[R] modelling and R misconceptions; was: package installtion

[Uwe Ligges]

This is hopeless, since you never seem to listen to our advice, 
therefore this will be my very last try:

So you actually need local advice, both for statistical concepts and R 
related. No statistics software can estimate effects of variables that 
you observed to be constant (e.g. 0) all the time. If any software does, 
please delete it a once from your machine.
Instead, ask a local statistician for advice on your problem. You 
certainly want to show the data and your model to the local expert - 
since you don't show us. And then you want to ask for local R course 
since reading the documentation seems not to help. Applying mtrace() in 
a non exiting object shows this straight away.

Uwe Ligges






On 17.11.2011 15:49, Scott Raynaud wrote:
> I believe the problem is a column of zeroes in my x matrix.  I have tried the suggestions in the documentation,
> so now to try to confirm the probelm I'd like to run debug.  Here's where I think the problem is:
>
> ###~~~~~~~~~~      Fitting the model using lmer funtion    ~~~~~~~~~~###
> (fitmodel<- lmer(modelformula,data,family=binomial(link=logit),nAGQ=1))
> mtrace(fitmodel)
>
> I added the mtrace to catch the error, but get the following:
>
> Error in mtrace(fitmodel) : Can't find fitmodel
>
> How can I debug this?
>
>
> ----- Original Message -----
> From: Rolf Turner<rolf.turner at xtra.co.nz>
> To: Scott Raynaud<scott.raynaud at yahoo.com>
> Cc: "r-help at r-project.org"<r-help at r-project.org>
> Sent: Wednesday, November 16, 2011 6:04 PM
> Subject: Re: [R] package installtion
>
> On 17/11/11 05:37, Scott Raynaud wrote:
>> That might be an option if it weren't my most important predictor.  I'm thinking my best bet is to use MLWin for the estimation since it will properly set fixed effects
>>    to 0.  All my other sample size simulation programs use SAS PROC IML which I don't have/can't afford.  I like R since it's free, but I can't work around the problem
>> I'm currently having.
>
> This is the ``push every possible button until you get a result and to hell with what
> anything actually means'' approach to statistics.  The probability of getting a
> *meaningful* result from this approach is close to zero.
>
> Why don't you try to *understand* what is going on, rather than wildly throwing
> every possible piece of software at the problem until one such piece runs?
>
>      cheers,
>
>          Rolf Turner
>
>
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