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.

Usage

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

if (FALSE) { # \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)  
} # }