bridges.cucumber {agridat} | R Documentation |
Cucumber yields in latin square design at two locs.
A data frame with 32 observations on the following 5 variables.
loc
location
gen
genotype/cultivar
row
row
col
column
yield
weight of marketable fruit per plot
Conducted at Clemson University in 1985. four cucumber cultivars were grown in a latin square design at Clemson, SC, and Tifton, GA.
Separate variances are modeled each location.
Plot dimensions are not given.
Bridges (1989) used this data to illustrate fitting a heterogeneous mixed model.
William Bridges, 1989. Analysis of a plant breeding experiment with heterogeneous variances using mixed model equations. Applications of mixed models in agriculture and related disciplines, S. Coop. Ser. Bull, 45–51.
Used with permission of William Bridges.
data(bridges.cucumber) dat <- bridges.cucumber dat <- transform(dat, rowf=factor(row), colf=factor(col)) if(require(desplot)){ desplot(yield~col*row|loc, data=dat, # aspect unknown text=gen, cex=1, main="bridges.cucumber") } # ---------------------------------------------------------------------------- ## Not run: # Graphical inference test for heterogenous variances require(nullabor) # Create a lineup of datasets fun <- null_permute("loc") dat20 <- lineup(fun, dat, n=20, pos=9) # Now plot library(lattice) bwplot(yield ~ loc|factor(.sample), dat20, main="bridges.cucumber - graphical inference") ## End(Not run) # ---------------------------------------------------------------------------- ## Not run: # asreml3 require(asreml) ## Random row/col/resid. Same as Bridges 1989, p. 147 m1 <- asreml(yield ~ 1 + gen + loc + loc:gen, random = ~ rowf:loc + colf:loc, data=dat) require(lucid) vc(m1) ## effect component std.error z.ratio constr ## rowf:loc!rowf.var 31.62 23.02 1.4 pos ## colf:loc!colf.var 18.08 15.32 1.2 pos ## R!variance 31.48 12.85 2.4 pos ## Random row/col/resid at each loc. Matches p. 147 m2 <- asreml(yield ~ 1 + gen + loc + loc:gen, random = ~ at(loc):rowf + at(loc):colf, data=dat, rcov = ~at(loc):units) vc(m2) ## effect component std.error z.ratio constr ## at(loc, Clemson):rowf!rowf.var 32.32 36.58 0.88 pos ## at(loc, Tifton):rowf!rowf.var 30.92 28.63 1.1 pos ## at(loc, Clemson):colf!colf.var 22.55 28.78 0.78 pos ## at(loc, Tifton):colf!colf.var 13.62 14.59 0.93 pos ## loc_Clemson!variance 46.85 27.05 1.7 pos ## loc_Tifton!variance 16.11 9.299 1.7 pos predict(m2, data=dat, classify='loc:gen')$predictions$pvals ## loc gen Predicted Std Err Status ## Clemson Dasher 45.55 5.043 Estimable ## Clemson Guardian 31.62 5.043 Estimable ## Clemson Poinsett 21.42 5.043 Estimable ## Clemson Sprint 25.95 5.043 Estimable ## Tifton Dasher 50.48 3.894 Estimable ## Tifton Guardian 38.72 3.894 Estimable ## Tifton Poinsett 33.01 3.894 Estimable ## Tifton Sprint 39.18 3.894 Estimable # Is a heterogeneous model justified? Maybe not. m1$loglik ## -67.35585 m2$loglik ## -66.35621 ## End(Not run) # ---------------------------------------------------------------------------- ## Not run: ## require(asreml4) ## ## Random row/col/resid. Same as Bridges 1989, p. 147 ## m1 <- asreml(yield ~ 1 + gen + loc + loc:gen, ## random = ~ rowf:loc + colf:loc, data=dat) ## require(lucid) ## vc(m1) ## ## effect component std.error z.ratio bound ## ## rowf:loc 31.62 23.02 1.4 P 0 ## ## colf:loc 18.08 15.32 1.2 P 0 ## ## units(R) 31.48 12.85 2.4 P 0 ## ## Random row/col/resid at each loc. Matches p. 147 ## m2 <- asreml(yield ~ 1 + gen + loc + loc:gen, ## random = ~ at(loc):rowf + at(loc):colf, data=dat, ## resid = ~ dsum( ~ units|loc)) ## vc(m2) ## ## effect component std.error z.ratio bound ## ## at(loc, Clemson):rowf 32.32 36.58 0.88 P 0 ## ## at(loc, Tifton):rowf 30.92 28.63 1.1 P 0 ## ## at(loc, Clemson):colf 22.55 28.78 0.78 P 0 ## ## at(loc, Tifton):colf 13.62 14.59 0.93 P 0 ## ## loc_Clemson(R) 46.85 27.05 1.7 P 0 ## ## loc_Tifton(R) 16.11 9.299 1.7 P 0 ## predict(m2, data=dat, classify='loc:gen')$pvals ## ## loc gen predicted.value std.error status ## ## 1 Clemson Dasher 45.6 5.04 Estimable ## ## 2 Clemson Guardian 31.6 5.04 Estimable ## ## 3 Clemson Poinsett 21.4 5.04 Estimable ## ## 4 Clemson Sprint 26 5.04 Estimable ## ## 5 Tifton Dasher 50.5 3.89 Estimable ## ## 6 Tifton Guardian 38.7 3.89 Estimable ## ## 7 Tifton Poinsett 33 3.89 Estimable ## ## 8 Tifton Sprint 39.2 3.89 Estimable ## # Is a heterogeneous model justified? Maybe not. ## m1$loglik ## ## -67.35585 ## m2$loglik ## ## -66.35621 ## End(Not run)