. do "C:\Users\DEFAUL~1.SID\AppData\Local\Temp\STD00000000.tmp"

. * do-file for lecture 7 of VHM 802, Winter 2014
. version 13

. set more off

. cd "f:\vhm\vhm802\data_csv"
f:\vhm\vhm802\data_csv

.
. insheet using ch08ta6.csv, clear /* Example 8.6 */
(3 vars, 12 obs)

. anova tfaa r50 r21 r50#r21 /* same with r50##r21 only */

Number of obs = 12 R-squared = 0.5459
Root MSE = .301177 Adj R-squared = 0.3756

Source | Partial SS df MS F Prob > F

+

Model | .872314116 3 .290771372 3.21 0.0834
|
r50 | .656136386 1 .656136386 7.23 0.0275
r21 | .21440139 1 .21440139 2.36 0.1627
r50#r21 | .00177634 1 .00177634 0.02 0.8922
|
Residual | .725662525 8 .090707816

+

Total | 1.59797664 11 .145270604

. regress /* Stata parametrization; note different P-values! */

Source | SS df MS Number of obs = 12

+
F( 3, 8) = 3.21
Model | .872314116 3 .290771372 Prob > F = 0.0834
Residual | .725662525 8 .090707816 R-squared = 0.5459

+
Adj R-squared = 0.3756
Total | 1.59797664 11 .145270604 Root MSE = .30118

+

--------

tfaa | Coef. Std. Err. t P>|t| [95% Con f. Interval]

+

--------

1.r50 | .4433333 .2459103 1.80 0.109 -.1237369 1.010403
1.r21 | .243 .2459103 0.99 0.352 -.3240702 .8100702
|
r50#r21 |
1 1 | .0486668 .3477697 0.14 0.892 -.7532916 .8506251
|
_cons | 1.709333 .1738848 9.83 0.000 1.308354 2.110313

+

--------

. * interaction plot
. margins r50#r21

Adjusted predictions Number of obs = 12

Expression : Linear prediction, predict()

+

--------

| Delta-method
| Margin Std. Err. t P>|t| [95% Con f. Interval]

+

--------

r50#r21 |
0 0 | 1.709333 .1738848 9.83 0.000 1.308354 2.110313
0 1 | 1.952333 .1738848 11.23 0.000 1.551354 2.353312
1 0 | 2.152667 .1738848 12.38 0.000 1.751687 2.553646
1 1 | 2.444333 .1738848 14.06 0.000 2.043354 2.845313

+

--------

. marginsplot, noci /* CIs often look messy */

Variables that uniquely identify margins: r50 r21

. * contrasts for SS decomposition computed manually
. generate tx=10*r50+r21 /* combined tx variable */

. anova tfaa tx

Number of obs = 12 R-squared = 0.5459
Root MSE = .301177 Adj R-squared = 0.3756

Source | Partial SS df MS F Prob > F

+

Model | .872314116 3 .290771372 3.21 0.0834
|
tx | .872314116 3 .290771372 3.21 0.0834
|
Residual | .725662525 8 .090707816

+

Total | 1.59797664 11 .145270604

. lincom 0.tx+1.tx-10.tx-11.tx /* r50 */

( 1) 0b.tx + 1.tx - 10.tx - 11.tx = 0

+

--------

tfaa | Coef. Std. Err. t P>|t| [95% Con f. Interval]

+

--------

(1) | -.9353334 .3477697 -2.69 0.028 -1.737292 -.133375

+

--------

. lincom 0.tx+10.tx-1.tx-11.tx /* r21 */

( 1) 0b.tx - 1.tx + 10.tx - 11.tx = 0

+

--------

tfaa | Coef. Std. Err. t P>|t| [95% Con f. Interval]

+

--------

(1) | -.5346667 .3477697 -1.54 0.163 -1.336625 .2672916

+

--------

. lincom 0.tx-1.tx-10.tx+11.tx /* r50*r21 */

( 1) 0b.tx - 1.tx - 10.tx + 11.tx = 0

+

--------

tfaa | Coef. Std. Err. t P>|t| [95% Con f. Interval]

+

--------

(1) | .0486668 .3477697 0.14 0.892 -.7532916 .8506251

+

--------

.
. insheet using ch08ta7.csv, clear /* Example 8.8 */
(5 vars, 54 obs)

. generate lnfault=ln(fault)

. anova lnfault alg##seq##size##alloc /* DFE=0 */

Number of obs = 54 R-squared = 1.0000
Root MSE = 0 Adj R-squared =

Source | Partial SS df MS F Prob > F

+

Model | 173.593158 53 3.27534261
|
alg | 2.50183751 1 2.50183751
seq | 24.6392539 2 12.3196269
alg#seq | .017636931 2 .008818465
size | 41.6916509 2 20.8458254
alg#size | .022214508 2 .011107254
seq#size | .828957455 4 .207239364
alg#seq#size | .014564023 4 .003641006
alloc | 92.6973005 2 46.3486503
alg#alloc | .060039547 2 .030019774
seq#alloc | 9.51047037 4 2.37761759
alg#seq#alloc | .02600767 4 .006501917
size#alloc | .504304845 4 .126076211
alg#size#alloc | .004001546 4 .001000387
seq#size#alloc | 1.05212232 8 .13151529
alg#seq#size#alloc | .022796194 8 .002849524
|
Residual | 0 0

+

Total | 173.593158 53 3.27534261

. anova lnfault alg##seq##size alg##seq##alloc alg##size##alloc seq##size##alloc

Number of obs = 54 R-squared = 0.9999
Root MSE = .053381 Adj R-squared = 0.9991

Source | Partial SS df MS F Prob > F

+

Model | 173.570362 45 3.85711916 1353.60 0.0000
|
alg | 2.50183751 1 2.50183751 877.98 0.0000
seq | 24.6392539 2 12.3196269 4323.40 0.0000
alg#seq | .017636931 2 .008818465 3.09 0.1010
size | 41.6916509 2 20.8458254 7315.55 0.0000
alg#size | .022214508 2 .011107254 3.90 0.0658
seq#size | .828957455 4 .207239364 72.73 0.0000
alg#seq#size | .014564023 4 .003641006 1.28 0.3548
alloc | 92.6973005 2 46.3486503 16265.40 0.0000
alg#alloc | .060039547 2 .030019774 10.54 0.0057
seq#alloc | 9.51047037 4 2.37761759 834.39 0.0000
alg#seq#alloc | .02600767 4 .006501917 2.28 0.1491
size#alloc | .504304845 4 .126076211 44.24 0.0000
alg#size#alloc | .004001546 4 .001000387 0.35 0.8365
seq#size#alloc | 1.05212232 8 .13151529 46.15 0.0000
|
Residual | .022796194 8 .002849524

+

Total | 173.593158 53 3.27534261

.
. insheet using ch10ta1.csv, clear /* Example 10.3 */
(3 vars, 17 obs)

. anova y a##b /* partial SS */

Number of obs = 17 R-squared = 0.6632
Root MSE = 10.6004 Adj R-squared = 0.5855

Source | Partial SS df MS F Prob > F

+

Model | 2876.88031 3 958.960102 8.53 0.0022
|
a | 499.951343 1 499.951343 4.45 0.0549
b | 265.471328 1 265.471328 2.36 0.1483
a#b | 65.2445572 1 65.2445572 0.58 0.4597
|
Residual | 1460.78901 13 112.368386

+

Total | 4337.66932 16 271.104332

. anova y a##b, sequential /* SS for b without interaction */

Number of obs = 17 R-squared = 0.6632
Root MSE = 10.6004 Adj R-squared = 0.5855

Source | Seq. SS df MS F Prob > F

+

Model | 2876.88031 3 958.960102 8.53 0.0022
|
a | 2557.00388 1 2557.00388 22.76 0.0004
b | 254.63187 1 254.63187 2.27 0.1561
a#b | 65.2445572 1 65.2445572 0.58 0.4597
|
Residual | 1460.78901 13 112.368386

+

Total | 4337.66932 16 271.104332

. anova y b##a, sequential /* SS for a without interaction */

Number of obs = 17 R-squared = 0.6632
Root MSE = 10.6004 Adj R-squared = 0.5855

Source | Seq. SS df MS F Prob > F

+

Model | 2876.88031 3 958.960102 8.53 0.0022
|
b | 2326.34871 1 2326.34871 20.70 0.0005
a | 485.287037 1 485.287037 4.32 0.0581
b#a | 65.2445572 1 65.2445572 0.58 0.4597
|
Residual | 1460.78901 13 112.368386

+

Total | 4337.66932 16 271.104332

.
. insheet using ch09ta2.csv, clear /* Example 9.2 */
(5 vars, 32 obs)

. anova y a##b##c##d

Number of obs = 32 R-squared = 0.9881
Root MSE = 1.01458 Adj R-squared = 0.9769

Source | Partial SS df MS F Prob > F

+

Model | 1367.19878 15 91.1465853 88.55 0.0000
|
a | 120.901246 1 120.901246 117.45 0.0000
b | 204.020006 1 204.020006 198.20 0.0000
a#b | 18.0000029 1 18.0000029 17.49 0.0007
c | 472.781235 1 472.781235 459.29 0.0000
a#c | 24.8512614 1 24.8512614 24.14 0.0002
b#c | 27.3800007 1 27.3800007 26.60 0.0001
a#b#c | 11.5199986 1 11.5199986 11.19 0.0041
d | 335.405027 1 335.405027 325.83 0.0000
a#d | 15.1250026 1 15.1250026 14.69 0.0015
b#d | 10.8112504 1 10.8112504 10.50 0.0051
a#b#d | 34.0312382 1 34.0312382 33.06 0.0000
c#d | 6.48000309 1 6.48000309 6.30 0.0232
a#c#d | 50.0000048 1 50.0000048 48.57 0.0000
b#c#d | 22.1112506 1 22.1112506 21.48 0.0003
a#b#c#d | 13.7812525 1 13.7812525 13.39 0.0021
|
Residual | 16.4699933 16 1.02937458

+

Total | 1383.66877 31 44.6344765

. anova y a b c d a#b#c#d

Number of obs = 32 R-squared = 0.9881
Root MSE = 1.01458 Adj R-squared = 0.9769

Source | Partial SS df MS F Prob > F

+

Model | 1367.19878 15 91.1465853 88.55 0.0000
|
a | 120.901246 1 120.901246 117.45 0.0000
b | 204.020006 1 204.020006 198.20 0.0000
c | 472.781235 1 472.781235 459.29 0.0000
d | 335.405027 1 335.405027 325.83 0.0000
a#b#c#d | 234.091266 11 21.2810242 20.67 0.0000
|
Residual | 16.4699933 16 1.02937458

+

Total | 1383.66877 31 44.6344765

. regress /* some indication that cell (1,1,1,1) is extreme */

Source | SS df MS Number of obs = 32

+
F( 15, 16) = 88.55
Model | 1367.19878 15 91.1465853 Prob > F = 0.0000
Residual | 16.4699933 16 1.02937458 R-squared = 0.9881

+
Adj R-squared = 0.9769
Total | 1383.66877 31 44.6344765 Root MSE = 1.0146

+

--------

y | Coef. Std. Err. t P>|t| [95% Con f. Interval]

+

--------

2.a | -15.6 1.014581 -15.38 0.000 -17.75082 -13.44918
2.b | -5.7 1.014581 -5.62 0.000 -7.850815 -3.549184
2.c | -3.500001 1.014581 -3.45 0.003 -5.650817 -1.349185
2.d | -4.5 1.014581 -4.44 0.000 -6.650816 -2.349184
|
a#b#c#d |
1 1 2 2 | 12.75 1.434834 8.89 0.000 9.708288 15.79171
1 2 1 2 | 12.4 1.434834 8.64 0.000 9.358287 15.44171
1 2 2 1 | 12.05 1.434834 8.40 0.000 9.008288 15.09171
1 2 2 2 | 25.3 2.029162 12.47 0.000 20.99837 29.60163
2 1 1 2 | 14.5 1.434834 10.11 0.000 11.45829 17.54171
2 1 2 1 | 13.55 1.434834 9.44 0.000 10.50829 16.59171
2 1 2 2 | 25.55 2.029162 12.59 0.000 21.24837 29.85163
2 2 1 1 | 12.15 1.434834 8.47 0.000 9.108287 15.19171
2 2 1 2 | 25.55 2.029162 12.59 0.000 21.24837 29.85163
2 2 2 1 | 27.7 2.029162 13.65 0.000 23.39837 32.00163
2 2 2 2 | 37.2 2.684329 13.86 0.000 31.50948 42.89052
|
_cons | 26.8 .7174171 37.36 0.000 25.27914 28.32086

+

--------

. generate onecell=(a==1)*(b==1)*(c==1)*(d==1)

. anova y a b c d onecell a##b##c##d, sequential

Number of obs = 32 R-squared = 0.9881
Root MSE = 1.01458 Adj R-squared = 0.9769

Source | Seq. SS df MS F Prob > F

+

Model | 1367.19878 15 91.1465853 88.55 0.0000
|
a | 120.901246 1 120.901246 117.45 0.0000
b | 204.020006 1 204.020006 198.20 0.0000
c | 472.781235 1 472.781235 459.29 0.0000
d | 335.405027 1 335.405027 325.83 0.0000
onecell | 217.351041 1 217.351041 211.15 0.0000
a#b | .045102207 1 .045102207 0.04 0.8368
a#c | .300126122 1 .300126122 0.29 0.5967
b#c | .765625 1 .765625 0.74 0.4012
a#b#c | .947232704 1 .947232704 0.92 0.3517
a#d | .347142563 1 .347142563 0.34 0.5695
b#d | 1.8375 1 1.8375 1.79 0.2002
a#b#d | 1.40624714 1 1.40624714 1.37 0.2596
c#d | 5.13374859 1 5.13374859 4.99 0.0402
a#c#d | 5.46750051 1 5.46750051 5.31 0.0349
b#c#d | .489999733 1 .489999733 0.48 0.5001
a#b#c#d | 0 0
|
Residual | 16.4699933 16 1.02937458

+

Total | 1383.66877 31 44.6344765

. anova y a b c d onecell

Number of obs = 32 R-squared = 0.9760
Root MSE = 1.13018 Adj R-squared = 0.9714

Source | Partial SS df MS F Prob > F

+

Model | 1350.45856 5 270.091711 211.45 0.0000
|
a | 39.3318513 1 39.3318513 30.79 0.0000
b | 321.533432 1 321.533432 251.73 0.0000
c | 628.692266 1 628.692266 492.20 0.0000
d | 474.815957 1 474.815957 371.73 0.0000
onecell | 217.351041 1 217.351041 170.16 0.0000
|
Residual | 33.2102179 26 1.27731607

+

Total | 1383.66877 31 44.6344765

.
. insheet using ch08ta9.csv, clear /* Example 8.10 */
(7 vars, 96 obs)

. egen tx=group(at gt v)

. xi: boxcox amylase i.tx
i.tx _Itx_1-32 (naturally coded; _Itx_1 omitted)
Fitting comparison model

Iteration 0: log likelihood = -542.90683
Iteration 1: log likelihood = -542.5619
Iteration 2: log likelihood = -542.56179
Iteration 3: log likelihood = -542.56179

Fitting full model

Iteration 0: log likelihood = -427.06096
Iteration 1: log likelihood = -424.07141
Iteration 2: log likelihood = -424.06421
Iteration 3: log likelihood = -424.06421

Number of obs = 96
LR chi2(31) = 237.00
Log likelihood = -424.06421 Prob > chi2 = 0.000

+

amylase | Coef. Std. Err. z P>|z| [95% Conf. Interval]

+

/theta | .0199989 .3958468 0.05 0.960 -.7558465 .7958443

+

Estimates of scale-variant parameters

+

| Coef.

+

Notrans |
_Itx_2 | -.2257092
_Itx_3 | -.1653663
_Itx_4 | -.3410324
_Itx_5 | .2061606
_Itx_6 | -.0799097
_Itx_7 | .096261
_Itx_8 | .0100406
_Itx_9 | .3149527
_Itx_10 | .0366516
_Itx_11 | .1889919
_Itx_12 | .1886505
_Itx_13 | .2654907
_Itx_14 | .0600479
_Itx_15 | .1791769
_Itx_16 | .1178467
_Itx_17 | .1514475
_Itx_18 | .0031704
_Itx_19 | .0760831
_Itx_20 | -.1154264
_Itx_21 | -.0755018
_Itx_22 | -.2582509
_Itx_23 | -.0948975
_Itx_24 | -.2019227
_Itx_25 | -.1874994
_Itx_26 | -.4267404
_Itx_27 | -.203189
_Itx_28 | -.3183826
_Itx_29 | -.2422305
_Itx_30 | -.6219556
_Itx_31 | -.3050953
_Itx_32 | -.4370663
_cons | 6.232128

+

/sigma | .0677822

+

Test Restricted LR statistic P-value
H0: log likelihood chi2 Prob > chi2

theta = -1 -427.42898 6.73 0.009
theta = 0 -424.06549 0.00 0.960
theta = 1 -427.06096 5.99 0.014

. generate lnam=ln(amylase)

. anova lnam atemp##gtemp##v

Number of obs = 96 R-squared = 0.9168
Root MSE = .073916 Adj R-squared = 0.8765

Source | Partial SS df MS F Prob > F

+

Model | 3.8523521 31 .124269423 22.74 0.0000
|
atemp | 3.01612744 7 .430875349 78.86 0.0000
gtemp | .004379535 1 .004379535 0.80 0.3740
atemp#gtemp | .081059687 7 .011579955 2.12 0.0539
v | .589569756 1 .589569756 107.91 0.0000
atemp#v | .027582211 7 .003940316 0.72 0.6544
gtemp#v | .085992792 1 .085992792 15.74 0.0002
atemp#gtemp#v | .047640678 7 .006805811 1.25 0.2916
|
Residual | .349670809 64 .005463606

+

Total | 4.20202291 95 .04423182

. anova lnam atemp##gtemp gtemp##v

Number of obs = 96 R-squared = 0.8989
Root MSE = .073806 Adj R-squared = 0.8768

Source | Partial SS df MS F Prob > F

+

Model | 3.77712921 17 .222184071 40.79 0.0000
|
atemp | 3.01612744 7 .430875349 79.10 0.0000
gtemp | .004379535 1 .004379535 0.80 0.3727
atemp#gtemp | .081059687 7 .011579955 2.13 0.0504
v | .589569756 1 .589569756 108.23 0.0000
gtemp#v | .085992792 1 .085992792 15.79 0.0002
|
Residual | .424893698 78 .005447355

+

Total | 4.20202291 95 .04423182

. * atemp modelled as continuous
. anova lnam c.atemp##gtemp gtemp##v

Number of obs = 96 R-squared = 0.3786
Root MSE = .170336 Adj R-squared = 0.3440

Source | Partial SS df MS F Prob > F

+

Model | 1.59074174 5 .318148348 10.97 0.0000
|
atemp | .87537029 1 .87537029 30.17 0.0000
gtemp | .021463201 1 .021463201 0.74 0.3920
gtemp#atemp | .035429365 1 .035429365 1.22 0.2721
v | .589569756 1 .589569756 20.32 0.0000
gtemp#v | .085992792 1 .085992792 2.96 0.0886
|
Residual | 2.61128117 90 .029014235

+

Total | 4.20202291 95 .04423182

. regress

Source | SS df MS Number of obs = 96

+
F( 5, 90) = 10.97
Model | 1.59074174 5 .318148348 Prob > F = 0.0000
Residual | 2.61128117 90 .029014235 R-squared = 0.3786

+
Adj R-squared = 0.3440
Total | 4.20202291 95 .04423182 Root MSE = .17034

+

--------

lnam | Coef. Std. Err. t P>|t| [95% Con f. Interval]

+

--------

atemp | .007507 .0024196 3.10 0.003 .0027 .0123139
25.gtemp | -.0154915 .094255 -0.16 0.870 -.2027455 .1717624
|
gtemp#c.atemp |
25 | .0037812 .0034218 1.11 0.272 -.0030168 .0105792
|
2.v | -.0968751 .0491717 -1.97 0.052 -.1945632 .000813
|
gtemp#v |
25 2 | -.1197169 .0695392 -1.72 0.089 -.2578687 .018435
|
_cons | 5.671236 .0666483 85.09 0.000 5.538827 5.803644

+

--------

. anova lnam c.atemp##c.atemp##gtemp gtemp##v

Number of obs = 96 R-squared = 0.8759
Root MSE = .07698 Adj R-squared = 0.8660

Source | Partial SS df MS F Prob > F

+

Model | 3.68054689 7 .525792413 88.73 0.0000
|
atemp | 2.48873057 1 2.48873057 419.98 0.0000
atemp#atemp | 2.08971611 1 2.08971611 352.64 0.0000
gtemp | .002178688 1 .002178688 0.37 0.5458
gtemp#atemp | .000443043 1 .000443043 0.07 0.7852
gtemp#atemp#atemp | .000089043 1 .000089043 0.02 0.9027
v | .589569756 1 .589569756 99.49 0.0000
gtemp#v | .085992792 1 .085992792 14.51 0.0003
|
Residual | .521476018 88 .005925864

+

Total | 4.20202291 95 .04423182

. regress

Source | SS df MS Number of obs = 96

+
F( 7, 88) = 88.73
Model | 3.68054689 7 .525792413 Prob > F = 0.0000
Residual | .521476018 88 .005925864 R-squared = 0.8759

+
Adj R-squared = 0.8660
Total | 4.20202291 95 .04423182 Root MSE = .07698

+

--------

lnam | Coef. Std. Err. t P>|t| [95% Con f. Interval]

+

--------

atemp | .0969251 .0067791 14.30 0.000 .083453 .1103971
|
c.atemp#c.atemp | -.0018086 .0001353 -13.37 0.000 -.0020775 -.0015397
|
25.gtemp | -.0036131 .1058513 -0.03 0.973 -.2139703 .2067441
|
gtemp#c.atemp |
25 | .0026214 .0095871 0.27 0.785 -.0164309 .0216737
|
gtemp#c.atemp#c.atemp |
25 | .0000235 .0001914 0.12 0.903 -.0003569 .0004038
|
2.v | -.0968751 .0222221 -4.36 0.000 -.1410369 -.0527133
|
gtemp#v |
25 2 | -.1197169 .0314268 -3.81 0.000 -.1821711 -.0572627
|
_cons | 4.755444 .0748482 63.53 0.000 4.606699 4.904189

+

--------

. anova lnam c.atemp##c.atemp##c.atemp##gtemp gtemp##v

Number of obs = 96 R-squared = 0.8928
Root MSE = .072366 Adj R-squared = 0.8816

Source | Partial SS df MS F Prob > F

+

Model | 3.75165208 9 .416850231 79.60 0.0000
|
atemp | .02573811 1 .02573811 4.91 0.0293
atemp#atemp | .002539245 1 .002539245 0.48 0.4881
atemp#atemp#atemp | .041993388 1 .041993388 8.02 0.0058
gtemp | .030996724 1 .030996724 5.92 0.0171
gtemp#atemp | .029214928 1 .029214928 5.58 0.0204
gtemp#atemp#atemp | .028445371 1 .028445371 5.43 0.0221
gtemp#atemp#atemp#atemp | .029111799 1 .029111799 5.56 0.0207
v | .589569756 1 .589569756 112.58 0.0000
gtemp#v | .085992792 1 .085992792 16.42 0.0001
|
Residual | .450370831 86 .00523687

+

Total | 4.20202291 95 .04423182

.
. * Example 10.1: analysis with missing value
. anova lnam atemp##gtemp gtemp##v if _n>1

Number of obs = 95 R-squared = 0.9006
Root MSE = .073411 Adj R-squared = 0.8787

Source | Partial SS df MS F Prob > F

+

Model | 3.75960438 17 .221153199 41.04 0.0000
|
atemp | 3.02601848 7 .432288355 80.22 0.0000
gtemp | .002975171 1 .002975171 0.55 0.4597
atemp#gtemp | .067027569 7 .009575367 1.78 0.1040
v | .565122839 1 .565122839 104.86 0.0000
gtemp#v | .078495586 1 .078495586 14.57 0.0003
|
Residual | .414961372 77 .005389109

+

Total | 4.17456575 94 .044410274

. margins gtemp#v, asbalanced /* least squares means */

Adjusted predictions Number of obs = 95

Expression : Linear prediction, predict()

at : atemp (asbalanced)

gtemp (asbalanced)

v (asbalanced)

+

--------

| Delta-method
| Margin Std. Err. t P>|t| [95% Con f. Interval]

+

--------

gtemp#v |
13 1 | 5.847649 .0149849 390.24 0.000 5.817811 5.877488
13 2 | 5.750774 .0149849 383.77 0.000 5.720936 5.780613
25 1 | 5.916409 .0153643 385.08 0.000 5.885815 5.947004
25 2 | 5.704424 .0149849 380.68 0.000 5.674586 5.734263

+

--------

. margins gtemp#v /* all estimates different */

Predictive margins Number of obs = 95

Expression : Linear prediction, predict()

+

--------

| Delta-method
| Margin Std. Err. t P>|t| [95% Con f. Interval]

+

--------

gtemp#v |
13 1 | 5.849246 .0149878 390.27 0.000 5.819402 5.879091
13 2 | 5.752371 .0149878 383.80 0.000 5.722527 5.782216
25 1 | 5.917001 .01534 385.72 0.000 5.886455 5.947547
25 2 | 5.705016 .0149883 380.63 0.000 5.67517 5.734861

+

--------

.
end of do-file

. exit, clear