# Fitting a model on continuous response variable using lme4 package in R? Factorial or Nested design?

I am trying to find the effect of plant traits on water infiltration and did some analysis using lme4 with the help of 2 statisticians, both of them suggest different models to check it. I can't decide who I can believe. Also my statistical background is very poor especially with the R. Please let me know which one is correct, or if none of them correct, please nominate the correct one.

Here my data descriptions:

1. Design or nesting of the study
• There are 3 Regions (North, Mid and South)
• Within each region we selected 2 different plant communities, but the communities are consistent across 3 regions
• For each community we got 3 different habitat quality like poor, mid and good
• For each habitat quality we selected 2 replicate sites

So, 3 regions x 2 communities x 3 condition or quality x 2 replicate sites = Totally 36 sites.

1. Each site there are 4 different plant types like tree, shrub etc and we called it Microsites. We did 3 replicate measurements of infiltration for each microsite. So within a site 4 microsites x 3 replicated measurements = totally 12 measurements per site. The measurement is infiltration.

• infiltration (continuous response variable)
2. we want to see the effect of following factors that we recorded

• height (numerical)
• canopy shape (categorical)
• stem width (continuous)

The model suggested by Statistician 1: He says it is nested,

D1$SSI10rnd <-round(D1$SSI10, digits = 0)
mod4 <-glmer(SSI10rnd ~ Height + Shape + Community*CondClass*Microsite +
(1|Region/Community/CondClass/CCRep/Microsite), data=D1, family=poisson)


The model suggested by Statistician 2: As she says our design is factorial, also she highlighted the response variable is continuous

mod_full=lmer(log(SSI10) ~ Region * Community * CondClass * Microsite +
height + shape + width + (1|SiteNumber), data=D1)


*ps: CondClass referring the habitat condition levels

Do you think the site is only random effect for my design? Also, I am not sure how i can distinguish random and fixed effects.

• There's a lot of information within 'Design or nesting' that will need more explanation if you are to get useful feedback here. To put it another way: you are asking us to choose between the opinions of two statisticians while providing us (presumably) with much less information than they had while forming those opinions.
– mkt
Jul 4 '16 at 11:40
• This isn't really coding related; flagging to move to CV Jul 5 '16 at 20:33
• The response is not Poisson so at least that part of model 1 is wrong and with so few levels of each nesting factor it may make sense to specify 5 levels of nesting. You need to provide much more information about the design of your study. Jul 6 '16 at 9:33
• Please clarify the physical relations among the variables you list under "design or nesting." That's needed to determine the nesting vs factorial structure. For example, do different regions have different sites? Are individual microsites specific to particular sites? What exactly is meant by "communities" in your study? A diagram of some sort would be very helpful.
– EdM
Jul 6 '16 at 11:16
• Thank you all for the comments. I have just edited to clarify the design of my study. Hope everything you want to know is in here. Please have a look once. Thanks in advance. Jul 7 '16 at 3:02