Greetings - I'm struggling to find an easy yet general way to extract model
coefficients (fixed effects) for use in graphing or related things. I've
examined -parmest- and similar commands but cannot seem to get what I want.
I fit an ANOVA model on some (fictitious) school-randomized trial data. What
I want is a way to extract the fixed-effect coefficients for the cond|school
effects, for when there are any number of schools (there are 20 in this
current case).
If using xtmixed, I can easily get random effect (for school) out with
. Predict re, reffects
Doing same in a fixed effect model is not obvious to me. Yes, -parmest- is
helpful but I cannot seem to get the "row" labels to work, as levels of
cond|school seem only given in the ANOVA regression table, not in
-parmest-.
I apologize in advance is if this is easy...
===============
. anova math cond cond|school, reg
Source | SS df MS Number of obs =
311
-------------+------------------------------ F( 19, 291) =
3.39
Model | 138604.165 19 7294.95607 Prob > F =
0.0000
Residual | 625751.327 291 2150.3482 R-squared =
0.1813
-------------+------------------------------ Adj R-squared =
0.1279
Total | 764355.492 310 2465.66288 Root MSE =
46.372
----------------------------------------------------------------------------
--
math Coef. Std. Err. t P>|t| [95% Conf.
Interval]
----------------------------------------------------------------------------
--
_cons 535.5 11.59296 46.19 0.000 512.6833
558.3167
cond
1 -19.88889 15.933 -1.25 0.213 -51.24742
11.46964
2 (dropped)
cond|school
1 2 15.90889 14.33449 1.11 0.268 -12.30354
44.12132
1 12 13.48889 18.2893 0.74 0.461 -22.50719
49.48497
1 23 12.61966 16.87825 0.75 0.455 -20.59925
45.83857
1 25 -34.96825 16.52453 -2.12 0.035 -67.491
-2.445508
1 31 -39.11111 16.52453 -2.37 0.019 -71.63386
-6.588365
1 35 -.8968254 16.52453 -0.05 0.957 -33.41957
31.62592
1 43 1.769841 14.89501 0.12 0.905 -27.54577
31.08545
1 45 -25.23611 15.933 -1.58 0.114 -56.59464
6.12242
1 70 -5.247475 17.74683 -0.30 0.768 -40.17589
29.68094
1 75 (dropped)
2 3 10.22 14.84623 0.69 0.492 -18.99961
39.43961
2 19 7.233333 16.66593 0.43 0.665 -25.56771
40.03438
2 24 -40.68182 15.23614 -2.67 0.008 -70.66881
-10.69482
2 27 -13.07143 16.97036 -0.77 0.442 -46.47163
20.32877
2 32 -38.94444 19.3216 -2.02 0.045 -76.97225
-.9166402
2 41 -11.5625 16.39492 -0.71 0.481 -43.83016
20.70516
2 44 -11.36667 16.66593 -0.68 0.496 -44.16771
21.43438
2 53 18.5 19.3216 0.96 0.339 -19.5278
56.5278
2 74 13.14286 16.97036 0.77 0.439 -20.25735
46.54306
2 86 (dropped)
----------------------------------------------------------------------------
--
===================
Thanks - Michael Oakes, UMN Epidemiology
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