posneg.data: Simulated growth of black-browed albatrosses
In FlexParamCurve: Tools to Fit Flexible Parametric Curves

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The posneg.data data frame has 1300 rows and 3 columns of records of the simulated masses for black-browed albatross chicks between 0 and 166 days of age.

1	posneg.data

This object of class c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame") containing the following columns:

mass: a numeric vector of chick masses (g).
age: a numeric vector of chick ages (days).
id: an ordered factor indicating unique id of each simulated individual, i.e. which data belongs to which individual.

No published parameter estimates with associated variability are available for positive-negative growth curves. These data were simulated using an 8-parameter positive-negative Richards curve (SSposnegRichards (model 1)), using parameters drawn from normal distributions with the following means (standard deviations):

  Asym=4300 (180)
  K=0.06 (0.01)
  Infl=23 (0.4)
  M=0.1 (0.05)
  RAsym=1433.3 (540) #1/3 of Asym, more variable
  Rk=0.108 (0.03) #1.8 times faster recession, more variable
  Ri=Infl+87.259 (1.7) # more variable but linked to Infl
  RM=M (0.15) #more variable

These values were chosen through comparison of growth curves with Huin and Prince (2000) Fig 2 and variability observed between individual chicks of little penguins in a 10 year dataset (Chiaradia and Nisbet unpublished data). Each simulated individual had 13 measurements stratified through the development period, with 1-13 day random differences in timing of each measurement. This data object has methods for nlme grouped-data classes.

Huin, N. & Prince, P.A. (2000) Chick growth in albatrosses: curve fitting with a twist. Journal of Avian Biology, 31, 418-425.

require(stats); require(graphics)
#view data
posneg.data
#create list for fixed parameters
modpar(posneg.data$age, posneg.data$mass, pn.options = "myoptions")
plot(mass ~ age, data = posneg.data, subset = id == "1",
     xlab = "Chick age (day)", las = 1,
     ylab = "Chick mass (g)",
     main = "posneg.data data and fitted curve (Chick #1 only)")
fm1 <- nls(mass ~ SSposnegRichards(age,Asym=Asym,K=K,Infl=Infl, RAsym=RAsym,
           Rk=Rk,Ri=Ri,modno=22, pn.options= "myoptions"),
           data = posneg.data, subset = id == "1")
age <- seq(0, 166, length.out = 101)
lines(age, predict(fm1, list(age = age)))