logist.data {FlexParamCurve}

Simulated growth of whiskered terns

Description

The logist.data data frame has 1100 rows and 3 columns of records of the simulated masses for whiskered tern chicks between 0 and 21 days of age.

Usage

logist.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 3-parameter positive-negative Richards curve (SSposnegRichards (model 20)), using parameters drawn from normal distributions with the following means (standard deviations):

  Asym=92.35 (15.65)
  K=0.06 (0.138)
  Infl=0.294 (1.72)

These values were taken from Pallisson et al. (2008) for 75 chicks reported. Each simulated individual had 11 measurements stratified through the development period, with 1-2 day random differences in timing of each measurement. This data object has methods for nlme grouped-data classes.

Source

Paillisson, J.-M., Latraube, F. & Reeber, S. (2008) Assessing growth and age of Whiskered Tern Chlidonias hybrida chicks using biometrics. Ardea, 96, 271-277.

Examples

require(stats); require(graphics)
#view data
logist.data
#create list for fixed parameters
modpar(logist.data$age, logist.data$mass, force4par = TRUE, pn.options = "myoptions")
plot(mass ~ age, data = logist.data, subset = id == "0.002",
     xlab = "Chick age (day)", las = 1,
     ylab = "Chick mass (g)",
     main = "logist.data and fitted curve (Chick #2 only)")
change.pnparameters(M=1, pn.options = "myoptions") # set curve to logistic (M=1) in subsequent fit
fm1 <- nls(mass ~ SSposnegRichards(age,Asym=Asym,K=K,Infl=Infl,
           modno=20, pn.options = "myoptions"),
           data = logist.data, subset = id == "0.002")
age <- seq(0, 166, length.out = 101)
lines(age, predict(fm1, list(age = age)))