Yields of four grasses for a wide range of nitrogen fertilizer

Yields of four grasses for a wide range of nitrogen fertilizer, conducted over 3 years.

data("reid.grasses")

Format

A data frame with 210 observations on the following 5 variables.

nitro

nitrogen, 21 numeric levels

year

Y1, Y2, or Y3

gen

genotype

drymatter

dry matter content

protein

protein content

Details

Experiment at the Hannah Research Institute, Ayr.

Single plots were planted to 4 different kinds of grasses. Within each plot, 21 nitrogen treatments were randomized.

Reid modeled the dry matter yield with four-parameter logistic curves of the form y = a - b exp(-cx^d).

Source

D. Reid (1985). A comparison of the yield responses of four grasses to a wide range of nitrogen application rates. J. Agric. Sci., 105, 381-387. Table 1 & 3. https://doi.org/10.1017/S0021859600056434

References

None

Examples

# \dontrun{
  
  library(agridat)
  data(reid.grasses)
  dat <- reid.grasses

  libs(latticeExtra)
  foo <- xyplot(drymatter + protein ~ nitro|year, dat, group=gen,
                auto.key=list(columns=4),
                as.table=TRUE, type=c('p','l'),
                main="reid.grasses",ylab="drymatter/protein trait value",
                scales=list(y=list(relation="free")))
  combineLimits(foo)

  # devtools::run_examples does NOT like groupedData
  if (0){
    libs(nlme)
    dat2 <- dat
    dat2$indiv <- paste(dat$year, dat$gen) # individual year+genotype curves
    
    # use all data to get initial values
    inits <- getInitial(drymatter ~ SSfpl(nitro, A, B, xmid, scal), data = dat2)
    inits
    ##         A          B       xmid       scal 
    ## -4.167902  12.139796  68.764796 128.313106 
    xvals <- 0:800
    y1 <- with(as.list(inits), SSfpl(xvals, A, B, xmid, scal))
    plot(drymatter ~ nitro, dat2)
    lines(xvals,y1)
    
    # must have groupedData object to use augPred
    dat2 <- groupedData(drymatter ~ nitro|indiv, data=dat2)
    plot(dat2)
    
    # without 'random', all effects are included in 'random'
    m1 <- nlme(drymatter ~ SSfpl(nitro, A, B, xmid,scale),
               data= dat2,
               fixed= A + B + xmid + scale ~ 1,
               random = A + B + xmid + scale ~ 1|indiv,
               start=inits)
    fixef(m1)
    summary(m1)
    plot(augPred(m1, level=0:1),
         main="reid.grasses - observed/predicted data") # only works with groupedData object
  } # if(0)  
# }