kempton.barley.uniformity {agridat} | R Documentation |
Uniformity trial of barley at Cambridge, England, 1978.
A data frame with 196 observations on the following 3 variables.
row
row
col
column
yield
grain yield, kg
A uniformity trial of spring barley planted in 1978. Conducted by the Plant Breeding Institute in Cambridge, England.
Each plot is 5 feet wide, 14 feet long.
Field width: 7 plots * 14 feet = 98 feet
Field length: 28 plots * 5 feet = 140 feet
R. A. Kempton and C. W. Howes (1981). The use of neighbouring plot values in the analysis of variety trials. Applied Statistics, 30, 59–70. http://doi.org/10.2307/2346657
McCullagh, P. and Clifford, D., (2006). Evidence for conformal invariance of crop yields, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science. 462, 2119–2143.
data(kempton.barley.uniformity) dat <- kempton.barley.uniformity if(require(desplot)){ desplot(yield~col*row, dat, aspect=140/98, tick=TRUE, # true aspect main="kempton.barley.uniformity") } # ---------------------------------------------------------------------------- ## Not run: # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91 dat <- transform(dat, xf = factor(col), yf=factor(row)) # asreml3 require(asreml) m1 <- asreml(yield ~ 1, data=dat, rcov=~ar1(xf):ar1(yf)) require(lucid) vc(m1) ## effect component std.error z.ratio constr ## R!variance 0.1044 0.022 4.7 pos ## R!xf.cor 0.2458 0.07484 3.3 uncon ## R!yf.cor 0.8186 0.03822 21 uncon # asreml estimates auto-regression correlations of 0.25, 0.82 # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91 ## End(Not run) # ---------------------------------------------------------------------------- ## Not run: # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91 ## dat <- transform(dat, xf = factor(col), yf=factor(row)) ## require(asreml4) ## m1 <- asreml(yield ~ 1, data=dat, residual = ~ar1(xf):ar1(yf)) ## require(lucid) ## vc(m1) ## ## effect component std.error z.ratio bound ## ## xf:yf(R) 0.1044 0.022 4.7 P 0 ## ## xf:yf!xf!cor 0.2458 0.07484 3.3 U 0 ## ## xf:yf!yf!cor 0.8186 0.03822 21 U 0 ## # asreml estimates auto-regression correlations of 0.25, 0.82 ## # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91 ## End(Not run) # ---------------------------------------------------------------------------- # Kempton defines 4 blocks, randomly assigns variety codes 1-49 in each block, fits # RCB model, computes mean squares for variety and residual. Repeat 40 times. # Kempton's estimate: variety = 1032, residual = 1013 # Our estimate: variety = 825, residual = 1080 fitfun <- function(dat){ dat <- transform(dat, block=factor(ceiling(row/7)), gen=factor(c(sample(1:49),sample(1:49),sample(1:49),sample(1:49)))) m2 <- lm(yield*100 ~ block + gen, dat) anova(m2)[2:3,'Mean Sq'] } set.seed(251) out <- replicate(50, fitfun(dat)) rowMeans(out) # 826 1079 # ----------------------------------------------------------------------------