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RE: Re:Re: Re: st: How do I run a 3-way repeated ANOVA?
From
"RAMPL Linn" <[email protected]>
To
<[email protected]>
Subject
RE: Re:Re: Re: st: How do I run a 3-way repeated ANOVA?
Date
Mon, 22 Mar 2010 14:28:36 +0100
>Linn,
>Can you check your data set with these commands and report back?
Thanks.
>anova y s a/s#a b/s#b c/s#c a#b/s#a#b a#c/s#a#c b#c/s#b#c a#b#c/,
>repeated(a b c)
>xtmixed y a##b##c || s: || s:R.a || s:R.b || s:R.c
>anovalator a b c, main fratio
>anovalator a b, two fratio
>anovalator a c, two fratio
>anovalator b c, two fratio
>anovalator a b c, 3way fratio
>anova y s a##b##c , repeated(a b c)
>xtmixed y a##b##c || s:
>anovalator a b c, main fratio
>anovalator a b, two fratio
>anovalator a c, two fratio
>anovalator b c, two fratio
>anovalator a b c, 3way fratio
Dear David,
thank you for your thoughts.
1.) The first 3-way anova command I cannot run, since matsize is too
small (in my version I already set the max of 800) and although I
dropped empty cells and my factors have only 2 levels each. Anyone knows
what I could do?
2.) The second command
xtmixed y a##b##c || s: || s:R.a || s:R.b || s:R.c
yields the following
(Frequency = a, trial_2 = b, laten_product_type = c, matlab_file = s):
------------------------------------------------------------------------
----rt_laten | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------
----1.frequency| -335.4941 105.84 -3.17 0.002 -542.9367
-128.0516
1.trial_2 | -211.8647 108.0537 -1.96 0.050 -423.646
-.0833961
frequency#|
trial_2 |
1 1 | 363.0471 149.6803 2.43 0.015 69.67901
656.4151
1.laten_pr~e| 1690.373 115.1755 14.68 0.000 1464.634
1916.113
frequency#|
laten_prod~e|
1 1 | -1450.713 154.5007 -9.39 0.000 -1753.529
-1147.898
trial_2#|
laten_prod~e |
1 1 | -1420.303 154.2353 -9.21 0.000 -1722.598
-1118.007
frequency#|
trial_2#|
laten_prod~e |
1 1 1 | 1235.996 215.1155 5.75 0.000 814.3772
1657.614
_cons | 1911.329 87.8797 21.75 0.000 1739.088
2083.57
------------------------------------------------------------------------
----
. anovalator frequency trial_2 laten_product_type, main fratio
anovalator main-effect for frequency
chi2(1) = 112.46737 p-value = 2.823e-26
scaled as F-ratio = 112.46737
anovalator main-effect for trial_2
chi2(1) = 55.329868 p-value = 1.019e-13
scaled as F-ratio = 55.329868
anovalator main-effect for laten_product_type
chi2(1) = 89.046871 p-value = 3.856e-21
scaled as F-ratio = 89.046871
. anovalator frequency trial_2, two fratio
anovalator two-way interaction for frequency#trial_2
chi2(1) = 83.194467 p-value = 7.437e-20
scaled as F-ratio = 83.194467
. anovalator frequency laten_product_type, two fratio
anovalator two-way interaction for frequency#laten_product_type
chi2(1) = 59.939065 p-value = 9.784e-15
scaled as F-ratio = 59.939065
. anovalator trial_2 laten_product_type, two fratio
anovalator two-way interaction for trial_2#laten_product_type
chi2(1) = 55.653241 p-value = 8.645e-14
scaled as F-ratio = 55.653241
. anovalator frequency trial_2 laten_product_type, 3way fratio
anovalator 3-way interaction for frequency#trial_2#laten_product_type
chi2(1) = 33.013427 p-value = 9.152e-09
scaled as F-ratio = 33.013427
3.) The third command
anova y s a##b##c , repeated(a b c)
yields the following
(Frequency = a, trial_2 = b, laten_product_type = c, matlab_file = s):
Source | Partial SS df MS F Prob > F
----------------------+-------------------------------------------------
-
Model | 350298338 91 3849432.29 7.64 0.0000
|
matlab_file | 140852038 84 1676809.98 3.33 0.0000
frequency | 53661693.5 1 53661693.5 106.45 0.0000
trial_2 | 30999754.2 1 30999754.2 61.49 0.0000
frequency#trial_2 | 39706885.9 1 39706885.9 78.76
0.0000
laten_pro~e | 52802044.5 1 52802044.5 104.74
0.0000
frequency#laten_pro~e | 28618795.1 1 28618795.1 56.77
0.0000
trial_2#laten_pro~e | 26815444.4 1 26815444.4 53.19
0.0000
frequency#trial_2# |
laten_pro~e | 15776930.6 1 15776930.6 31.30
0.0000
Residual | 288357651 572 504121.767
----------------------+-------------------------------------------------
-
Total | 638655989 663 963282.035
. anovalator frequency trial_2 laten_product_type, main fratio
anovalator main-effect for frequency
chi2(1) = 106.4459 p-value = 5.888e-25
scaled as F-ratio = 106.4459
anovalator main-effect for trial_2
chi2(1) = 61.492592 p-value = 4.444e-15
scaled as F-ratio = 61.492592
anovalator main-effect for laten_product_type
chi2(1) = 104.74066 p-value = 1.392e-24
scaled as F-ratio = 104.74066
. anovalator frequency trial_2, two fratio
anovalator two-way interaction for frequency#trial_2
chi2(1) = 78.764474 p-value = 6.997e-19
scaled as F-ratio = 78.764474
. anovalator frequency laten_product_type, two fratio
anovalator two-way interaction for frequency#laten_product_type
chi2(1) = 56.769608 p-value = 4.900e-14
scaled as F-ratio = 56.769608
. anovalator trial_2 laten_product_type, two fratio
anovalator two-way interaction for trial_2#laten_product_type
chi2(1) = 53.192395 p-value = 3.024e-13
scaled as F-ratio = 53.192395
. anovalator frequency trial_2 laten_product_type, 3way fratio
anovalator 3-way interaction for frequency#trial_2#laten_product_type
chi2(1) = 31.295873 p-value = 2.216e-08
scaled as F-ratio = 31.295873
4.) The fourth command
xtmixed y a##b##c || s:
yields the following
(Frequency = a, trial_2 = b, laten_product_type = c, matlab_file = s):
------------------------------------------------------------------------
----
rt_laten | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------
----
1.frequency | -335.4941 108.9435 -3.08 0.002 -549.0194
-121.9688
1.trial_2 | -211.8647 108.9435 -1.94 0.052 -425.39
1.660584
frequency#|
trial_2 |
1 1 | 363.0471 154.0693 2.36 0.018 61.0767
665.0174
1.laten_pr~e | 1684.502 115.1668 14.63 0.000 1458.78
1910.225
frequency#|
laten_prod~e |
1 1 | -1445.588 158.7853 -9.10 0.000 -1756.801
-1134.375
trial_2#|
laten_prod~e |
1 1 | -1414.432 158.531 -8.92 0.000 -1725.147
-1103.717
frequency#|
trial_2#|
laten_prod~e |
1 1 1 | 1230.87 221.2467 5.56 0.000 797.2348
1664.506
_cons | 1911.329 87.82307 21.76 0.000 1739.199
2083.459
------------------------------------------------------------------------
----
. anovalator frequency trial_2 laten_product_type, main fratio
anovalator main-effect for frequency
chi2(1) = 105.8431 p-value = 7.981e-25
scaled as F-ratio = 105.8431
anovalator main-effect for trial_2
chi2(1) = 60.399831 p-value = 7.742e-15
scaled as F-ratio = 60.399831
anovalator main-effect for laten_product_type
chi2(1) = 103.32856 p-value = 2.839e-24
scaled as F-ratio = 103.32856
. anovalator frequency trial_2, two fratio
anovalator two-way interaction for frequency#trial_2
chi2(1) = 78.236995 p-value = 9.139e-19
scaled as F-ratio = 78.236995
. anovalator frequency laten_product_type, two fratio
anovalator two-way interaction for frequency#laten_product_type
chi2(1) = 56.314778 p-value = 6.175e-14
scaled as F-ratio = 56.314778
. anovalator trial_2 laten_product_type, two fratio
anovalator two-way interaction for trial_2#laten_product_type
chi2(1) = 52.173862 p-value = 5.080e-13
scaled as F-ratio = 52.173862
. anovalator frequency trial_2 laten_product_type, 3way fratio
anovalator 3-way interaction for frequency#trial_2#laten_product_type
chi2(1) = 30.950733 p-value = 2.647e-08
scaled as F-ratio = 30.950733
So obviously, the last two commands do produce slightly different
results regarding the F-ratios. Why would that be, since they should
model the same?
I am not sure, if I got the difference between the first two and the
last two right?
Thank you so much for your help!!
Linn
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