Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
Re: st: Obtaining 95%CI for marginal effect
From
Steven Samuels <[email protected]>
To
[email protected]
Subject
Re: st: Obtaining 95%CI for marginal effect
Date
Sun, 6 Feb 2011 11:08:13 -0500
The method I showed will work for indicator variables too, but I, for
one, don't understand what is represented when indicators for
variables with >2 categories are set to their means. (-svy: prop-
won't help, as for k categories, it gives k proportions). Much better,
I think, to set the other categorical variables to typical values.
e.g., for the auto data set:
xi: svy: reg mpg weight i.foreign i.rep78
// adjust to rep78=3
adjust weight=3138.575 _Irep78_2=0 _Irep78_3=1 _Irep78_4=0
_Irep78_5=0, by(foreign)
For non-linear models like -logistic-, setting variables to their
means can produce unexpected results. See: http://www.stata.com/statalist/archive/2010-07/msg01596.html
and Michael Norman Mitchell's follow-up.
Note: Your original formulation in -mfx- looks incomplete. You
specified
"at_Imedgrp_1=0 _Imedgrp_2=0" But medgrp had four levels,( 0,1,2,3),
since -xi- produced three indicator variables. Your specification was
equivalent to saying: at medgrp==0 or medgrp==3. Is this what you
intended?
Steve
On Feb 6, 2011, at 9:59 AM, Nur Hafidha Hikmayani wrote:
Thanks Steve.
I'm afraid however that I'm not clear enough when some independent
variables are categorical. You gave an example in which weight and
turn are numerical variables - in my case, there is only 1 numerical
IV. Suppose foreign is the main IV of interest and other covariates
are mostly categorical, can we use -adjust- too (perhaps with
-svy:prop- beforehand instead)?
hafida-
On Sun, Feb 6, 2011 at 9:12 PM, Steven Samuels <[email protected]>
wrote:
Nur-
I apologize. I checked and discovered -adjust- after -svy: reg- does
not
compute predictions at the weighted means of the covariates, only at
the
unweighted means. As a work-around, you could substitute the
weighted means
by hand.
****************************
sysuse auto, clear
drop if rep78==.
svyset rep78 [pw=head]
svy: mean weight turn //get survey weighted means
xi: svy: reg mpg weight turn i.foreign
adjust weight= 3138.575 turn=40.33816, by(foreign) ci se
*****************************
Steve
[email protected]
Nur-
Use -adjust- with the -ci- option. The fitted value of y is not a
"marginal
effect"; for -regress- or (-svy: regress-) the default marginal
effects are
the regression coefficients.
*********************
sysuse auto, clear
xi: reg weight price turn i.foreign
adjust price turn, by(foreign), se ci
******************
Steve
[email protected]
On Feb 6, 2011, at 7:02 AM, Nur Hafidha Hikmayani wrote:
Dear all,
I've been running some regression models using -svy- and estimating
its marginal effect using -mfx- (I use Stata 10.1).
I wonder how can I get the 95% CI for the marginal effects (y)?
The output for regression and its marginal effect are as follows:
. xi: svy: reg GH i.medgrp exgrp chronic nummed gp
------------------------------------------------------------------------------
| Linearized
GH | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------
+----------------------------------------------------------------
_Imedgrp_1 | 4.429839 3.56263 1.24 0.214 -2.564628
11.42431
_Imedgrp_2 | 8.333728 3.633545 2.29 0.022 1.200035
15.46742
_Imedgrp_3 | 10.05818 3.773961 2.67 0.008 2.648813
17.46755
exgrp | -8.916839 1.89046 -4.72 0.000 -12.62835
-5.205324
chronic | -10.31767 1.936802 -5.33 0.000 -14.12017
-6.515169
nummed | -1.063043 .3452676 -3.08 0.002 -1.740902 -.
3851831
gp | -4.347845 1.773649 -2.45 0.014 -7.830027 -.
865663
_cons | 83.19335 3.520136 23.63 0.000 76.28231
90.10438
------------------------------------------------------------------------------
. mfx, at(mean _Imedgrp_1=0 _Imedgrp_2=0)
Marginal effects after svy:regress
y = Fitted values (predict)
= 53.085624
------------------------------------------------------------------------------
variable | dy/dx Std. Err. z P>|z| [ 95%
C.I. ] X
---------
+--------------------------------------------------------------------
_Imedg~1*| 4.429839 3.56263 1.24 0.214 -2.55279 11.4125
0
_Imedg~2*| 8.333728 3.63354 2.29 0.022 1.21211 15.4553
.379185
_Imedg~3*| 10.05818 3.77396 2.67 0.008 2.66136 17.455
0
exgrp*| -8.916839 1.89046 -4.72 0.000 -12.6221 -5.21161 .
762312
chronic*| -10.31767 1.9368 -5.33 0.000 -14.1137 -6.52161
.83752
nummed | -1.063043 .34527 -3.08 0.002 -1.73975 -.386331
6.69376
gp*| -4.347845 1.77365 -2.45 0.014 -7.82413 -.871558 .
472814
------------------------------------------------------------------------------
(*) dy/dx is for discrete change of dummy variable from 0 to 1
Any help is much appreciated,
Thanks,
hafida-
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/