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List: r-sig-mixed-models
Subject: [R-sig-ME] Fw: Question about inclusion of a random effect
From: Chad Newbolt <newboch () auburn ! edu>
Date: 2017-08-08 18:57:11
Message-ID: 1502218632202.18544 () auburn ! edu
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So might help out to give more specifics...here is my model with explanations of \
effects and associated levels
results=glmer(Status2~Group+Distance+Type+Biologist+Sex+Experience+(1|ID)+(1|Question),data=datum,family=binomial)
Status2 = Binomial response where 0 is incorrect response and 1 is correct response
Distance (Outside, Inside) = Image contains an animal inside or outside a specified \
distance Type (Night, Day) = Image is taken during day or night
Group (Male, Female, Juvenille) = Image contains an animal that is male, female or \
juvenille Biologist (Yes, No) = Respondent is/is not a Biologist
Sex (Male Female) = Sex of respondent
Experience (High, moderate, low, none) = experience looking at images of species of \
animal in images
As you can see my fixed effects can be broken down into two broad categories 1) those \
that categorize the image, 2) those that categorize the respondent...both of which \
may influence their ability to correctly answer questions. I made sure during study \
planning that I have roughly equal numbers of each possible "category" of image \
represented in the survey. These were chosen at random from larger pools of each \
possible image category. In light of the previous response, since I have fixed \
effects that categorize the images, or questions, would it still make sense to \
include (1|Question) or create (1|Category) with n levels to account for variation \
not associated with my fixed effects?
For reference, I evaluated VIF of the fixed effects and found little evidence of \
multicollinearity, and I'm interested in the effects of each of these so I would \
prefer to keep them in the model in this case.
Chad Newbolt
Research Associate
School of Forestry And Wildlife Sciences
Auburn University
334-332-4864
________________________________________
From: Ewart A C Thomas <ethomas@stanford.edu>
Sent: Tuesday, August 8, 2017 1:19 PM
To: Chad Newbolt
Subject: Re: [R-sig-ME] Question about inclusion of a random effect
chad, lmer() and its optimisation is a computationally complex undertaking, and one \
shd always keep the ’size’ of the model in mind.
you have 94 items. might you reduce this to ‘categories’ of items (e.g., ‘faces’, \
‘people’, ‘houses’, …), such that you have a much smaller number (e.g., 10) of \
categories. you wd replace each respondent’s string of 94 responses by a string of \
10 categ-responses, and the random effect term wd be (1 | category). does this make \
theoretical sense, given the nature of your material?
also, including x1 thru x6 feels a little like a shopping expedition. maybe some \
exploratory anal (factor anal?) wd suggest either (i) using 2 or 3 composites based \
on x1-x6, or (ii) omitting about 3 of the x’s, because they don’t explain anything.
you didn’t raise this possibility, but it cd be that some questions/categories are \
more ‘sensitive’ to x1 than other questions. in this case, you might try to fit a \
model with (1 + x1 | category), and see if it fits sig better than the ‘intercept \
only’ model with (1 | category) - using anova(model1, model2). good luck! ewart
> On Aug 8, 2017, at 11:06 AM, Chad Newbolt <newboch@auburn.edu> wrote:
>
> When I include (1|Question) I receive the dreaded convergence warning...
>
> In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
> Model failed to converge with max|grad| = 0.00303355 (tol = 0.001, component 1)
>
> If I remove (1|Question) there is no convergence warning. Is this an indication \
> that the variance of this random effect is 0 thereby creating a problem with the \
> optimizer? Does this warrant removing this random effect? If not, any suggestions \
> on how to proceed with the convergence issues?
>
>
> Chad Newbolt
>
> Research Associate
>
> School of Forestry And Wildlife Sciences
>
> Auburn University
>
> 334-332-4864
>
> ________________________________________
> From: R-sig-mixed-models <r-sig-mixed-models-bounces@r-project.org> on behalf of \
> Chad Newbolt <newboch@auburn.edu>
> Sent: Tuesday, August 8, 2017 12:50 PM
> To: r-sig-mixed-models@r-project.org
> Subject: Re: [R-sig-ME] Question about inclusion of a random effect
>
> Thanks to everyone for the clarification and quick responses!!!
>
> ________________________________
> From: Alday, Phillip <Phillip.Alday@mpi.nl>
> Sent: Tuesday, August 8, 2017 12:43 PM
> To: Chad Newbolt; r-sig-mixed-models@r-project.org
> Subject: Re: [R-sig-ME] Question about inclusion of a random effect
>
>
> Yes, it makes sense. This is what is often called an "item" in the discussion on \
> crossed random effects and leaving it out can distort inferences - see Clark 1974 \
> "Language as a fixed effect fallacy" and more recent work by Westfall and Judd \
> (I'm thinking of their 2012 paper on this, but I can't think of the title or author \
> order and I'm not at my desk to look it up).
> Phillip
> ________________________________
> From: Chad Newbolt <newboch@auburn.edu>
> Sent: Aug 8, 2017 7:25 PM
> To: r-sig-mixed-models@r-project.org
> Subject: [R-sig-ME] Question about inclusion of a random effect
>
>
> All,
>
>
>
> I'm working on analyzing a data set from a survey. In the survey, I asked a group \
> of respondents to view a series of 94 images, or test questions, and I'm in process \
> of evaluating the influence of various factors on their ability to correctly \
> identify an item in an image. The test questions likely show a considerable amount \
> of variation in difficulty, with some being harder to correctly answer than others. \
> I understand that I clearly should include a random effect for each respondent \
> (ID), however, I'm not sure if it is appropriate to include a random effect for \
> question (1|Question) to account for variation. I may be overthinking this one, \
> but, including and removing (1|Question) dramatically changes my results so I want \
> to make sure to get this one right.
>
>
> My basic model is shown below for reference:
>
>
>
> results=glmer(Y~X1+X2+X3+X4+X5+X6+(1|ID)+(1|Question),data=datum,na.action = \
> na.omit,family=binomial)
>
>
> Thanks in advance for the help
>
> [[alternative HTML version deleted]]
>
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> R-sig-mixed-models@r-project.org mailing list
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>
> [[alternative HTML version deleted]]
>
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>
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