Dave, your situation is clearer now. You wrote (see the full context at

the end of the message):

> From this, you will see that I have 4 control sites and 7 treatment

> sites that are measured each week. All 13 locations have different

> names, and Location is a random varaible. Is Location nested within

> Habitat? I thought it was, but maybe I am wrong. Perhaps it is a

> random variable that is not nested?

I think so, or at least that nesting is irrelevant. If all factors were

fixed, to specify a nesting of Location within Habitat, you should write

the term "Habitat/Location", but that is equal to "Habitat +

Habitat:Location", and since there cannot be two different values of

Habitat for the same Location, that's exactly the same as "Habitat +

Location". So you can safely separate both terms.

Moreover, the different type of effect for Habitat and Location makes

the nesting notation unsuitable, because Habitat is a fixed effect,

whereas Habitat:Location (i.e. Location) is random. If you put

"Habitat/Location" in the "fixed" part of the formula, both terms would

be treated as fixed, and if you put it in the "random" part, both would

be treated as random, and you don't want that, I suppose.

> My main goal is to look for an effect of Habitat. But if there is a

> significant Week x Habitat interaction, I would examine the effect of

> Habitat separately for each Week.

>

> Hopefully, the above helps to clarify my situation. I should

re-state,

> I would like to use an lmer or lme syntax to properly analyze these

> data, especially given that they are counts, I would like to try

family

> = poisson or quasipoisson.

If you need a generalized linear model, you can try lmer (in package

lme4), since it supports the "family" argument where you can specify the

type of error distribution. On the other hand, lme (in package nlme)

only considers normal errors.

Regarding the formula, I like building formulas term by term, asking

myself if each possible term has a potential effect, and including it in

the model if I can answer "yes". From what you have said, I can infer

that you do think that there may be a Week:Habitat interaction, so that

term must be in your formula. Now:

1. Do you think that the habitat may influence your outcome (the

counts), regardless of the other factors? I guess you do, so let's

include Habitat as well.

2. Do you think that Week may influence the counts, regardless of the

other factors?

- 2.1. If so, the fixed part of your formula would be Habitat*Week (=

Habitat + Week + Week:Habitat)

- 2.2. Otherwise, it would be Habitat/Week (= Habitat +

Habitat:Week)

As already commented on, the random part would just be Location. In

theory, since each location is measured in various weeks, you might

consider that the (fixed) effect of Week could be influenced by the

random Location as well, and in that case you would have an additional

random term, the Location:Week interaction. (I.e., you could write the

random term as "Location/Week".) However, in your data set there is only

one observation for each value of Location:Week, so it would be

impossible to distinguish that random term from the residual error, and

you may just omit it.

All in all, you can try:

m. <- lmer(CO ~ Habitat*Week + (1|Location), family=poisson)

or if Week is only relevant for different types of habitat:

m. <- lmer(CO ~ Habitat/Week + (1|Location), family=poisson)

I must admit that I'm not used to analyse generalized linear models, so

I don't know if that approach is correct, but I'd say that's the code to

do what you asked for.

Now, the bad news is that perhaps you are expecting to get p-values

from anova(m.), but you won't get it. Douglas Bates explained why here:

https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html
On the other hand, you can get p-values from Anova (from package car),

instead of anova, but I don't entirely understand the calculations of

that approach.

Helios

>>> El día 29/09/2011 a las 20:49, Dave Robichaud <

[hidden email]>

escribió:

> Hi again,

>

> Thank you very much for taking the time to respond to my question. I

am

> sorry that my explanation was confusing. Please allow me to try to

clarify.

>

> First, please ignore my attempts to define a lmer model. By putting

> forward my best first guess, which was clearly wrong, I have only

served

> to confuse matters. My goal here is to get advice on how to

formulate

> the correct lmer model. Hopefully someone can help with that.

>

> I should describe my data in more detail. I have the following

columns:

>

> Location Habitat Week CO

> 1 Control 1 10

> 2 Control 1 12

> 3 Control 1 0

> 4 Control 1 5

> 5 Treatment 1 10

> 6 Treatment 1 7

> 7 Treatment 1 8

> 8 Treatment 1 6

> 9 Treatment 1 0

> 10 Treatment 1 5

> 11 Treatment 1 3

> 12 Treatment 1 12

> 13 Treatment 1 0

> ... (9 weeks of data omitted to save space)

> 1 Control 11 9

> 2 Control 11 8

> 3 Control 11 3

> 4 Control 11 6

> 5 Treatment 11 9

> 6 Treatment 11 6

> 7 Treatment 11 5

> 8 Treatment 11 10

> 9 Treatment 11 2

> 10 Treatment 11 4

> 11 Treatment 11 6

> 12 Treatment 11 9

> 13 Treatment 11 2

>

> From this, you will see that I have 4 control sites and 7 treatment

> sites that are measured each week. All 13 locations have different

> names, and Location is a random varaible. Is Location nested within

> Habitat? I thought it was, but maybe I am wrong. Perhaps it is a

> random variable that is not nested?

>

> My main goal is to look for an effect of Habitat. But if there is a

> significant Week x Habitat interaction, I would examine the effect of

> Habitat separately for each Week.

>

> Hopefully, the above helps to clarify my situation. I should

re-state,

> I would like to use an lmer or lme syntax to properly analyze these

> data, especially given that they are counts, I would like to try

family

> = poisson or quasipoisson.

>

> Thanks again,

>

> Dave

[Copy of previous posts snipped off. See the previous part history of

this thread in:

https://stat.ethz.ch/pipermail/r-help/2011-September/291178.html ]

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