gilmour.slatehall {agridat} | R Documentation |
Yields for a trial at Slate Hall Farm in 1978.
A data frame with 150 observations on the following 5 variables.
row
row
col
column
yield
yield (grams/plot)
gen
genotype factor, 25 levels
rep
rep factor, 6 levels
The trial was of spring wheat at Slate Hall Farm in 1978. The experiment was a balanced lattice with 25 varieties in 6 replicates. The 'rep' labels are arbitrary (no rep labels appeared in the source data). Each row within a rep is an incomplete block. The plot size was 1.5 meters by 4 meters.
Field width: 10 plots x 4 m = 40 m
Field length: 15 plots x 1.5 meters = 22.5 m
Arthur R Gilmour and Brian R Cullis and Arunas P Verbyla (1997). Accounting for natural and extraneous variation in the analysis of field experiments. Journal of Agricultural, Biological, and Environmental Statistics, 2, 269-293. http://doi.org/10.2307/1400446
None.
data(gilmour.slatehall) dat <- gilmour.slatehall if(require(desplot)){ desplot(yield ~ col * row, dat, aspect=22.5/40, num=gen, out1=rep, cex=1, main="gilmour.slatehall") } # ---------------------------------------------------------------------------- ## Not run: # Model 4 of Gilmour et al 1997 # asreml3 require(asreml) dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$xf, dat$yf), ] m4 <- asreml(yield ~ gen + lin(row), data=dat, random = ~ dev(row) + dev(col), rcov = ~ ar1(xf):ar1(yf)) coef(m4)$fixed[1] # linear row # [1] 31.72252 # (sign switch due to row ordering) require(lucid) vc(m4) ## effect component std.error z.ratio constr ## dev(row) 20290 10260 2 pos ## dev(col) 2519 1959 1.3 pos ## R!variance 23950 4616 5.2 pos ## R!xf.cor 0.439 0.113 3.9 uncon ## R!yf.cor 0.125 0.117 1.1 uncon plot(variogram(m4)) ## End(Not run) # ---------------------------------------------------------------------------- ## Not run: # Model 4 of Gilmour et al 1997 ## require(asreml4) ## dat <- transform(dat, xf=factor(col), yf=factor(row)) ## dat <- dat[order(dat$xf, dat$yf), ] ## m4 <- asreml(yield ~ gen + lin(row), data=dat, ## random = ~ dev(row) + dev(col), ## resid = ~ ar1(xf):ar1(yf)) ## coef(m4)$fixed[1] # linear row ## # [1] 31.72252 # (sign switch due to row ordering) ## require(lucid) ## vc(m4) ## ## effect component std.error z.ratio bound ## ## dev(col) 2519 1959 1.3 P 0 ## ## dev(row) 20290 10260 2 P 0 ## ## xf:yf(R) 23950 4616 5.2 P 0 ## ## xf:yf!xf!cor 0.439 0.113 3.9 U 0 ## ## xf:yf!yf!cor 0.125 0.117 1.1 U 0 ## plot(varioGram(m4)) ## End(Not run)