Growth model of the reared sea urchin Paracentrotus ... - SciViews

Annexes
Code of functions required to run the script
These functions perform various data manipulations and implement the
object-oriented R code for fitting envelope models. Utilities to convert
data, to generate artificial datasets and to run simulations are also
provided.
#===============================================================================#
# #
# Envelope Growth Models v. 1.0. #
# #
#===============================================================================#
#
# by Ph. Grosjean, 2001 (phgrosjean@sciviews.org)
#
# Parameters estimation, analyses and simulations of envelope growth models
# including the fuzzy-remanent growth curve presented in:
# Grosjean, Ph., Ch. Spirlet & M. Jangoux, A functional growth model with
# intraspecific competition applied to sea urchins (in preparation).
# Regression methods include nonlinear quantile regression with an interior
# point algorithm of Koenker, and a custom nonlinear quantile regression
# over the whole quantile range 0 -> 1 specifically developped for envelope
# growth models (models that envelop size distributions with time).
#
# This is a free software distributed under the terms of the GNU Public
# License version 2 or above at your convenience (see licence.txt).
#
#This version is still in development. Use at your own risks!
# To run these samples:
# Source this file in R, version 1.3.0 or higher
# Change current directory to the one containing Plividus.txt and Growth.R
# and enter source("Growth.R", print.eval=TRUE) in R
#(see Growth.R for more details)
###==== Basic data manipulation ===========================================
# Data are issued from Plividus.txt and stored in variable 'dat':
# dat is: - col #1: class (1 by 1 mm in test diameter)
# - col #2: mean diameter for each class (ex: class 1 = 0 to 1 mm, mean diam.
= 0.5 mm)
# - cols #3 to 27: counts of individuals in each class at various increasing
ages (Xxxx where xxx is age from fertilisation in days)
## Transformations of raw data
# Sum per column
colsum