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List: r-sig-mixed-models
Subject: Re: [R-sig-ME] Specification of binomial mixed model with custom intercept
From: Dale Barr <dale.barr () glasgow ! ac ! uk>
Date: 2015-06-24 16:31:16
Message-ID: 558ADB54.3020304 () glasgow ! ac ! uk
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[ ...and of course also including the offset(baseline) term in the
formula... ]
On 24/06/15 17:27, Dale Barr wrote:
> Hi Tom,
>
> You might want to try the original syntax again, but without estimation
> of the (known) intercept term, using the "-1" syntax:
>
> fit=glmer(cbind(infected,not_infected) ~ -1 + (1|colony) + treatment *
> time, family=binomial, data=data)
>
> -Dale
>
> On 23/06/15 22:23, Tom Wenseleers wrote:
> > Hi Jake,
> > Many thanks for your advice! And yes realised the model would never quite get to \
> > 0% or 100% - that's why I had been trying putting in an intercept, as in \
> > data$baseline=qlogis(c(0.001,0.999))[data$treatment]
> > But just tried adding points for t=0 and that does indeed seem to give sensible \
> > results - so I'll go with that then - thanks for the advice!
> > If anyone else on this list would know how to formally put in constraints like \
> > the ones I mentioned, please let me know though!
> > cheers,
> > Tom
> >
> > ________________________________
> > From: Jake Westfall [jake987722@hotmail.com]
> > Sent: 23 June 2015 23:01
> > To: Tom Wenseleers
> > Subject: RE: [R-sig-ME] Specification of binomial mixed model with custom \
> > intercept
> > I see. This does seem more sensible. One complication I should point out is that \
> > you will never get your model to predict exactly 100% or 0%, as these correspond \
> > to logits of infinity or -infinity, respectively. You could set them to something \
> > high like logit = +/- 10 (corresonding to p = .99995 or .00005), but the exact \
> > values you fix them to are arbitrary and will affect the other model estimates. \
> > So it's tricky. One sort of klugey solution could be to put the time=0 \
> > measurements in the dataset "as if" you had recorded them -- with the \
> > justification being that you are virtually certain what the measurements would \
> > have been had you technically taken them at time=0 -- and then run the \
> > unconstrained model. This would basically just be a not-completely-arbitrary way \
> > of deciding what non-infinite values to fix the time=0 predictions to.
> >
> > Jake
> >
> > > From: Tom.Wenseleers@bio.kuleuven.be
> > > To: jake987722@hotmail.com; r-sig-mixed-models@r-project.org
> > > Subject: RE: [R-sig-ME] Specification of binomial mixed model with custom \
> > > intercept
> > > Date: Tue, 23 Jun 2015 19:35:20 +0000
> > >
> > > Hi Jake,
> > > Well to clarify a bit - I have actual datapoints for time=4, 8, 12 and 16, but \
> > > not for t=0 days. For t=0, however, I know that based on my treatments \
> > > (injecting individuals with virus lysate or with buffer) the proportion of \
> > > infected individuals was ca 0% for the CONTROL treatment and 100% for the \
> > > INJECTED group. Problem is that if this a priori constraint is not taken into \
> > > account and I fit my model and make an effect plot, the prediction is not \
> > > exactly 0% for the CONTROL group or 100% for the INJECTED group, even though I \
> > > know that it should. So my question is whether constraints such as these can be \
> > > taken into account into either binomial GLMs or binomial mixed models, e.g. by \
> > > specifying custom offsets/intercepts? (I also have other similar models where I \
> > > would like to be able to specify that at time=0 the initial proportion is known \
> > > a priori to be 0.5)
> > > In general my aim of specifying constraints such as these would be to obtain \
> > > better fits that better/more parsimoniously reflect known facts about the \
> > > actual experiments.
> > > cheers,
> > > Tom
> > >
> > > ________________________________________
> > > From: R-sig-mixed-models [r-sig-mixed-models-bounces@r-project.org] on behalf \
> > > of Jake Westfall [jake987722@hotmail.com]
> > > Sent: 23 June 2015 17:30
> > > To: r-sig-mixed-models@r-project.org
> > > Subject: Re: [R-sig-ME] Specification of binomial mixed model with custom \
> > > intercept
> > > Hi Tom,
> > >
> > > I'm not sure if this is a sensible thing to do. If your presumption about the \
> > > proportion of infected insects in each group at time=0 is correct, then surely \
> > > your data must already reflect this fact? In which case I don't see why you \
> > > can't just estimate the unconstrained model that you wrote and let the model \
> > > figure out for itself what p(infected) is at time=0. In short, I don't see the \
> > > added value of the constraints you mention.
> > > With that said, it occurs to me that if you really do want to implement the two \
> > > constraints that you mentioned, then really you are not estimating any \
> > > fixed-effect parameters at time=0. So it seems you could just as well exclude \
> > > the time=0 data and just model the treatment factor at time=1. >From those \
> > > parameter estimates it would be easier to figure out what the time slopes are \
> > > for each group, since they will just be the difference between the time=1 \
> > > parameter estimates and whatever values you fixed the proportions at time=0 to. \
> > > Hope this makes sense.
> > > Jake
> > >
> > > > From: Tom.Wenseleers@bio.kuleuven.be
> > > > To: r-sig-mixed-models@r-project.org
> > > > Date: Tue, 23 Jun 2015 15:02:47 +0000
> > > > Subject: [R-sig-ME] Specification of binomial mixed model with custom \
> > > > intercept
> > > > Dear all,
> > > > I have a binomial mixed model
> > > > fit=glmer(cbind(infected,not_infected)~(1|colony)+treatment*time,family=binomial,data=data)
> > > > in which I am modelling the evolution of an infection in different social \
> > > > insect colonies across two treatment groups (INJECTED and CONTROL) as a \
> > > > function of time. However, as my INJECTED group individuals should all be \
> > > > infected at time=0, whereas none of my CONTROL individuals should be infected \
> > > > at time=0, I would like to force the model to go approx through 1 at time t=0 \
> > > > for the INJECTED group and to go approx through 0 at time t=1 for the CONTROL \
> > > > group. What would be the correct way to specify such a model? I tried with
> > > > data$baseline=qlogis(c(0.001,0.999))[data$treatment]
> > > > fit=glmer(cbind(infected,not_infected)~(1|colony)+treatment*time+offset(baseline),family=binomial,data=data)
> > > > but this doesn't seem to give sensible predictions.
> > > > Any thoughts on the correct syntax?
> > > >
> > > > cheers,
> > > > Tom Wenseleers
> > > >
> > > >
> > > > _______________________________________________
> > > > R-sig-mixed-models@r-project.org mailing list
> > > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> > > [[alternative HTML version deleted]]
> > >
> > > _______________________________________________
> > > R-sig-mixed-models@r-project.org mailing list
> > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> > [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
--
Dale Barr
Institute of Neuroscience and Psychology
University of Glasgow
58 Hillhead Street
Glasgow G12 8QB
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