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st: Anova and pwcompare and not estimable


From   Garry Anderson <[email protected]>
To   "[email protected]" <[email protected]>
Subject   st: Anova and pwcompare and not estimable
Date   Mon, 9 Jan 2012 08:52:34 +0000

Dear Statalist,

The following output from the -pwcompare- command after -anova- compares all pairwise differences in a repeated measures anova with one factor being repeated. The standard errors are not estimable when comparing levels of the between subject factor (calib). There do not appear to be any empty cells. Empty cells can cause the 'not estimable' text.
Calib is a between subject factor and shape is a within subject factor.

Note I have separate subjects for the two levels of calib.

An suggestions as to how to estimate these standard errors would be appreciated.

An example follows

. set more off

. use http://www.stata-press.com/data/r12/t77
(T7.7 -- Winer, Brown, Michels)

. gen sub1to6 = subject

. replace sub1to6=sub1to6 + 3 if calib==2
(12 real changes made)



. anova score calib / sub1to6|calib shape calib#shape,repeated(shape)

                           Number of obs =      24     R-squared     =  0.8925
                           Root MSE      = 1.11181     Adj R-squared =  0.7939

                  Source |  Partial SS    df       MS           F     Prob > F
           --------------+----------------------------------------------------
                   Model |     123.125    11  11.1931818       9.06     0.0003
                         |
                   calib |  51.0416667     1  51.0416667      11.89     0.0261
           sub1to6|calib |  17.1666667     4  4.29166667   
           --------------+----------------------------------------------------
                   shape |  47.4583333     3  15.8194444      12.80     0.0005
             calib#shape |  7.45833333     3  2.48611111       2.01     0.1662
                         |
                Residual |  14.8333333    12  1.23611111   
           --------------+----------------------------------------------------
                   Total |  137.958333    23  5.99818841   


Between-subjects error term:  sub1to6|calib
                     Levels:  6         (4 df)
     Lowest b.s.e. variable:  sub1to6
     Covariance pooled over:  calib     (for repeated variable)

Repeated variable: shape
                                          Huynh-Feldt epsilon        =  0.8483
                                          Greenhouse-Geisser epsilon =  0.4751
                                          Box's conservative epsilon =  0.3333

                                            ------------ Prob > F ------------
                  Source |     df      F    Regular    H-F      G-G      Box
           --------------+----------------------------------------------------
                   shape |      3    12.80   0.0005   0.0011   0.0099   0.0232
             calib#shape |      3     2.01   0.1662   0.1791   0.2152   0.2291
                Residual |     12
           -------------------------------------------------------------------

. pwcompare calib#shape

Pairwise comparisons of marginal linear predictions

Margins      : asbalanced

-----------------------------------------------------------------
                |                                 Unadjusted
                |   Contrast   Std. Err.     [95% Conf. Interval]
----------------+------------------------------------------------
    calib#shape |
(1 2) vs (1 1)  |         -1   .9077853     -2.977894    .9778942
(1 3) vs (1 1)  |          3   .9077853      1.022106    4.977894
(1 4) vs (1 1)  |   .6666667   .9077853     -1.311227    2.644561
(2 1) vs (1 1)  |          .  (not estimable)
(2 2) vs (1 1)  |          .  (not estimable)
(2 3) vs (1 1)  |          .  (not estimable)
(2 4) vs (1 1)  |          .  (not estimable)
(1 3) vs (1 2)  |          4   .9077853      2.022106    5.977894
(1 4) vs (1 2)  |   1.666667   .9077853     -.3112275    3.644561
(2 1) vs (1 2)  |          .  (not estimable)
(2 2) vs (1 2)  |          .  (not estimable)
(2 3) vs (1 2)  |          .  (not estimable)
(2 4) vs (1 2)  |          .  (not estimable)
(1 4) vs (1 3)  |  -2.333333   .9077853     -4.311227   -.3554392
(2 1) vs (1 3)  |          .  (not estimable)
(2 2) vs (1 3)  |          .  (not estimable)
(2 3) vs (1 3)  |          .  (not estimable)
(2 4) vs (1 3)  |          .  (not estimable)
(2 1) vs (1 4)  |          .  (not estimable)
(2 2) vs (1 4)  |          .  (not estimable)
(2 3) vs (1 4)  |          .  (not estimable)
(2 4) vs (1 4)  |          .  (not estimable)
(2 2) vs (2 1)  |  -1.666667   .9077853     -3.644561    .3112275
(2 3) vs (2 1)  |   1.666667   .9077853     -.3112275    3.644561
(2 4) vs (2 1)  |   2.333333   .9077853      .3554392    4.311227
(2 3) vs (2 2)  |   3.333333   .9077853      1.355439    5.311227
(2 4) vs (2 2)  |          4   .9077853      2.022106    5.977894
(2 4) vs (2 3)  |   .6666667   .9077853     -1.311227    2.644561
-----------------------------------------------------------------


. table calib shape,con(n score)

----------------------------------
2 methods |
for       |
calibrati |     4 dial shapes     
ng dials  |    1     2     3     4
----------+-----------------------
        1 |    3     3     3     3
        2 |    3     3     3     3
----------------------------------


 
. 
Kind regards, Garry
Garry Anderson
Faculty of Veterinary Science
University of Melbourne
250 Princes Highway    Ph  03 9731 2221
WERRIBEE    3030       Fax 03 9731 2388
Email:  [email protected]

 



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