Dear Clive
I tried
.mfx,
> predict(p outcome()) at(mean)
as you suggest below
and
. mfx predict (outcome (2))
but I got
unknown mfx subcommand predict
r(198);
Many Thanks
--- Clive Nicholas <[email protected]>
wrote:
> Peter Harper wrote:
>
> > I have run -margeff-, on the three responders
> > categories of price being 'very' 'fairly' and
> 'not'
> > important. But the z-statistic of one of the
> variables
> > in the 'fairly important' category is 998.42. For
> the
> > other two categories they were around 2.00. Can
> anyone
> > tell me how to correctly interpret the z-statistic
> of
> > 998.42 please?
>
> Looks a bit suss to me, but it's difficult to advise
> you further without
> seeing some actual output. Also, you may find it
> more useful to use -mfx,
> predict(p outcome()) at(mean)-. You should find that
> your results are
> slightly different using -mfx-. An example:
>
> . use http://www.gseis.ucla.edu/courses/data/hsb2,
> clear
> (highschool and beyond (200 cases))
>
> . ologit ses female race read write math
>
> Iteration 0: log likelihood = -210.58254
> Iteration 1: log likelihood = -197.51869
> Iteration 2: log likelihood = -197.36092
> Iteration 3: log likelihood = -197.36061
>
> Ordered logit estimates
> Number of obs = 200
> LR
> chi2(5) = 26.44
> Prob
> > chi2 = 0.0001
> Log likelihood = -197.36061
> Pseudo R2 = 0.0628
>
----------------------------------------------------------------------------
> ses | Coef. Std. Err. z P>|z|
> [95% Conf. Interval]
>
-----------+----------------------------------------------------------------
> female | -.4909873 .2952412 -1.66 0.096
> -1.069649 .0876748
> race | .2350773 .137125 1.71 0.086
> -.0336828 .5038373
> read | .0311981 .0193404 1.61 0.107
> -.0067085 .0691046
> write | .0118174 .0209855 0.56 0.573
> -.0293134 .0529481
> math | .0228895 .0209798 1.09 0.275
> -.0182301 .0640091
>
-----------+----------------------------------------------------------------
> _cut1 | 2.692794 .9248859
> (Ancillary parameters)
> _cut2 | 4.99739 .9796558
>
----------------------------------------------------------------------------
>
> . margeff
>
> Marginal effects on Prob(ses) after ologit
>
----------------------------------------------------------------------------
> ses | Coef. Std. Err. z P>|z|
> [95% Conf. Interval]
>
-----------+----------------------------------------------------------------
> low |
> female | .0803274 .119315 0.67 0.501
> -.1535258 .3141805
> race | -.0388374 .0480915 -0.81 0.419
> -.1330949 .0554202
> read | -.0051543 .0068777 -0.75 0.454
> -.0186343 .0083258
> write | -.0019524 .0037731 -0.52 0.605
> -.0093475 .0054428
> math | -.0037816 .0049511 -0.76 0.445
> -.0134856 .0059224
>
-----------+----------------------------------------------------------------
> middle |
> female | -.0802519 .196229 -0.41 0.683
> -.4648537 .30435
> race | .1212574 .0938469 1.29 0.196
> -.0626791 .305194
> read | .1593779 .0124249 12.83 0.000
> .1350254 .1837303
> write | .1630016 .0048255 33.78 0.000
> .1535437 .1724595
> math | .1609314 .00923 17.44 0.000
> .1428409 .1790218
>
-----------+----------------------------------------------------------------
> high |
> female | -.0930442 .1030793 -0.90 0.367
> -.2950758 .1089874
> race | .0439537 .0564441 0.78 0.436
> -.0666746 .1545821
> read | .0058333 .0072293 0.81 0.420
> -.0083359 .0200025
> write | .0022096 .0049231 0.45 0.654
> -.0074394 .0118586
> math | .0042798 .006634 0.65 0.519
> -.0087226 .0172821
>
----------------------------------------------------------------------------
>
> . mfx, predict(p outcome(1)) at(mean)
>
> Marginal effects after ologit
> y = Pr(ses==1) (predict, p outcome(1))
> = .21347808
>
----------------------------------------------------------------------------
> variab | dy/dx Std. Err. z P>|z| [
> 95% C.I. ] X
>
-------+--------------------------------------------------------------------
> female*| .0813982 .04854 1.68 0.094
> -.013747 .176543 .545
> race | -.0394707 .02316 -1.70 0.088
> -.084865 .005923 3.43
> read | -.0052383 .00326 -1.61 0.108
> -.011619 .001143 52.23
> write | -.0019842 .00352 -0.56 0.573
> -.008889 .004921 52.775
> math | -.0038433 .00352 -1.09 0.275
> -.010748 .003061 52.645
>
----------------------------------------------------------------------------
> (*) dy/dx is for discrete change of dummy variable
> from 0 to 1
>
> . mfx, predict(p outcome(2)) at(mean)
>
> Marginal effects after ologit
> y = Pr(ses==2) (predict, p outcome(2))
> = .51768066
>
----------------------------------------------------------------------------
> variab | dy/dx Std. Err. z P>|z| [
> 95% C.I. ] X
>
-------+--------------------------------------------------------------------
> female*| .0159065 .01619 0.98 0.326
> -.015826 .047638 .545
> race | -.0067374 .0071 -0.95 0.343
> -.020658 .007183 3.43
> read | -.0008942 .00097 -0.92 0.358
> -.002799 .001011 52.23
> write | -.0003387 .00068 -0.50 0.617
> -.001665 .000988 52.775
> math | -.000656 .00084 -0.78 0.436
> -.002307 .000995 52.645
>
----------------------------------------------------------------------------
> (*) dy/dx is for discrete change of dummy variable
> from 0 to 1
>
> . mfx, predict(p outcome(3)) at(mean)
>
> Marginal effects after ologit
> y = Pr(ses==3) (predict, p outcome(3))
> = .26884126
>
----------------------------------------------------------------------------
> variab | dy/dx Std. Err. z P>|z| [
> 95% C.I. ] X
>
-------+--------------------------------------------------------------------
> female*| -.0973046 .05883 -1.65 0.098
> -.212612 .018002 .545
> race | .0462081 .02689 1.72 0.086
> -.006496 .098913 3.43
> read | .0061325 .0038 1.61 0.107
> -.001323 .013588 52.23
> write | .0023229 .00413 0.56 0.574
> -.005767 .010413 52.775
> math | .0044993 .00413 1.09 0.275
> -.003586 .012584 52.645
>
----------------------------------------------------------------------------
> (*) dy/dx is for discrete change of dummy variable
> from 0 to 1
>
> In this example, we can see that the elasticities
> (dy/dx) are very
> similiar to those given in -margeff- for low and
> high values of
> socioeconomic status (and, indeed, the pattern is
> identical), but are
> completely different at 'middle' values of SES.
>
> Note that -mfx- provides you with some extra
> information here: the
> (overall) predicted probability of Y = 1, 2 or 3
> given the X-variables set
> at their mean values. In this example, respondents
> with 'average' social
> and educational characteristics are much more likely
> to be in the 'middle'
> SES category than in the other two.
>
> I hope that helps.
>
> CLIVE NICHOLAS |t: 0(044)7903 397793
> Politics |e: [email protected]
> Newcastle University |http://www.ncl.ac.uk/geps
>
> *
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