[R-sig-ME] Rules of thumb for model complexity with small sample size in lme()

[John Ludlam]

Hello,

I have an experiment with six streams in two groups (regulated and control).  At each stream there were five sites (Transect).  At each site there were unreplicated nutrient treatments (N, P, N+P, C).  Light was measured at each site.

Stream	Regulated	Transect	Nitrogen	Phosphorus	R	Light
Cranberry	Regulated	30	C	C	-0.102512563	2042.266667
Cranberry	Regulated	30	C	P	-0.08877551	2042.266667
Cranberry	Regulated	50	C	C	-0.107142857	1283.3
Cranberry	Regulated	50	N	C	-0.059375	1283.3
Cranberry	Regulated	70	C	C	-0.067346939	1336.6
Cranberry	Regulated	70	N	C	-0.063636364	1336.6
...

I would like to know if the response differs among groups (regulated vs control) or is related to light or nutrient treatment.  I have two separate analyses, N = 107 and N = 66 with different numbers of missing values (N = 120 before missing values). 

I think the appropriate model structure is:

lme(Response ~ Regulated + Light + Nitrogen + Phosphorus + Nitrogen:Phosphorus), random=~1|Stream/Transect, data=data, method="ML"))

However, I'm concerned that the model is far too complex for my sample size.  Any advice would be appreciated!

Thanks!

John P. Ludlam, Ph.D. - Fitchburg State University