The following output results from fitting models using lmer and lm to data arising from a split-plot experiment (#320 from "Small Data Sets" by Hand et al. 1994). The data is given at the bottom of this message. My question is why is the sum of squares for variety (V) different in the ANOVA table generated from the lmer model fit from that generated by the lm model fit. The decomposition of the sum of squares should be the same regardless of whether block is treated as random of fixed. Or am I misinterpreting the ANOVA table from the lmer fit?
I noticed that other people have asked similar questions in the past, but I haven't seen a satisfactory explanation.
Jim Booth. > B=factor(block) > V=factor(variety) > N=factor(nitrogen) > Y=yield > lmm.split=lmer(Y~V+N+V:N+(1|B)+(1|B:V)+(1|B:N)) > anova(lmm.split) Analysis of Variance Table Df Sum Sq Mean Sq F value V 2 526.1 263.0 1.4853 N 3 20020.5 6673.5 37.6856 V:N 6 321.8 53.6 0.3028 > lm.split=lm(Y~B*V+B*N+V*N) > anova(lm.split) Analysis of Variance Table Response: Y Df Sum Sq Mean Sq F value Pr(>F) B 5 15875.3 3175.1 15.4114 1.609e-07 *** V 2 1786.4 893.2 4.3354 0.02219 * N 3 20020.5 6673.5 32.3926 1.540e-09 *** B:V 10 6013.3 601.3 2.9188 0.01123 * B:N 15 1788.2 119.2 0.5786 0.86816 V:N 6 321.7 53.6 0.2603 0.95103 Residuals 30 6180.6 206.0 > > split block variety nitrogen yield 1 1 1 0 111 2 1 1 1 130 3 1 1 2 157 4 1 1 4 174 5 1 2 0 117 6 1 2 1 114 7 1 2 2 161 8 1 2 4 141 9 1 3 0 105 10 1 3 1 140 11 1 3 2 118 12 1 3 4 156 13 2 1 0 61 14 2 1 1 91 15 2 1 2 97 16 2 1 4 100 17 2 2 0 70 18 2 2 1 108 19 2 2 2 126 20 2 2 4 149 21 2 3 0 96 22 2 3 1 124 23 2 3 2 121 24 2 3 4 144 25 3 1 0 68 26 3 1 1 64 27 3 1 2 112 28 3 1 4 86 29 3 2 0 60 30 3 2 1 102 31 3 2 2 89 32 3 2 4 96 33 3 3 0 89 34 3 3 1 129 35 3 3 2 132 36 3 3 4 124 37 4 1 0 74 38 4 1 1 89 39 4 1 2 81 40 4 1 4 122 41 4 2 0 64 42 4 2 1 103 43 4 2 2 132 44 4 2 4 133 45 4 3 0 70 46 4 3 1 89 47 4 3 2 104 48 4 3 4 117 49 5 1 0 62 50 5 1 1 90 51 5 1 2 100 52 5 1 4 116 53 5 2 0 80 54 5 2 1 82 55 5 2 2 94 56 5 2 4 126 57 5 3 0 63 58 5 3 1 70 59 5 3 2 109 60 5 3 4 99 61 6 1 0 53 62 6 1 1 74 63 6 1 2 118 64 6 1 4 113 65 6 2 0 89 66 6 2 1 82 67 6 2 2 86 68 6 2 4 104 69 6 3 0 97 70 6 3 1 99 71 6 3 2 119 72 6 3 4 121
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