 .2145495 30.07 0.000 6.030996 6.872014
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
ccodecow: Identity |
var(_cons) | 1.02656 .1550357 .7635409 1.380183
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 25271.80 Prob >= chibar2 = 0.0000
Any thoughts and/or suggestions are appreciated.
Cheers,
Ray
Stata 16MP

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