if I estimate two mlogit commands with the same outcome (dependent) variable but different sets of covariates, I will, by default, get from e(table) the name of the outcome variable as the column header (see below); I want to change that to show something else so the columns will be easier to refer to; the following simple (and unrealistic) example shows the result and my attempt to change one of the headers using the eqrecode option:
here, I attempted to change the first column header to "mi" but no change was made
I think that my problem is that I don't understand what the equation name is for mlogit, but it could be something else???
In case it makes a difference, in my actual data, I will have one column for the data with missing values and one column that comes after multiple imputation and using "mi estimate"
Code:
. sysuse auto
(1978 automobile data)
. mlogit rep78 price
Iteration 0: Log likelihood = -93.692061
Iteration 1: Log likelihood = -93.113707
Iteration 2: Log likelihood = -92.974678
Iteration 3: Log likelihood = -92.970763
Iteration 4: Log likelihood = -92.970763
Multinomial logistic regression Number of obs = 69
LR chi2(4) = 1.44
Prob > chi2 = 0.8368
Log likelihood = -92.970763 Pseudo R2 = 0.0077
------------------------------------------------------------------------------
rep78 | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
1 |
price | -.000549 .0007284 -0.75 0.451 -.0019766 .0008786
_cons | .1169081 3.421563 0.03 0.973 -6.589231 6.823048
-------------+----------------------------------------------------------------
2 |
price | -.0000547 .0001431 -0.38 0.703 -.0003352 .0002259
_cons | -.9834618 .952914 -1.03 0.302 -2.851139 .8842154
-------------+----------------------------------------------------------------
3 | (base outcome)
-------------+----------------------------------------------------------------
4 |
price | -.000041 .0001027 -0.40 0.690 -.0002424 .0001603
_cons | -.2545298 .7025195 -0.36 0.717 -1.631443 1.122383
-------------+----------------------------------------------------------------
5 |
price | -.0000622 .0001279 -0.49 0.627 -.0003129 .0001884
_cons | -.6201496 .8475732 -0.73 0.464 -2.281363 1.041063
------------------------------------------------------------------------------
. est store m1
. mlogit rep78 mpg
Iteration 0: Log likelihood = -93.692061
Iteration 1: Log likelihood = -86.581485
Iteration 2: Log likelihood = -85.767758
Iteration 3: Log likelihood = -85.752385
Iteration 4: Log likelihood = -85.752375
Iteration 5: Log likelihood = -85.752375
Multinomial logistic regression Number of obs = 69
LR chi2(4) = 15.88
Prob > chi2 = 0.0032
Log likelihood = -85.752375 Pseudo R2 = 0.0847
------------------------------------------------------------------------------
rep78 | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
1 |
mpg | .0708122 .1471461 0.48 0.630 -.2175888 .3592132
_cons | -4.137144 3.15707 -1.31 0.190 -10.32489 2.0506
-------------+----------------------------------------------------------------
2 |
mpg | -.0164251 .0926724 -0.18 0.859 -.1980597 .1652096
_cons | -1.005118 1.822129 -0.55 0.581 -4.576426 2.56619
-------------+----------------------------------------------------------------
3 | (base outcome)
-------------+----------------------------------------------------------------
4 |
mpg | .0958626 .0633329 1.51 0.130 -.0282676 .2199927
_cons | -2.474187 1.341131 -1.84 0.065 -5.102756 .1543813
-------------+----------------------------------------------------------------
5 |
mpg | .2477469 .0764076 3.24 0.001 .0979908 .397503
_cons | -6.653164 1.841794 -3.61 0.000 -10.26301 -3.043314
------------------------------------------------------------------------------
r; t=0.03 8:49:53
. est store m2
. etable, estimates(m1 m2) showeq eqrecode(m1 = mi) cstat(_r_b) cstat(_r_p)
------------------------------------
rep78 rep78
------------------------------------
1
Price -0.001
0.45
Mileage (mpg) 0.071
0.63
Intercept 0.117 -4.137
0.97 0.19
2
Price -0.000
0.70
Mileage (mpg) -0.016
0.86
Intercept -0.983 -1.005
0.30 0.58
4
Price -0.000
0.69
Mileage (mpg) 0.096
0.13
Intercept -0.255 -2.474
0.72 0.07
5
Price -0.000
0.63
Mileage (mpg) 0.248
0.00
Intercept -0.620 -6.653
0.46 0.00
Number of observations 69 69
------------------------------------
I think that my problem is that I don't understand what the equation name is for mlogit, but it could be something else???
In case it makes a difference, in my actual data, I will have one column for the data with missing values and one column that comes after multiple imputation and using "mi estimate"

Comment