.-
help for ^grand2^                              (statalist distribution 14sep1998)
.-

Compute an estimate of the grand mean/intercept and differences from the grand
------------------------------------------------------------------------------

       ^grand2^ indicator_variable_list [^, l^evel^(^#^)^ ]

^grand2^ typed without arguments redisplays the prior estimates.

Description
-----------

For use after @fit@ to present a set of indicator/dummy variables in the form
of a "grand mean" and differences from the "grand mean".  The specified list of
variables (indicator_variable_list) must be orthogonal and completely span the
space.  The model estimated by @fit@ must also include the complete list of
indicator variables that fully span the space.  Use the ^hascons^ option on
@fit@ to allow the full set of indicators to be included in the model.

Note:  ^grand2^ cannot be used after @regress@, it requires some of the results
stored by @fit@.

Options
-------

^level(^#^)^ specifies the confidence level, in percent, for confidence intervals;
    see help @level@.

Technical Notes
---------------

^grand2^ recasts the full parameter and variance estimates from the prior @fit@
and saves the result as the current estimates.  In this sense, ^grand2^ acts
like an estimator.  The coefficients, standard errors, and matrices may be
accessed in the same way as any estimator (See [U] 26 Estimation and
post-estimation commands).  Specifically, @test@ may be used to perform tests
with the grand mean and differences.  If estimates in the original space are
required, the ^fit^ command must be reissued.

The grand mean is presented as the coefficient on _cons.  This method is used
so that @predict@ will produce expected results after the estimates have been
transformed by ^grand2^.


Example
-------

Using the auto data, generate a full set of mutually exclusive indicators for
repair record.

   ^. tab rep78, gen(reprec)^

Repair      |
Record 1978 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          2        2.90        2.90
          2 |          8       11.59       14.49
          3 |         30       43.48       57.97
          4 |         18       26.09       84.06
          5 |         11       15.94      100.00
------------+-----------------------------------
      Total |         69      100.00

Estimate the full model using -fit- with the -hascons- option.

   ^. fit price displ reprec*, hascons^

  Source |       SS       df       MS                  Number of obs =      69
---------+------------------------------               F(  5,    63) =    7.36
   Model |   212659562     5  42531912.4               Prob > F      =  0.0000
Residual |   364137397    63  5779958.68               R-squared     =  0.3687
---------+------------------------------               Adj R-squared =  0.3186
   Total |   576796959    68  8482308.22               Root MSE      =  2404.2

------------------------------------------------------------------------------
   price |      Coef.   Std. Err.       t     P>|t|       [95% Conf. Interval]
---------+--------------------------------------------------------------------
   displ |   21.21876   3.569023      5.945   0.000       14.08664    28.35089
 reprec1 |   511.7159   1831.576      0.279   0.781      -3148.397    4171.829
 reprec2 |   827.3793   1212.444      0.682   0.497      -1595.497    3250.255
 reprec3 |    1548.21   930.9655      1.663   0.101      -312.1759    3408.596
 reprec4 |   2276.878   853.5133      2.668   0.010       571.2674    3982.488
 reprec5 |   3555.788   826.2275      4.304   0.000       1904.704    5206.872
------------------------------------------------------------------------------


Obtain the grand means and marginal differences by specifying the full set
of orthogonal indicators on the ^grand2^ command line.

   ^. grand2 reprec*^

Estimates as grand mean/intercept and marginal impacts

------------------------------------------------------------------------------
   price |      Coef.   Std. Err.       t     P>|t|       [95% Conf. Interval]
---------+--------------------------------------------------------------------
   displ |   21.21876   3.569023      5.945   0.000       14.08664    28.35089
 reprec1 |  -1433.012   1675.361     -0.855   0.396      -4780.955     1914.93
 reprec2 |  -1117.349   814.6588     -1.372   0.175      -2745.314    510.6168
 reprec3 |  -396.5179     349.24     -1.135   0.261      -1094.418    301.3826
 reprec4 |   332.1495    491.956      0.675   0.502      -650.9461    1315.245
 reprec5 |    1611.06   733.4134      2.197   0.032       145.4504     3076.67
   _cons |   1944.728   763.6394      2.547   0.013       418.7167    3470.739
------------------------------------------------------------------------------
Note:  The grand mean is the coefficient of _cons


Perform a test in the new coefficient space.

    ^. test _cons + reprec1 + reprec4 = 0^

 ( 1)  reprec1 + reprec4 + _cons = 0.0

       F(  1,    63) =    0.21
            Prob > F =    0.6457


Author
------

     Vince Wiggins
     StataCorp.
     vwiggins@@stata.com


Also see
--------

 Manual:  ^[U] 26 Estimation and post-estimation commands^
On-line:  help for @lincom@, @predict@, @test@, @testnl@
