st: re: ANOVA model (my mistake)

[David Airey <david.airey@Vanderbilt.Edu>]

My version of Stata disagrees with what you say about numbering 
schemes. Here is data and the same test repeated twice with different 
numbering schemes for the subsamples, and code used to generate the 
data can results. Note the different results.

		drop _all
		set obs 150
		*only difference in next line
		egen neuron = seq(), from(1) to(3) block(1)
		egen subject = seq(), from(1) to(10) block(15)
		egen treatment = seq(), from(1) to(5) block(3)
		gen a = invnorm(uniform()) in 1/10
		gen a_effect = a[subject]
		drop a
		gen c_effect = invnorm(uniform())
		gen error = invnorm(uniform())
		gen measure = a_effect + c_effect + error
		
		* correct RMA model nesting neuron in subject
		anova measure subject / neuron|subject treatment / treatment*subject 
/, repeated(treatment)
		test treatment / treatment*subject

      neuron   subject  treatm~t    measure
  1.        1         1         1   1.232982
  2.        2         1         1   1.166421
  3.        3         1         1   1.674022
  4.        1         1         2   1.690412
  5.        2         1         2   1.079583
  6.        3         1         2   1.983932
  7.        1         1         3  -1.573235
  8.        2         1         3   2.714028
  9.        3         1         3       3.24
 10.        1         1         4   1.470877
 11.        2         1         4   4.342456
 12.        3         1         4   .1263056
 13.        1         1         5   3.049922
 14.        2         1         5   2.537046
 15.        3         1         5  -.7629604
 16.        1         2         1  -1.013575
 17.        2         2         1   .6785818
 18.        3         2         1  -1.619054
 19.        1         2         2   1.396539
 20.        2         2         2   .9016123
 21.        3         2         2  -2.113595
 22.        1         2         3   2.285021
 23.        2         2         3   .9567382
 24.        3         2         3   2.037529
 25.        1         2         4   .5208883
etc.
                           Number of obs =     150     R-squared     =  
0.6542
                           Root MSE      = 1.46849     Adj R-squared =  
0.3560

                  Source |  Partial SS    df       MS           F     
Prob > F
       
------------------+----------------------------------------------------
                   Model |  326.402584    69  4.73047224       2.19     
0.0004
                         |
                 subject |   211.95834     9  23.5509266       8.84     
0.0000
          neuron|subject |  53.2741741    20  2.66370871
       
------------------+----------------------------------------------------
               treatment |  2.60790639     4  .651976599       0.40     
0.8068
       treatment*subject |  58.5621641    36  1.62672678
       
------------------+----------------------------------------------------
       treatment*subject |  58.5621641    36  1.62672678       0.75     
0.8249
                         |
                Residual |  172.515961    80  2.15644951
       
------------------+----------------------------------------------------
                   Total |  498.918545   149  3.34844661


		drop _all
		set obs 150
		egen neuron = seq(), from(1) to(150) block(1)
		egen subject = seq(), from(1) to(10) block(15)
		egen treatment = seq(), from(1) to(5) block(3)
		gen a = invnorm(uniform()) in 1/10
		gen a_effect = a[subject]
		drop a
		gen c_effect = invnorm(uniform())
		gen error = invnorm(uniform())
		gen measure = a_effect + c_effect + error
		
		* correct RMA model nesting neuron in subject
		anova measure subject / neuron|subject treatment / treatment*subject 
/, repeated(treatment)
		test treatment / treatment*subject

       neuron   subject  treatm~t    measure
  1.        1         1         1   3.588527
  2.        2         1         1   2.431523
  3.        3         1         1   1.267083
  4.        4         1         2   2.137298
  5.        5         1         2   1.644081
  6.        6         1         2   4.000641
  7.        7         1         3   2.452223
  8.        8         1         3   2.811015
  9.        9         1         3    2.97793
 10.       10         1         4   1.751251
 11.       11         1         4   3.431399
 12.       12         1         4   .4201414
 13.       13         1         5   1.742686
 14.       14         1         5   .4739965
 15.       15         1         5   1.596641
 16.       16         2         1  -.9146303
 17.       17         2         1  -.5972514
 18.       18         2         1  -1.519495
 19.       19         2         2  -.6959237
 20.       20         2         2  -.3078359
 21.       21         2         2  -.5360426
 22.       22         2         3   .9754638
 23.       23         2         3   -.007028
 24.       24         2         3  -.2681147
 25.       25         2         4   1.523469
etc.

                           Number of obs =     150     R-squared     =  
1.0000
                           Root MSE      =    0.00     Adj R-squared =

                  Source |  Partial SS    df       MS           F     
Prob > F
       
------------------+----------------------------------------------------
                   Model |  495.194439   149  3.32345261
                         |
                 subject |  234.868301     9  26.0964779      13.89     
0.0000
          neuron|subject |  255.445948   136  1.87827903
       
------------------+----------------------------------------------------
               treatment |  11.6365863     4  2.90914656
       treatment*subject |        0.00     0
       
------------------+----------------------------------------------------
       treatment*subject |        0.00     0
                         |
                Residual |        0.00     0
       
------------------+----------------------------------------------------
                   Total |  495.194439   149  3.32345261

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