Title: exampledata. #A,B,C,D,E,F #1,3,0.117,0.044,1,1 #1,3,-0.043,0.022,1,2 #1,3,0.007,0.02,1,3 #2,3,0.142,0.044,1,1 A * # !I B * # !I C #!*10 D #!*10 E * # !I F * # !I # Check/Correct these field definitions. exampledata.csv !SKIP 1 !FCON tabulate C ~ B A F !stats tabulate C ~ B A !stats tabulate C ~ A B !stats tabulate C ~ F B !stats C ~ mu F B D F.B F.D -B.D -F.B.D, # Specify fixed model !r A # Specify random model PREDICT F B !FITMARGIN PREDICT F !PRESENT F B PREDICT B !PRESENT F B 0Notes:
- - - Results from analysis of C - - - Approximate stratum variance decomposition Stratum Degrees-Freedom Variance Component Coefficients Source Model terms Gamma Component Comp/SE % C A 140 140 0.102574E-05 0.133539E-08 0.00 0 B Variance 280 248 1.00000 0.130188E-02 11.14 0 P Warning: Code B - fixed at a boundary (!GP) F - fixed by user ? - liable to change from P to B P - positive definite C - Constrained by user (!VCC) U - unbounded S - Singular Information matrix S means there is no information in the data for this parameter. Very small components with Comp/SE ratios of zero sometimes indicate poor scaling. Consider rescaling the design matrix in such cases. Wald F statistics Source of Variation NumDF DenDFcn Fic Fcn M Pcn 7 mu 1 248.0 1281.91 283.34 . <.001 6 F 3 248.0 201.57 192.33 A <.001 2 B 5 248.0 11.44 11.72 A <.001 4 D 1 248.0 3.31 0.06 A 0.801 8 F.B 9 248.0 3.89 3.40 b <.001 9 F.D 3 248.0 1.06 0.98 B 0.405 10 B.D 4 248.0 1.18 1.18 B 0.323 11 F.B.D 6 248.0 1.26 1.26 C 0.277 Notice: The DenDF values are calculated ignoring fixed/boundary/singular variance parameters using algebraic derivatives. 1 A 140 effects fitted ( 2 are zero)Which shows no variance component associated with A, A big effect of F and B, and interaction. ; no effect of D So you wanted to predict these tables. Tabulation shows 18 combinations of F and B
F1 B1 b2 B3 B4 B5 B6 F2 B1 b2 B3 - - - F3 B1 b2 B3 B4 B5 B6 F4 - - - B4 B5 B6Surprisingly, only 14 combinations are reported from predict F B The ones missing are F1B6 F3B6 F4B5 and F4B6 despite the fact that there is the correct DF (1 + 3 + 5 + 9=18)
My first guess is that this might be a scaling effect but multiplying by 10 did not solve the problem.Second was that it was associated with the NS D regressions. Dropping the F.B.D and B.D model terms resolved the problem. Looking at the ANOVA table again, we see that these terms were deficient in DF (B.D had 4 not 5, F.B.D had 6 not 9) so these singularities were sufficient to make some cells not estimable. Now concerning the F and B tables, given the 6 missing cells, there is no standard way to calculate the margins (except F1 and F3 which are complete). There are two possibilities in ASReml but you must determine which if either is valid. I have added !FITMARGIN to PREDICT F B and this generates marginal means from the F B table assuming that interaction effects associated with missing cells are zero. I have added !PRESENT F B to the other two predict statements so that marginal means are calculated just from those cells in the row/column of F x B table which are present. Neither of these approaches is necessarily appropriate or reasonable. Given the large F effects, B means using the PRESENT strategy will be confounded with the F effects (at least comparisons between the B1 B2 B3 set and the B4 B5 B6 set).
ARG 25 Oct 2008