Dear colleagues,
I would like to know how to activate the marksample function of Stata in runmlwin.
I know in Stata, it is marksample1 if xvar==value, and then after that if correlation, it is corr x y if sample1==1.
Thank you,
Russell
Mark sample in runmlwin
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- Posts: 1384
- Joined: Mon Oct 19, 2009 10:34 am
Re: Mark sample in runmlwin
The same syntax that you referred to should apply, i.e.:
Load data:
Run model on full dataset:
Mark subsample to use:
Run model on subsample:
You can also skip the -mark- command and use an expression directly:
Load data:
Code: Select all
use http://www.bristol.ac.uk/cmm/media/runmlwin/tutorial, clear
Code: Select all
. runmlwin normexam cons standlrt, level2(school: cons) level1(student: cons) nopause
MLwiN 2.31 multilevel model Number of obs = 4059
Normal response model
Estimation algorithm: IGLS
-----------------------------------------------------------
| No. of Observations per Group
Level Variable | Groups Minimum Average Maximum
----------------+------------------------------------------
school | 65 2 62.4 198
-----------------------------------------------------------
Run time (seconds) = 1.08
Number of iterations = 4
Log likelihood = -4678.6211
Deviance = 9357.2422
------------------------------------------------------------------------------
normexam | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cons | .0023908 .0400224 0.06 0.952 -.0760516 .0808332
standlrt | .5633712 .0124654 45.19 0.000 .5389394 .5878029
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 2: school |
var(cons) | .0921275 .0181475 .0565591 .127696
-----------------------------+------------------------------------------------
Level 1: student |
var(cons) | .565731 .0126585 .5409209 .5905412
------------------------------------------------------------------------------
Code: Select all
. mark sample1 if girl==1
Code: Select all
. runmlwin normexam cons standlrt if sample1==1, level2(school: cons) level1(student: cons) nopause
MLwiN 2.31 multilevel model Number of obs = 2436
Normal response model
Estimation algorithm: IGLS
-----------------------------------------------------------
| No. of Observations per Group
Level Variable | Groups Minimum Average Maximum
----------------+------------------------------------------
school | 55 1 44.3 120
-----------------------------------------------------------
Run time (seconds) = 1.21
Number of iterations = 3
Log likelihood = -2761.4929
Deviance = 5522.9858
------------------------------------------------------------------------------
normexam | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cons | .059983 .0461238 1.30 0.193 -.0304179 .150384
standlrt | .5563728 .0164685 33.78 0.000 .5240951 .5886504
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 2: school |
var(cons) | .0965134 .0217593 .053866 .1391608
-----------------------------+------------------------------------------------
Level 1: student |
var(cons) | .5403317 .0156572 .5096441 .5710193
------------------------------------------------------------------------------
Code: Select all
. runmlwin normexam cons standlrt if girl==1, level2(school: cons) level1(student: cons) nopause
MLwiN 2.31 multilevel model Number of obs = 2436
Normal response model
Estimation algorithm: IGLS
-----------------------------------------------------------
| No. of Observations per Group
Level Variable | Groups Minimum Average Maximum
----------------+------------------------------------------
school | 55 1 44.3 120
-----------------------------------------------------------
Run time (seconds) = 1.23
Number of iterations = 3
Log likelihood = -2761.4929
Deviance = 5522.9858
------------------------------------------------------------------------------
normexam | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cons | .059983 .0461238 1.30 0.193 -.0304179 .150384
standlrt | .5563728 .0164685 33.78 0.000 .5240951 .5886504
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 2: school |
var(cons) | .0965134 .0217593 .053866 .1391608
-----------------------------+------------------------------------------------
Level 1: student |
var(cons) | .5403317 .0156572 .5096441 .5710193
------------------------------------------------------------------------------
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- Posts: 6
- Joined: Tue Apr 22, 2014 2:54 pm
Re: Mark sample in runmlwin
Thank you Chris.
Much appreciated,
Russell
Much appreciated,
Russell