burgueno.rowcol {agridat} | R Documentation |
Row-column design
data("burgueno.rowcol")
A data frame with 128 observations on the following 5 variables.
rep
rep, 2 levels
row
row
col
column
gen
genotype, 64 levels
yield
yield, tons/ha
A field experiment with two contiguous replicates in 8 rows, 16 columns.
The plot size is not given.
J Burgueno, A Cadena, J Crossa, M Banziger, A Gilmour, B Cullis. 2000. User's guide for spatial analysis of field variety trials using ASREML. CIMMYT.
Electronic version of the data obtained from CropStat software.
Used with permission of Juan Burgueno.
data(burgueno.rowcol) dat <- burgueno.rowcol # Two contiguous reps in 8 rows, 16 columns if(require(desplot)){ desplot(yield ~ col*row, data=dat, out1=rep, # aspect unknown text=gen, shorten="none", cex=.75, main="burgueno.rowcol") } ## Not run: require(lme4) require(lucid) # Random rep, row and col within rep m1 <- lmer(yield ~ gen + (1|rep) + (1|rep:row) + (1|rep:col), data=dat) vc(m1) # Match components of Burgueno p. 40 ## grp var1 var2 vcov sdcor ## rep:col (Intercept) <NA> 0.2189 0.4679 ## rep:row (Intercept) <NA> 0.1646 0.4057 ## rep (Intercept) <NA> 0.1916 0.4378 ## Residual <NA> <NA> 0.1796 0.4238 ## End(Not run) # ---------------------------------------------------------------------------- ## Not run: # asreml3 require(asreml) # AR1 x AR1 with linear row/col effects, random spline row/col dat <- transform(dat, xf=factor(col), yf=factor(row)) dat <- dat[order(dat$xf,dat$yf),] m2 <- asreml(yield ~ gen + lin(yf) + lin(xf), data=dat, random = ~ spl(yf) + spl(xf), rcov = ~ ar1(xf):ar1(yf)) m2 <- update(m2) # More iterations # Scaling of spl components has changed in asreml from old versions require(lucid) vc(m2) # Match Burgueno p. 42 ## effect component std.error z.ratio constr ## spl(yf) 0.09077 0.08252 1.1 pos ## spl(xf) 0.08108 0.0821 0.99 pos ## R!variance 0.1482 0.03119 4.8 pos ## R!xf.cor 0.1152 0.2269 0.51 uncon ## R!yf.cor 0.009436 0.2414 0.039 uncon ## plot(variogram(m2), main="burgueno.rowcol") ## End(Not run) # ---------------------------------------------------------------------------- ## Not run: ## require(asreml4) ## # AR1 x AR1 with linear row/col effects, random spline row/col ## dat <- transform(dat, xf=factor(col), yf=factor(row)) ## dat <- dat[order(dat$xf,dat$yf),] ## m2 <- asreml(yield ~ gen + lin(yf) + lin(xf), data=dat, ## random = ~ spl(yf) + spl(xf), ## resid = ~ ar1(xf):ar1(yf)) ## m2 <- update(m2) # More iterations ## # Scaling of spl components has changed in asreml from old versions ## require(lucid) ## vc(m2) # Match Burgueno p. 42 ## ## effect component std.error z.ratio bound ## ## spl(yf) 0.09077 0.08252 1.1 P 0 ## ## spl(xf) 0.08107 0.08209 0.99 P 0 ## ## xf:yf(R) 0.1482 0.03119 4.8 P 0 ## ## xf:yf!xf!cor 0.1152 0.2269 0.51 U 0.1 ## ## xf:yf!yf!cor 0.009467 0.2414 0.039 U 0.9 ## plot(varioGram(m2), main="burgueno.rowcol") ## End(Not run)