FlexParamCurve (version 1.5-6)

posneg.data: Simulated growth of black-browed albatrosses

Description

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.

Usage

posneg.data

Format

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.

Details

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Fig 2 and variability observed between individual chicks of little penguins in a 10 year dataset (Chiaradia and Nisbet unpublished data).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.

Examples

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)))