[R-sig-ME] Logistic and nonlinear mixed models: Accounting for guessing probability

[Ken Knoblauch]

Douglas Bates <bates at ...> writes:

> 
> On Thu, Sep 30, 2010 at 3:29 PM, Douglas Bates <bates at ...> 
wrote:
> > Unfortunately an nlmer model is not appropriate for a binary 
response,
> > because it doesn't appropriately weight the residuals.
> >
> > Incorporating a non-zero guessing parameter requires a 
generalized
> > nonlinear mixed model if you need to estimate the guessing 
parameter.
> > The long term plan is to allow such a model.  This is the 
reason that
> > Martin and I worked on factoring out the internal code 
dealing with
> > different kinds of models for the expected response.  
Nonlinear 
models
> > affect these in one way and generalized linear models 
in another 
so
> > you need to chain these effects.
> >
> > For the particular case that Robert is considering, in 
which the
> > guessing parameter is fixed at 0.33 I think it may be 
possible to use
> > the mafc.logit link from the psyphy package with lme4a, the
> > development version of lme4.  I am currently installing the 
necessary
> > packages to see if I can make it work.  My thanks to Robert 
for making
> > the data available so we can test it.
> 
> It wasn't as easy as I had hoped it would be.  I'm getting 
an error
> when evaluating the linkfun (and, presumably, will get 
such an error
> for all the other functions in the family).  It probably has 
to do
> with the environment in which the function is evaluated 
in that it
> can't see the value of 'm'.
> 
> I'm not sure if I will be able to fix it in a reasonable 
amount of
> time (I should be grading assignments from one of 
my classes right
> now).

First, let me say thanks for putting some time into this issue,
 though I can hear you; it's time that you don't have.

If either you are Martin can suggest some way that I could modify
the mafc functions in psyphy to make m visible, I would be
happy to change them, provided they still work with glm, or I would
just define special ones for mixed effects models, but that wouldn't
be as efficient.  For example, I would think that defining functions with
fixed m for 2, 3, and 4 (and possibly 8) would cover about 99% of 
the cases in my field, but I can't speak for others.  In fact, would the 
following work for the case m = 3 in your modified code,

mafc.logit3 <- function () 
{

    linkfun <- function(mu) {
        mu <- pmax(mu, 1/3 + .Machine$double.eps)
        qlogis((3 * mu - 1)/2)
    }
    linkinv <- function(eta) {
        1/3 + (2/3) * .Call("logit_linkinv", eta, PACKAGE = "stats")
    }
    mu.eta <- function(eta) (2/3) * .Call("logit_mu_eta", 
        eta, PACKAGE = "stats")
    valideta <- function(eta) TRUE
    link <- paste("mafc.logit()", sep = "")
    structure(list(linkfun = linkfun, linkinv = linkinv, mu.eta = mu.eta, 
        valideta = valideta, name = link), class = "link-glm")
}



Finally, there is also the ecc2 data set in the psyphy package, if you 
need another data set for testing.  It is from a 4-alternative experiment
in which an obtserver had to choose between 1 of 4 positions at which a 
low contrast letter appeared and then identify it as 1 of 4 possible letters. 
It gives the aggrergated proportion correct, not the individual binary 
responses, but I could dig them out (I still have all that data), if binary 
responses were preferred.

Good luck.

best,

Ken

-- 
Ken Knoblauch
Inserm U846
Stem-cell and Brain Research Institute
Department of Integrative Neurosciences
18 avenue du Doyen Lépine
69500 Bron
France
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http://www.sbri.fr/members/kenneth-knoblauch.html