agridat (version 1.23)

cramer.cucumber: Cucumber yields and quantitative traits

Description

Cucumber yields and quantitative traits

Usage

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.

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

if (FALSE) {

  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))


  # 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
  ## 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
  ##     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

  # 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

  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

  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
}