Cucumber yields and quantitative traits

data("cramer.cucumber")

Format

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

Details

The data are used to illustrate path analysis of the correlations between phenotypic traits.

Used with permission of Christopher Cramer.

Source

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.

References

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

Examples

# \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 # }