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| From | "Ben Hoen" <bhoen@lbl.gov> |
| To | <statalist@hsphsun2.harvard.edu> |
| Subject | RE: st: interaction notation for lincom (and test) |
| Date | Thu, 29 Nov 2012 11:39:37 -0500 |
Fantastic Jeff. This fully answers my question and gives me a lot of
additional detail to digest.
Thank you!
Ben
Ben Hoen
LBNL
Office: 845-758-1896
Cell: 718-812-7589
-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Jeff Pitblado,
StataCorp LP
Sent: Thursday, November 29, 2012 11:14 AM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: interaction notation for lincom (and test)
Ben Hoen <bhoen@lbl.gov> is using interaction terms that contain variables
with long names, and would like to compute tests and linear combinations of
their coefficients:
> This is a bit of a shot in the dark...
>
> I have interacted variables in a regression that I want to compare using
> lincom (or test) and wanted to know if there was a shorthand way to recall
> the variables. The difficulty is that a number of the names of the
> variables are quite long (and not something I can change), so having a
> shorthand way to get at their coefficients and their se would be
preferred.
>
>
> For example, say I had this:
> *==================================================
> sysuse auto, clear
> g long_name_a=round(runiform())+1
> g long_name_b=round(2*runiform())+1
>
> areg price mpg i.long_name_a#ib3.long_name_b, abs(foreign)
>
> matrix list e(b)
>
> * I can access the stored results of the variables like this
> lincom 1b.long_name_a#1.long_name_b-2.long_name_a#1.long_name_b
>
> *but I hoped that I could get at the same via some matrix notation from
e(b)
>
> *====================================================
>
>
> Admittedly I am a novice in how to work with matrices but I thought I
would
> try the list.
I built a working example based on Ben's example:
. set seed 1234
. sysuse auto
. gen long_name_a = ceil(2*runiform())
. gen long_name_b = ceil(3*runiform())
. areg price mpg i.l~e_a#ib3.l~e_b, abs(foreign)
Side note -- notation shortcut:
Notice that I used the tilde character '~' as a way to cut down on
the
number of characters I have to type while referencing variables with
long names. Here '~' is the abbreviation character that tells Stata
to look for the unique variable that fits the specified pattern. In
Ben's example, the long variable names all have prefix 'long_name_',
and since there are no other variables that also use a prefix like
this, we can use 'l~e_' as our prefix. This is merely a shortcut in
specifying the variables and does not answer Ben's original
question.
On to Ben's question.
Ben would like to compute tests and linear combinations of the coefficients
on
the elements of interaction terms. The -lincom- example provided was:
. lincom 1b.long_name_a#1.long_name_b-2.long_name_a#1.long_name_b
Neither -test- nor -lincom- allows the '~' shortcut, so that is no help.
Stata 12 has two new postestimation commands that were specifically
developed
to take advantage of factor variables notation to perform the kinds of
computations that Ben is asking about.
-pwcompare- performs pairwise comparisons across levels of factor
variables in the current model. It will also report the marginal
linear predictions for each cell of a specified factor variables
term.
-contrast- computes contrasts (and tests) across levels of factor
variables in the current model, and has a more direct bearing on
Ben's
question.
So using the above -lincom- as an example, the equivalent -constrast-
command
is:
. contrast a.l~e_a@l~e_b
The revelant output from our example follows after my signature.
The -contrast- command extends the factor variables notation to include
operators for several kinds of contrasts. The ones used here are 'a.',
the adjacent contrast operator, which compares the current level with the
next; and '@' which tells -contrast- to compute contrasts of the preceeding
variable within levels of the following variable. Thus breaking the above
syntax into its parts we have
a.l~e_a -- compute adjacent contrasts of l~e_a
@l~e_b -- compute the above contrasts within each level of l~e_b
Since l~e_a has 2 levels and l~e_b has 3 levels, this will yield a contrast
of
1.l~e_a versus 2.l~e_a for each of the 3 levels of l~e_b. These comparisons
are between the marginal linear predictions within each cell defined by
the interaction term l~e_a#l~e_b originally specified in the model. Ben can
use the -cimeans- option of -pwcompare- with the interaction term of
interest
to see what the marginal linear predictions are. In our example, that would
be
. pwcompare l~e_b#l~e_a, cimargins
--Jeff
jpitblado@stata.com
***** BEGIN:
. areg price mpg i.l~e_a#ib3.l~e_b, abs(for)
Linear regression, absorbing indicators Number of obs =
74
F( 6, 66) =
4.84
Prob > F =
0.0004
R-squared =
0.3072
Adj R-squared =
0.2338
Root MSE =
2581.8232
----------------------------------------------------------------------------
--
price | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
mpg | -280.9069 59.85255 -4.69 0.000 -400.4063
-161.4074
|
long_name_a#|
long_name_b |
1 1 | -540.983 1057.622 -0.51 0.611 -2652.594
1570.628
1 2 | -405.8745 1010.844 -0.40 0.689 -2424.09
1612.341
2 1 | -286.6583 1199.695 -0.24 0.812 -2681.926
2108.61
2 2 | 658.2365 1111.33 0.59 0.556 -1560.606
2877.079
2 3 | 500.0031 1071.185 0.47 0.642 -1638.687
2638.693
|
_cons | 12188.71 1436.19 8.49 0.000 9321.264
15056.15
-------------+--------------------------------------------------------------
--
foreign | F(1, 66) = 6.310 0.014 (2
categories)
. lincom 1b.long_name_a#1.long_name_b-2.long_name_a#1.long_name_b
( 1) 1b.long_name_a#1.long_name_b - 2.long_name_a#1.long_name_b = 0
----------------------------------------------------------------------------
--
price | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
(1) | -254.3247 1159.891 -0.22 0.827 -2570.121
2061.472
----------------------------------------------------------------------------
--
. contrast a.l~e_a@l~e_b
Contrasts of marginal linear predictions
Margins : asbalanced
-----------------------------------------------------------
| df F P>F
------------------------+----------------------------------
long_name_a@long_name_b |
(1 vs 2) 1 | 1 0.05 0.8271
(1 vs 2) 2 | 1 1.13 0.2909
(1 vs 2) 3 | 1 0.22 0.6422
Joint | 3 0.47 0.7062
|
Residual | 66
-----------------------------------------------------------
-------------------------------------------------------------------------
| Contrast Std. Err. [95% Conf. Interval]
------------------------+------------------------------------------------
long_name_a@long_name_b |
(1 vs 2) 1 | -254.3247 1159.891 -2570.121 2061.472
(1 vs 2) 2 | -1064.111 999.4268 -3059.531 931.3089
(1 vs 2) 3 | -500.0031 1071.185 -2638.693 1638.687
-------------------------------------------------------------------------
. pwcompare l~e_b#l~e_a, cimargins
Pairwise comparisons of marginal linear predictions
Margins : asbalanced
-------------------------------------------------------------------------
| Unadjusted
| Margin Std. Err. [95% Conf. Interval]
------------------------+------------------------------------------------
long_name_b#long_name_a |
1 1 | 11647.73 1556.118 8540.836 14754.62
1 2 | 11902.05 1513.169 8880.911 14923.19
2 1 | 11782.83 1473.998 8839.903 14725.77
2 2 | 12846.95 1554.33 9743.626 15950.27
3 1 | 12188.71 1436.19 9321.264 15056.15
3 2 | 12688.71 1326.12 10041.03 15336.4
-------------------------------------------------------------------------
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