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List:       r-sig-mixed-models
Subject:    [R-sig-ME] model advice
From:       "Guy,Travis J" <tguy () ufl ! edu>
Date:       2016-07-28 2:03:41
Message-ID: 1469671421430.15990 () ufl ! edu
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Hello!

I'm a master's student studying pollination networks. I have been furiously trying to \
learn about linear mixed models and glmms, but I have some specific questions \
relating to my project analysis that I am hoping someone can help me with


Here's the short of my project. I can provide more details if need be. I am looking \
at 16 pollination metrics (ex. specialization). A few of the metrics are count data \
(ex. floral abundance) and several are proportions (limited to be between 0 and 1). I \
am interested in how rainfall (high and low location) and wildlife exclusion \
(treatment) affect the pollination metrics. I have constructed 12 networks in total. \
6 networks in the low rainfall area having 3 networks with wildlife excluded and 3 \
networks allowing wildlife. Then there are 6 networks in the high rainfall area again \
have 3 networks with wildlife excluded and 3 with wildlife included. So sample size \
is obviously small. It's a block design with 3 blocks in the low rainfall and 3 \
blocks in the south location. Each block has the wildlife excluded treatment and the \
wildlife allowed treatment.

Here are my questions:

The majority of my metrics fit model assumptions (normality of residuals, variance \
within groups, normality within groups, normality of random effects, and \
linearity/absence of heteroskedasticity). However I have some where normality appear \
to be violated and the fitted vs residuals plot is no good. Various transformations \
(log, ln, sqrt,arcsin(sqrt)) don't seem to help.  >From reading papers by Dr. Ben \
Bolker, this is where it appears GLMMs come in.

So for the metrics that fit model assumptions my plan is to fit this model

    metric.model <- lmer(metric ~ Treatment + Location + (1 |Blocks), data = \
UHURUnets)?

but for those where model assumptions aren't met, I'm not sure how one picks which \
exponential family to use and which link to use. How does one go about deciding what \
family and link to use?

I read in Dr. Bolker's TREE paper that binomial distribution and logit link are best \
for proportions. Is this generally the case?

NODF.M1 <- glmer(weighted_NODF ~ Treatment + Location + (1|Blocks), data = UHURUnets, \
family = binomial(link = "logit")?

For the count data (ex. floral abundance, insect abundance), it seems like I should \
use Poisson and log link according to that same paper paper.

No.Fl.units.M1 <- glmer(number_of_floral_units ~ Treatment + Location + (1|Blocks), \
data = UHURUnets, family = poisson(link = "log")?

But what distribution and link would one use for continuous data that is not in \
proportions?

And once you have made a GLMM model, I am assuming it is okay that this model still \
does not fit the normality assumptions or the residual vs fitted plots. Is this true?

My models (both glmms and lmer) currently only have random intercepts. I have read \
that it might be wise to also have random slopes as well because the pollination \
metric could vary for each treatment and location depending on which block it is in.

So then I believe I would have a model like this
?
Vuln.LL.M3 <- glmer(vulnerability.LL ~ Treatment + Location + (1 + Treatment|Blocks) \
+ (1 + Location|Blocks), family = gaussian(link = log), data = UHURUnets)

I am not sure if this is correct. I get 2 warnings (failed to converge and unable to \
evaluated scaled gradient). Interestingly I appear to not get these warnings if I am \
running linear mixed models (lmer). Am I doing this correctly?

Lastly, is it appropriate to use interaction terms in GLMMs and lmers? I imagine that \
the rainfall level my interact with the treatment to influence the pollination \
metric.

 metric.model <- glmer(metric ~ Treatment*Location + (1 |Blocks), data = UHURUnets, \
family = gaussian(link = log)??)

Many thanks in advance for your help!

Cheers,
Travis




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