[R-sig-ME] R-sig-mixed-models Digest, Vol 26, Issue 12
Iasonas Lamprianou
lamprianou at yahoo.com
Sun Feb 8 20:24:59 CET 2009
Dear Douglas Bates
we all support you and you should not allow anyone make you feel upset. You are contributing/offering a lot to all of us and we are greateful. Keep on good work and try to ignore other nuisances. lme4 is the reason why a number of people considered R at the first place
jason
Dr. Iasonas Lamprianou
Department of Education
The University of Manchester
Oxford Road, Manchester M13 9PL, UK
Tel. 0044 161 275 3485
iasonas.lamprianou at manchester.ac.uk
--- On Sun, 8/2/09, r-sig-mixed-models-request at r-project.org <r-sig-mixed-models-request at r-project.org> wrote:
> From: r-sig-mixed-models-request at r-project.org <r-sig-mixed-models-request at r-project.org>
> Subject: R-sig-mixed-models Digest, Vol 26, Issue 12
> To: r-sig-mixed-models at r-project.org
> Date: Sunday, 8 February, 2009, 11:00 AM
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> Today's Topics:
>
> 1. Lack of replies from me (Douglas Bates)
> 2. Re: Lack of replies from me (Andrew Robinson)
> 3. logistic growth, vexing choice of which timepoints
> (Nicholas Lewin-Koh)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Sat, 7 Feb 2009 08:33:59 -0600
> From: Douglas Bates <bates at stat.wisc.edu>
> Subject: [R-sig-ME] Lack of replies from me
> To: R Models Mixed <r-sig-mixed-models at r-project.org>
> Message-ID:
> <40e66e0b0902070633s428cbf0ekd3e37f1e82ccf2be at mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
>
> I regret that I have been absent from the list for some
> time. It
> happens that in the last couple of weeks I have been
> involved in a
> series of extremely unpleasant interactions on another,
> private email
> list that have left me with little enthusiasm for
> developing and
> supporting CRAN packages. One conclusion I have reached
> from these
> interactions is that I will never allow lme4 to be a
> recommended
> package in R. I am even having doubts about whether I want
> it to
> continue to be a CRAN package at all, as opposed to, say,
> moving it to
> Bioconductor or even switching development to another
> language.
>
> I'm sure the last option would be "cutting off my
> nose to spite my
> face" and I don't expect I would ever do that.
> There are many
> wonderful aspects to R and many reasons why I want to
> continue to use
> it. But right now I find myself forced to evaluate options
> other than
> putting a package on CRAN.
>
>
>
> ------------------------------
>
> Message: 2
> Date: Sun, 8 Feb 2009 07:13:06 +1100
> From: Andrew Robinson <A.Robinson at ms.unimelb.edu.au>
> Subject: Re: [R-sig-ME] Lack of replies from me
> To: Douglas Bates <bates at stat.wisc.edu>
> Cc: R Models Mixed <r-sig-mixed-models at r-project.org>
> Message-ID:
> <20090207201306.GP51827 at ms.unimelb.edu.au>
> Content-Type: text/plain; charset=us-ascii
>
> On Sat, Feb 07, 2009 at 08:33:59AM -0600, Douglas Bates
> wrote:
> > I regret that I have been absent from the list for
> some time. It
> > happens that in the last couple of weeks I have been
> involved in a
> > series of extremely unpleasant interactions on
> another, private email
> > list that have left me with little enthusiasm for
> developing and
> > supporting CRAN packages. One conclusion I have
> reached from these
> > interactions is that I will never allow lme4 to be a
> recommended
> > package in R. I am even having doubts about whether I
> want it to
> > continue to be a CRAN package at all, as opposed to,
> say, moving it to
> > Bioconductor or even switching development to another
> language.
> >
> > I'm sure the last option would be "cutting
> off my nose to spite my
> > face" and I don't expect I would ever do
> that. There are many
> > wonderful aspects to R and many reasons why I want to
> continue to use
> > it. But right now I find myself forced to evaluate
> options other than
> > putting a package on CRAN.
>
> I'm very sorry to hear about your frustrations, Doug.
> I'm sure that I
> speak for all of us when I say that we'll continue to
> use and support
> lme4 regardless of the delivery mechanism.
>
> Warm wishes
>
> Andrew
> --
> Andrew Robinson
> Department of Mathematics and Statistics Tel:
> +61-3-8344-6410
> University of Melbourne, VIC 3010 Australia
> (prefer email)
> http://www.ms.unimelb.edu.au/~andrewpr Fax:
> +61-3-8344-4599
> http://blogs.mbs.edu/fishing-in-the-bay/
>
>
>
> ------------------------------
>
> Message: 3
> Date: Sat, 07 Feb 2009 14:16:56 -0800
> From: "Nicholas Lewin-Koh"
> <nikko at hailmail.net>
> Subject: [R-sig-ME] logistic growth, vexing choice of which
> timepoints
> To: r-sig-mixed-models at r-project.org
> Cc: Jenny Bryan <jenny at stat.ubc.ca>
> Message-ID:
> <1234045016.6848.1299107865 at webmail.messagingengine.com>
> Content-Type: text/plain; charset="ISO-8859-1"
>
> Hi Jenny
> What Steven said below is true, the zeros are below the
> detection limit.
> However,
> one might ask if the time until the populations cross the
> detection
> threshold matters?
> For instance if two wells treated differently both have
> similar logistic
> curves, but one
> starts accelerating at t(i) and the other at t(j), j > i
> that does
> provide some information about
> what is going on below the detection limit. A sophisticated
> approach
> might be to fit a joint model
> modeling the time to the event, and the logistic growth
> simultaneously.
> Given that that is
> hard, and there may not be any software to do it, you might
> want to fit
> the survival model (time to event)
> and then the logistic growth model n the non-zero data.
> This is very
> add-hoc, but will at least give you
> some idea of whether it is worth chasing a more complicated
> model. This
> will be more effective if
> you have replicate wells.
>
> Nicholas
>
> > Message: 1
> > Date: Fri, 6 Feb 2009 13:54:47 -0800
> > From: Jenny Bryan <jenny at stat.ubc.ca>
> > Subject: [R-sig-ME] logistic growth, vexing choice of
> which timepoints
> > to include
> > To: r-sig-mixed-models at r-project.org
> > Message-ID:
> <4B7E17CD-C967-479F-85CD-97404D975969 at stat.ubc.ca>
> > Content-Type: text/plain; charset=US-ASCII;
> format=flowed; delsp=yes
> >
> > Hello. I'm looking for advice on how to make a
> seemingly unavoidable
> > subjective choice in an analysis I'm doing, using
> the logistic growth
> > model. I'm using nlme, but that has nothing to do
> with my question,
> > so I hope it's not too inappropriate for me to
> post this here.
> > Reading the list archive suggests that it's not
> too hard to tempt this
> > group into philosophical discussions :-)
> >
> > I have growth data that can be reasonably modelled
> with a four-
> > parameter logistic curve. The experimental unit is a
> well in a
> > microtitre plate and I get light absorbance readings
> over time that
> > reflect cell density. There are many wells on a
> plate, e.g. 96 or
> > 384, and experiments often span many plates.
> Systematic differences
> > between the wells can be, for example, specific
> genetic mutations
> > carried by the cells and/or different chemicals added
> to the growth
> > medium. I am mostly interested in performing
> inference on the fixed
> > effects, i.e. how the genetic perturbations, the
> chemicals, or their
> > interactions, modify key growth parameters, especially
> the one
> > inversely related to the underlying exponential growth
> rate we'd see
> > in the absence of resource constraints (phi_4 in
> Pinheiro & Bates p.
> > 517).
> >
> > Problem: The number of cells inoculated into the
> wells at the start
> > is quite small -- well below the detection threshhold
> for the light
> > absorbance readings. Therefore, each timecourse
> begins with a loooong
> > string of zeros, before the classic sigmoidal shape
> kicks in. And, of
> > course, the timing of this happy event is both
> ill-defined and very
> > variable across the wells.
> >
> > For figure-making purposes, I removed some early
> timepoints that were
> > uniformly zero for all wells. Which made me wonder:
> why couldn't
> > (shouldn't?) I do the same prior to model fitting?
> When I fit the
> > logistic growth model with and without these early
> timepoints, I get
> > essentially the same estimated fixed effects and,
> even, estimated
> > variances for the random effects. But there *is* a
> substantial
> > difference in the estimate of residual variance, which
> then obviously
> > has a noticeable effect on the inference for the fixed
> effects and,
> > especially, the one I care about. Including all the
> timepoints drives
> > the residual variance down, as you might expect. But
> that almost
> > seems misleading or artificial ... other collaborators
> I work with
> > don't even start taking OD readings until the
> first 12 hours have
> > passed, which makes their initial strings of zeros
> quite short,
> > which ... gives them less statistical significance for
> the same
> > observed effect size?!?
> >
> > Does anyone have a comment or advice?
> >
> > Thanks in advance for reading this,
> > Jenny
> >
> > Jennifer Bryan
> > Department of Statistics and
> > the Michael Smith Laboratories
> > University of British Columbia
> >
> >
> >
> > ------------------------------
> >
> > Message: 2
> > Date: Fri, 6 Feb 2009 15:18:53 -0800
> > From: Steven McKinney <smckinney at bccrc.ca>
> > Subject: Re: [R-sig-ME] logistic growth, vexing choice
> of which
> > timepoints to include
> > To: "Jenny Bryan" <jenny at stat.ubc.ca>,
> > <r-sig-mixed-models at r-project.org>
> > Message-ID:
> >
> <0BE438149FF2254DB4199E2682C8DFEB0328A5AC at crcmail1.BCCRC.CA>
> > Content-Type: text/plain;
> charset="iso-8859-1"
> >
> > Hi Jenny,
> >
> > [Caveat: Comments from an applied statistician, not
> > a world-heavyweight likelihood theorist]
> >
> > In the logistic world a zero value maps to a
> > minus infinity value. It seems to me that only
> > the 'last' zero value contains any information
> > relevant to the likelihood (equivalently only
> > the 'first' one value (plus infinity in the
> > logistic realm) contains any information
> > relevant to the likelihood). Perhaps the
> > coding for the likelihood has not been set
> > up to take this into account so you are getting
> > the artificial contribution of the rest of the
> > zero values folded into the likelihood, artificially
> > deflating the variance estimates.
> >
> > I would exclude or set to NA all but the last
> > (or even all of) the zero values for any well
> > and all but the first (or even all of) the one
> > values.
> >
> > The zero values are really below the detection
> > limit of the sensor involved so should theoretically
> > be handled as truncated data but that's another
> > level of complexity for the analysis.
> >
> > Steven McKinney
> >
> > Statistician
> > Molecular Oncology and Breast Cancer Program
> > British Columbia Cancer Research Centre
> >
> > email: smckinney +at+ bccrc +dot+ ca
> >
> > tel: 604-675-8000 x7561
> >
> > BCCRC
> > Molecular Oncology
> > 675 West 10th Ave, Floor 4
> > Vancouver B.C.
> > V5Z 1L3
> > Canada
> >
> >
> >
> >
>
>
>
> ------------------------------
>
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