cramer.cucumber.Rd
Cucumber yields and quantitative traits
data("cramer.cucumber")
A data frame with 24 observations on the following 9 variables.
cycle
cycle
rep
replicate
plants
plants per plot
flowers
number of pistillate flowers
branches
number of branches
leaves
number of leaves
totalfruit
total fruit number
culledfruit
culled fruit number
earlyfruit
early fruit number
The data are used to illustrate path analysis of the correlations between phenotypic traits.
Used with permission of Christopher Cramer.
Christopher S. Cramer, Todd C. Wehner, and Sandra B. Donaghy. 1999. Path Coefficient Analysis of Quantitative Traits. In: Handbook of Formulas and Software for Plant Geneticists and Breeders, page 89.
Cramer, C. S., T. C. Wehner, and S. B. Donaghy. 1999. PATHSAS: a SAS computer program for path coefficient analysis of quantitative data. J. Hered, 90, 260-262 https://doi.org/10.1093/jhered/90.1.260
# \dontrun{ library(agridat) data(cramer.cucumber) dat <- cramer.cucumber libs(lattice) splom(dat[3:9], group=dat$cycle, main="cramer.cucumber - traits by cycle", auto.key=list(columns=3))#> Error in eval(substitute(groups), data, environment(formula)): object 'dat' not found# derived traits dat <- transform(dat, marketable = totalfruit-culledfruit, branchesperplant = branches/plants, nodesperbranch = leaves/(branches+plants), femalenodes = flowers+totalfruit) dat <- transform(dat, perfenod = (femalenodes/leaves), fruitset = totalfruit/flowers, fruitperplant = totalfruit / plants, marketableperplant = marketable/plants, earlyperplant=earlyfruit/plants) # just use cycle 1 dat1 <- subset(dat, cycle==1) # define independent and dependent variables indep <- c("branchesperplant", "nodesperbranch", "perfenod", "fruitset") dep0 <- "fruitperplant" dep <- c("marketable","earlyperplant") # standardize trait data for cycle 1 sdat <- data.frame(scale(dat1[1:8, c(indep,dep0,dep)])) # slopes for dep0 ~ indep X <- as.matrix(sdat[,indep]) Y <- as.matrix(sdat[,c(dep0)]) # estdep <- solve(t(X) estdep <- solve(crossprod(X), crossprod(X,Y)) estdep#> [,1] #> branchesperplant 0.7160269 #> nodesperbranch 0.3415537 #> perfenod 0.2316693 #> fruitset 0.2985557## branchesperplant 0.7160269 ## nodesperbranch 0.3415537 ## perfenod 0.2316693 ## fruitset 0.2985557 # slopes for dep ~ dep0 X <- as.matrix(sdat[,dep0]) Y <- as.matrix(sdat[,c(dep)]) # estind2 <- solve(t(X) estind2 <- solve(crossprod(X), crossprod(X,Y)) estind2#> marketable earlyperplant #> [1,] 0.97196 0.8828393## marketable earlyperplant ## 0.97196 0.8828393 # correlation coefficients for indep variables corrind=cor(sdat[,indep]) round(corrind,2)#> branchesperplant nodesperbranch perfenod fruitset #> branchesperplant 1.00 0.52 -0.24 0.09 #> nodesperbranch 0.52 1.00 -0.44 0.14 #> perfenod -0.24 -0.44 1.00 0.04 #> fruitset 0.09 0.14 0.04 1.00## branchesperplant nodesperbranch perfenod fruitset ## branchesperplant 1.00 0.52 -0.24 0.09 ## nodesperbranch 0.52 1.00 -0.44 0.14 ## perfenod -0.24 -0.44 1.00 0.04 ## fruitset 0.09 0.14 0.04 1.00 # Correlation coefficients for dependent variables corrdep=cor(sdat[,c(dep0, dep)]) round(corrdep,2)#> fruitperplant marketable earlyperplant #> fruitperplant 1.00 0.97 0.88 #> marketable 0.97 1.00 0.96 #> earlyperplant 0.88 0.96 1.00## fruitperplant marketable earlyperplant ## fruitperplant 1.00 0.97 0.88 ## marketable 0.97 1.00 0.96 ## earlyperplant 0.88 0.96 1.00 result = corrind result = result*matrix(estdep,ncol=4,nrow=4,byrow=TRUE) round(result,2) # match SAS output columns 1-4#> branchesperplant nodesperbranch perfenod fruitset #> branchesperplant 0.72 0.18 -0.06 0.03 #> nodesperbranch 0.37 0.34 -0.10 0.04 #> perfenod -0.17 -0.15 0.23 0.01 #> fruitset 0.07 0.05 0.01 0.30## branchesperplant nodesperbranch perfenod fruitset ## branchesperplant 0.72 0.18 -0.06 0.03 ## nodesperbranch 0.37 0.34 -0.10 0.04 ## perfenod -0.17 -0.15 0.23 0.01 ## fruitset 0.07 0.05 0.01 0.30 resdep0 = rowSums(result) resdep <- cbind(resdep0,resdep0)*matrix(estind2, nrow=4,ncol=2,byrow=TRUE) colnames(resdep) <- dep # slightly different from SAS output last 2 columns round(cbind(fruitperplant=resdep0, round(resdep,2)),2)#> fruitperplant marketable earlyperplant #> branchesperplant 0.87 0.84 0.76 #> nodesperbranch 0.65 0.63 0.58 #> perfenod -0.08 -0.08 -0.07 #> fruitset 0.42 0.41 0.37## fruitperplant marketable earlyperplant ## branchesperplant 0.87 0.84 0.76 ## nodesperbranch 0.65 0.63 0.58 ## perfenod -0.08 -0.08 -0.07 ## fruitset 0.42 0.41 0.37 # }