Hi R-listers,



I would like to compare my field data to an index
model. The index was created by using the following script:

devel.index <- function(values, weights=c(1, 2, 3, 4, 5, 6)) { foo <-  
values*weights
return(apply(foo, 1, sum) / apply(values, 1, sum))
  }

Background:
Surveyed egg embryos have been categorized into 6 stages of
development in the field. The stages in the field data are named ST0,  
ST1,
ST2, ST3, ST4, & Shells.

Q?
1. What is the best way to analyze the field data on embryonic  
development
of 6 stages?
2. Doing this while considering, testing the variables: Veg, HTL,
Aeventexhumed, Sector, Rayos, TotalEggs?
3. And then compare the results to a development index.

  The goal is to determine hatching success in various areas of the  
beach. And
  try to create a development index of these microenvironments.  
Seasonality
  would play a key role. Is this possible?

Please advise. Many thanks!
  J

Definitions of Variables:
HTL - distance to high tide line (continuous)
Veg - distance to vegetation
Aeventexhumed - Event of exhumation
Sector - number measurements along the beach
Rayos - major sections of beach (grouped sectors)
TotalEggs - nest egg density


	[[alternative HTML version deleted]]

