**Quantum Forest » rblogs**, and kindly contributed to R-bloggers)

Disappeared for a while collecting frequent flyer points. In the process I ‘discovered’ that I live in the middle of nowhere, as it took me 36 hours to reach my conference destination (Estoril, Portugal) through Christchurch, Sydney, Bangkok, Dubai, Madrid and Lisbon.

Where was I? Showing how split-plots look like under the bonnet (hood for you US readers). Yates presented a nice diagram of his oats data set in the paper, so we have the spatial location of each data point which permits us playing with within-trial spatial trends.

Rather than mucking around with typing coordinates we can rely on Kevin Wright’s version of the oats dataset contained in the agridat package. Kevin is a man of mistery, a James Bond of statisticians—so he keeps a low profile—with a keen interest in experimental design and analyses. This chap has put a really nice collection of data sets WITH suggested coding for the analyses, including nlme, lme4, asreml, MCMCglmm and a few other bits and pieces. Recommended!

Plants (triffids excepted) do not move, which means that environmental trends within a trial (things like fertility, water availability, temperature, etc) can affect experimental units in a way that varies with space and which induces correlation of the residuals. Kind of we could be violating the independence assumption if you haven’t got the hint yet.

There are a few ways to model environmental trends (AR processes, simple polynomials, splines, etc) that can be accounted for either through the G matrix (as random effects) or the R matrix. See previous post for explanation of the bits and pieces. We will use here a very popular approach, which is to consider two separable (so we can estimate the bloody things) autoregressive processes, one for rows and one for columns, to model spatial association. In addition, we will have a spatial residual. In summary, the residuals have moved from to . I previously showed the general form of this autoregressive matrices in this post, and you can see the matrix below. In some cases we can also add an independent residual (the so-called nugget) to the residual matrix.

We will first fit a split-plot model considering spatial residuals using asreml because, let’s face it, there is no other package that will give you the flexibility:

require(asreml) require(agridat) # Having a look at the structure of yates.oats # and changing things slightly so it matches # previous post str(yates.oats) names(yates.oats) = c('row', 'col', 'Y', 'N', 'V', 'B') yates.oats$row = factor(yates.oats$row) yates.oats$col = factor(yates.oats$col) yates.oats$N = factor(yates.oats$N) yates.oats$MP = yates.oats$V # Base model (this was used in the previous post) m2 = asreml(Y ~ V*N, random = ~ B/MP, data = yates.oats) summary(m2)$varcomp # gamma component std.error z.ratio constraint # B!B.var 1.2111647 214.4771 168.83404 1.270343 Positive # B:MP!B.var 0.5989373 106.0618 67.87553 1.562593 Positive # R!variance 1.0000000 177.0833 37.33244 4.743416 Positive # Spatial model m3 = asreml(Y ~ V*N, random = ~ B/MP, rcov = ~ ar1(col):ar1(row), data = yates.oats) summary(m3)$varcomp # gamma component std.error z.ratio constraint # B!B.var 0.80338277 169.24347389 156.8662436 1.0789031 Positive # B:MP!B.var 0.49218005 103.68440202 73.6390759 1.4080079 Positive # R!variance 1.00000000 210.66355939 67.4051020 3.1253355 Positive # R!col.cor 0.04484166 0.04484166 0.2006562 0.2234751 Unconstrained # R!row.cor 0.49412567 0.49412567 0.1420397 3.4787860 Unconstrained coef(m3)$fixed coef(m3)$random # effect # V_GoldenRain:N_0 0.0000000 # V_GoldenRain:N_0.2 0.0000000 # V_GoldenRain:N_0.4 0.0000000 # V_GoldenRain:N_0.6 0.0000000 # V_Marvellous:N_0 0.0000000 # V_Marvellous:N_0.2 -0.8691155 # V_Marvellous:N_0.4 -12.4223873 # V_Marvellous:N_0.6 -5.5018907 # V_Victory:N_0 0.0000000 # V_Victory:N_0.2 -1.9580360 # V_Victory:N_0.4 2.1913469 # V_Victory:N_0.6 0.3728648 # N_0 0.0000000 # N_0.2 23.3299154 # N_0.4 40.0570745 # N_0.6 47.1749577 # V_GoldenRain 0.0000000 # V_Marvellous 9.2845952 # V_Victory -5.7259866 # (Intercept) 76.5774292 # effect # B_B1 21.4952875 # B_B2 1.0944484 # B_B3 -5.4336461 # B_B4 -4.4334455 # B_B5 -6.6925874 # B_B6 -6.0300569 # B_B1:MP_GoldenRain 1.3036724 # B_B1:MP_Marvellous -0.9082462 # B_B1:MP_Victory 12.7754283 # B_B2:MP_GoldenRain 1.3286187 # B_B2:MP_Marvellous 7.4181674 # B_B2:MP_Victory -8.0761824 # B_B3:MP_GoldenRain -6.5288220 # B_B3:MP_Marvellous 10.4361799 # B_B3:MP_Victory -7.2367277 # B_B4:MP_GoldenRain 6.6868810 # B_B4:MP_Marvellous -9.2317585 # B_B4:MP_Victory -0.1716372 # B_B5:MP_GoldenRain 2.4635492 # B_B5:MP_Marvellous -9.7086196 # B_B5:MP_Victory 3.1443067 # B_B6:MP_GoldenRain -5.2538993 # B_B6:MP_Marvellous 1.9942770 # B_B6:MP_Victory -0.4351876 # In a larger experiment we could try fitting a nugget using units m4 = asreml(Y ~ V*N, random = ~ B/MP + units, rcov = ~ ar1(col):ar1(row), data = yates.oats) summary(m4) # However in this small experiments the system # goes crazy and results meaningless # gamma component std.error z.ratio constraint # B!B.var 0.006759124 223.70262 185.3816 1.206714 Positive # B:MP!B.var 0.001339017 44.31663 29.2004 1.517672 Positive # units!units.var 0.003150356 104.26542 34.2738 3.042132 Positive # R!variance 1.000000000 33096.39128 19480.8333 1.698921 Positive # R!col.cor 0.999000000 0.99900 NA NA Fixed # R!row.cor 0.999000000 0.99900 NA NA Fixed

So we have to build an autoregressive correlation matrix for rows, one for columns and multiply the whole thing for a spatial variance. Then we can add an independent residual (the nugget, if we want—and can estimate—one). Peter Dalgaard has neat code for building the autocorrelation matrix. And going back to the code in the previous post:

ar.matrix = function(ar, dim) { M = diag(dim) M = ar^abs(row(M)-col(M)) return(M) } # Variance components (from m3) varB = 169.243 varMP = 103.684 varR = 210.664 arcol = 0.045 arrow = 0.494 # Basic vector and matrices: y, X, Z, G & R y = matrix(yates.oats$Y, nrow = dim(yates.oats)[1], ncol = 1) X = model.matrix(~ V*N, data = yates.oats) Z = model.matrix(~ B/MP - 1, data = yates.oats, contrasts.arg = list(B = contrasts(yates.oats$B, contrasts = F), MP = contrasts(yates.oats$MP, contrasts = F))) G = diag(c(rep(varB, 6), rep(varMP, 18))) # Only change from previous post is building the R matrix Rcol = ar.matrix(arcol, 4) Rrow = ar.matrix(arrow, 18) Rcol # Having a look at the structure # [,1] [,2] [,3] [,4] # [1,] 1.0000e+00 0.045000 0.002025 9.1125e-05 # [2,] 4.5000e-02 1.000000 0.045000 2.0250e-03 # [3,] 2.0250e-03 0.045000 1.000000 4.5000e-02 # [4,] 9.1125e-05 0.002025 0.045000 1.0000e+00 R = varR * kronecker(Rcol, Rrow) Rinv = solve(R) # Components of C XpX = t(X) %*% Rinv %*% X ZpZ = t(Z) %*% Rinv %*% Z XpZ = t(X) %*% Rinv %*% Z ZpX = t(Z) %*% Rinv %*% X Xpy = t(X) %*% Rinv %*% y Zpy = t(Z) %*% Rinv %*% y # Building C * [b a] = RHS C = rbind(cbind(XpX, XpZ), cbind(ZpX, ZpZ + solve(G))) RHS = rbind(Xpy, Zpy) blup = solve(C, RHS) blup # [,1] # (Intercept) 76.5778238 # VMarvellous 9.2853002 # VVictory -5.7262894 # N0.2 23.3283060 # N0.4 40.0555464 # N0.6 47.1740348 # VMarvellous:N0.2 -0.8682597 # VVictory:N0.2 -1.9568979 # VMarvellous:N0.4 -12.4200362 # VVictory:N0.4 2.1912083 # VMarvellous:N0.6 -5.5017225 # VVictory:N0.6 0.3732453 # BB1 21.4974445 # BB2 1.0949433 # BB3 -5.4344098 # BB4 -4.4333080 # BB5 -6.6948783 # BB6 -6.0297918 # BB1:MPGoldenRain 1.3047656 # BB2:MPGoldenRain 1.3294043 # BB3:MPGoldenRain -6.5286993 # BB4:MPGoldenRain 6.6855568 # BB5:MPGoldenRain 2.4624436 # BB6:MPGoldenRain -5.2534710 # BB1:MPMarvellous -0.9096022 # BB2:MPMarvellous 7.4170634 # BB3:MPMarvellous 10.4349240 # BB4:MPMarvellous -9.2293528 # BB5:MPMarvellous -9.7080694 # BB6:MPMarvellous 1.9950370 # BB1:MPVictory 12.7749000 # BB2:MPVictory -8.0756682 # BB3:MPVictory -7.2355284 # BB4:MPVictory -0.1721988 # BB5:MPVictory 3.1441163 # BB6:MPVictory -0.4356209

Which are the same results one gets from ASReml-R. QED.

P.S. Many thanks to Kevin Wright for answering my questions about agridat.

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