R help - Jan 2005 - binomia data and mixed model

Hi,
I am a first user of R.

I was hoping I could get some help on some data I need to analyze.

The experimental design is a complete randomized design with 2 factors (Source 
material and Depth). The experimental design was suppose to consist of 4 
treatments replicated 3 time, Source 1 and applied at 10 cm and source 2 
applied at 20 cm. During the construction of the treatmetns the depths vary 
considerably so i can't test all my samples based on 10 and 20 cm any more
the
depths are now considered random and not fixed. Each treatment was sampled for 
depth and total density of plants with 3 transects with 28 quadrats per 
transect. The data is very non-normal (lots of zeros) therefore the only way 
to analyze it is to convert to binomial data. Does any one know what type of 
analysis I should use? I was told that a NLmixed model would work but also a 
GLIM mixed model was appropriate. Is there any info using these in R.

Dean D. MacKenzie
Master's of Science Candidate. A.Ag
Department of Renewable Resources
Rm 723 GSB
University of Alberta
Edmonton, AB
T6G 2H1
Office tel: (780) 492-4135
Home tel:   (780) 437-9563

> The experimental design was suppose to consist of 4 
> treatments replicated 3 time, Source 1 and applied at 10 cm 
> and source 2 applied at 20 cm. During the construction of the
> treatmetns the depths vary considerably so i can't test all my
> samples based on 10 and 20 cm any more the depths are now
> considered random and not fixed.
It is not really clear what you measuring ... but do you know the depth even
if it is not exactly 10 and 20 cm? Then, perhaps you could use this variable
in a continous fashion but still as fixed? I do not really see how you can
treat such a variable as random.
> The data is very non-normal (lots of zeros) therefore the only way 
> to analyze it is to convert to binomial data.
I doubt this is the only way but certainly a valid one.
> Does any one know what type of analysis I should use? I was told
> that a NLmixed model would work but also a GLIM mixed model was
> appropriate. Is there any info using these in R.
I do not know about those but you can conduct binomial mixed effects models
by either using glmmPQL in library 'MASS' or GLMM in library
'lme4'.

Also do read:
  author = 	 {Pinheiro, Jose C and Bates, Douglas M},
  title = 	 {Mixed-Effects Models in {S} and {S}-{P}{L}{U}{S}},
  publisher = 	 {Springer},
  year = 	 {2000},
  address = 	 {New York}
and
	AUTHOR = {Venables, W N and Ripley, B D},
	TITLE = {Modern Applied Statistics with {S}},
	PUBLISHER = {Springer},
	YEAR = {2002},
	ADDRESS = {New York},
	EDITION = {fourth}

Regards, Lorenz
- 
Lorenz Gygax, Dr. sc. nat.
Centre for proper housing of ruminants and pigs
Swiss Federal Veterinary Office
agroscope FAT T?nikon, CH-8356 Ettenhausen / Switzerland