Hi to everybody,
i 'm trying to do mlogit. For me it's the first time
where a can assume these values (1,2,3 e 4); ITEM3 (0,1)
I would like to check if it is respected "II Independence of Irrelevant Alternatives". I try this code. Is it correct?
and obtain this output
p is equal to 0.9994 so was it respected II Independence of Irrelevant Alternatives?
thanks in advanced to everybody
i 'm trying to do mlogit. For me it's the first time
Code:
mlogit a ITEM3 , baseoutcome(3) rrr
where a can assume these values (1,2,3 e 4); ITEM3 (0,1)
I would like to check if it is respected "II Independence of Irrelevant Alternatives". I try this code. Is it correct?
Code:
mlogit Diagnosi_num ITEM3 , baseoutcome(3) rrr estimates store allcats mlogit Diagnosi_num ITEM3 if Diagnosi_num!=2 , baseoutcome(3) rrr hausman . allcats, alleqs constant
HTML Code:
mlogit a ITEM3 , baseoutcome(3) rrr Iteration 0: Log likelihood = -86.308789 Iteration 1: Log likelihood = -85.826252 Iteration 2: Log likelihood = -85.717961 Iteration 3: Log likelihood = -85.694522 Iteration 4: Log likelihood = -85.68864 Iteration 5: Log likelihood = -85.687493 Iteration 6: Log likelihood = -85.687307 Iteration 7: Log likelihood = -85.687286 Iteration 8: Log likelihood = -85.687282 Multinomial logistic regression Number of obs = 92 LR chi2(2) = 1.24 Prob > chi2 = 0.5371 Log likelihood = -85.687282 Pseudo R2 = 0.0072 ------------------------------------------------------------------------------ a | RRR Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- 1 | ITEM3 | 1.58e-06 .0016328 -0.01 0.990 0 . _cons | .2962833 .0843346 -4.27 0.000 .1695976 .5176005 -------------+---------------------------------------------------------------- 2 | ITEM3 | 1.419923 1.78003 0.28 0.780 .1216717 16.57065 _cons | .351858 .0938537 -3.92 0.000 .2086028 .5934919 -------------+---------------------------------------------------------------- 3 | (base outcome) ------------------------------------------------------------------------------ Note: _cons estimates baseline relative risk for each outcome. . . estimates store allcats . . mlogit a ITEM3 if a!=2 , baseoutcome(3) rrr Iteration 0: Log likelihood = -38.138846 Iteration 1: Log likelihood = -37.672198 Iteration 2: Log likelihood = -37.632994 Iteration 3: Log likelihood = -37.629057 Iteration 4: Log likelihood = -37.62832 Iteration 5: Log likelihood = -37.628158 Iteration 6: Log likelihood = -37.628119 Iteration 7: Log likelihood = -37.628111 Iteration 8: Log likelihood = -37.628109 Multinomial logistic regression Number of obs = 72 LR chi2(1) = 1.02 Prob > chi2 = 0.3122 Log likelihood = -37.628109 Pseudo R2 = 0.0134 ------------------------------------------------------------------------------ a | RRR Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- 1 | ITEM3 | 7.37e-07 .0011153 -0.01 0.993 0 . _cons | .2962638 .0843303 -4.27 0.000 .169585 .5175707 -------------+---------------------------------------------------------------- 3 | (base outcome) ------------------------------------------------------------------------------ Note: _cons estimates baseline relative risk for each outcome. . . hausman . allcats, alleqs constant Note: the rank of the differenced variance matrix (1) does not equal the number of coefficients being tested (2); be sure this is what you expect, or there may be problems computing the test. Examine the output of your estimators for anything unexpected and possibly consider scaling your variables so that the coefficients are on a similar scale. ---- Coefficients ---- | (b) (B) (b-B) sqrt(diag(V_b-V_B)) | . allcats Difference Std. err. -------------+---------------------------------------------------------------- ITEM3 | -14.12059 -13.35799 -.7625974 1105.455 _cons | -1.216505 -1.216439 -.0000661 .0015597 ------------------------------------------------------------------------------ b = Consistent under H0 and Ha; obtained from mlogit. B = Inconsistent under Ha, efficient under H0; obtained from mlogit. Test of H0: Difference in coefficients not systematic chi2(1) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 0.00 Prob > chi2 = 0.9994 .
thanks in advanced to everybody
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