lonnquist.maize {agridat} | R Documentation |
Half diallel of maize
data("lonnquist.maize")
A data frame with 78 observations on the following 3 variables.
p1
parent 1 factor
p2
parent 2 factor
yield
yield
Twelve hybrids were selfed/crossed in a half-diallel design planted in 3 reps at 2 locations in 2 years. The data here are means adjusted for block effects.
J. H. Lonnquist, C. O. Gardner. (1961) Heterosis in Intervarietal Crosses in Maize and Its Implication in Breeding Procedures. Crop Science, 1, 179-183. Table 1.
Mohring, Melchinger, Piepho. (2011). REML-Based Diallel Analysis. Crop Science, 51, 470-478. http://doi.org/10.2135/cropsci2010.05.0272
C. O. Gardner and S. A. Eberhart. 1966. Analysis and Interpretation of the Variety Cross Diallel and Related Populations. Biometrics, 22, 439-452. http://doi.org/10.2307/2528181
data(lonnquist.maize) dat <- lonnquist.maize dat <- transform(dat, p1=factor(p1, levels=c("C","L","M","H","G","P","B","RM","N","K","R2","K2")), p2=factor(p2, levels=c("C","L","M","H","G","P","B","RM","N","K","R2","K2"))) require(lattice) redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997")) levelplot(yield ~ p1*p2, dat, col.regions=redblue, main="lonnquist.maize - yield of diallel cross") # Calculate the F1 means in Lonnquist, table 1 if(require(reshape2)){ mat <- acast(dat, p1~p2) mat[upper.tri(mat)] <- t(mat)[upper.tri(mat)] # make symmetric diag(mat) <- NA round(rowMeans(mat, na.rm=TRUE),1) ## C L M H G P B RM N K R2 K2 ## 94.8 89.2 95.0 96.4 95.3 95.2 97.3 93.7 95.0 94.0 98.9 102.4 } # ---------------------------------------------------------------------------- ## Not run: # Mohring 2011 used 6 varieties to calculate GCA & SCA # Matches Table 3, column 2 d2 <- subset(dat, is.element(p1, c("M","H","G","B","K","K2")) & is.element(p2, c("M","H","G","B","K","K2"))) d2 <- droplevels(d2) # asreml4 require(asreml) m2 <- asreml(yield~ 1, data=d2, random = ~ p1 + and(p2)) require(lucid) vc(m2) ## effect component std.error z.ratio con ## p1!p1.var 3.865 3.774 1 Positive ## R!variance 15.93 5.817 2.7 Positive # Calculate GCA effects m3 <- asreml(yield~ p1 + and(p2), data=d2) coef(m3)$fixed-1.462 # Matches Gardner 1966, Table 5, Griffing method ## End(Not run) # ---------------------------------------------------------------------------- ## Not run: # Mohring 2011 used 6 varieties to calculate GCA & SCA # Matches Table 3, column 2 ## d2 <- subset(dat, p1 ## p2 ## d2 <- droplevels(d2) ## require(asreml4) ## m2 <- asreml(yield~ 1, data=d2, random = ~ p1 + and(p2)) ## require(lucid) ## vc(m2) ## ## effect component std.error z.ratio con ## ## p1!p1.var 3.865 3.774 1 Positive ## ## R!variance 15.93 5.817 2.7 Positive ## # Calculate GCA effects ## m3 <- asreml(yield~ p1 + and(p2), data=d2) ## coef(m3)$fixed-1.462 ## # Matches Gardner 1966, Table 5, Griffing method ## End(Not run) # ----------------------------------------------------------------------------