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Re: st: Strange Behaviour When Selecting Levels For Factor Variables In Regression With i#


From   Richard Williams <[email protected]>
To   [email protected], [email protected]
Subject   Re: st: Strange Behaviour When Selecting Levels For Factor Variables In Regression With i#
Date   Sat, 19 Jan 2013 01:48:28 -0500

At 06:45 PM 1/18/2013, [email protected] wrote:
Hello,

when i use indicator i with selecting level of a factor variable like i1.varname to run a regression I get strange results.

For example:

sysuse blong,clear
regress bp i.sex    i.when  c.patient   i.when#c.patient
regress bp i1.sex  i.when  c.patient   i.when#c.patient
regress bp i0.sex  i.when  c.patient   i.when#c.patient

This regression is wihout sense but theoretically it should estimate the same model and should give same results except for variable sex cause all I do is demand an indicator for a different level of a 2-level variable sex. But if I run these lines I get three regressions with three different coefficients for the variable "when" and "patient" even I didnt change anything that should be related to these variables.
Whats wrong here?

regards
Daniel

First off, I think you mean bplong.

Second, it seems to work fine for me. Are you leaving something out? Could your version of Stata be corrupted or out of date? I'm sure the problem is at your end because everything seems ok on mine. I'll just go ahead and give all the output below.

. sysuse bplong.dta, clear
(fictional blood-pressure data)

. regress bp i.sex i.when c.patient i.when#c.patient

      Source |       SS       df       MS              Number of obs =     240
-------------+------------------------------           F(  4,   235) =   21.29
       Model |  10881.7115     4  2720.42787           Prob > F      =  0.0000
    Residual |  30031.0843   235  127.791848           R-squared     =  0.2660
-------------+------------------------------           Adj R-squared =  0.2535
       Total |  40912.7958   239  171.183246           Root MSE      =  11.305

--------------------------------------------------------------------------------
bp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------+----------------------------------------------------------------
1.sex | -24.86705 2.919115 -8.52 0.000 -30.61803 -19.11608 2.when | -4.519608 2.937149 -1.54 0.125 -10.30611 1.266899 patient | .3029286 .0471077 6.43 0.000 .2101214 .3957359
               |
when#c.patient |
2 | -.0094555 .0421309 -0.22 0.823 -.092458 .0735469
               |
_cons | 150.5563 2.20753 68.20 0.000 146.2073 154.9054
--------------------------------------------------------------------------------

. regress bp i1.sex i.when c.patient i.when#c.patient

      Source |       SS       df       MS              Number of obs =     240
-------------+------------------------------           F(  4,   235) =   21.29
       Model |  10881.7115     4  2720.42787           Prob > F      =  0.0000
    Residual |  30031.0843   235  127.791848           R-squared     =  0.2660
-------------+------------------------------           Adj R-squared =  0.2535
       Total |  40912.7958   239  171.183246           Root MSE      =  11.305

--------------------------------------------------------------------------------
bp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------+----------------------------------------------------------------
1.sex | -24.86705 2.919115 -8.52 0.000 -30.61803 -19.11608 2.when | -4.519608 2.937149 -1.54 0.125 -10.30611 1.266899 patient | .3029286 .0471077 6.43 0.000 .2101214 .3957359
               |
when#c.patient |
2 | -.0094555 .0421309 -0.22 0.823 -.092458 .0735469
               |
_cons | 150.5563 2.20753 68.20 0.000 146.2073 154.9054
--------------------------------------------------------------------------------

. regress bp i0.sex i.when c.patient i.when#c.patient

      Source |       SS       df       MS              Number of obs =     240
-------------+------------------------------           F(  4,   235) =   21.29
       Model |  10881.7115     4  2720.42787           Prob > F      =  0.0000
    Residual |  30031.0843   235  127.791848           R-squared     =  0.2660
-------------+------------------------------           Adj R-squared =  0.2535
       Total |  40912.7958   239  171.183246           Root MSE      =  11.305

--------------------------------------------------------------------------------
bp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------+----------------------------------------------------------------
0.sex | 24.86705 2.919115 8.52 0.000 19.11608 30.61803 2.when | -4.519608 2.937149 -1.54 0.125 -10.30611 1.266899 patient | .3029286 .0471077 6.43 0.000 .2101214 .3957359
               |
when#c.patient |
2 | -.0094555 .0421309 -0.22 0.823 -.092458 .0735469
               |
_cons | 125.6893 4.214552 29.82 0.000 117.3862 133.9924
--------------------------------------------------------------------------------

.



-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME:   (574)289-5227
EMAIL:  [email protected]
WWW:    http://www.nd.edu/~rwilliam

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