Hi Bert,
Thanks for your reply.
I AM making an assumption of MAR data, because
informative missingness (I assume you mean NMAR) is too hard to deal with
I have quite a few covariates (so the observed is likely to predict
the missing and mitigate against informative missingness)
the missingness is not supposed to be censoring
I doubt the missingness on the covariates (mostly environmental type
measures) is censoring with respect to the independent variables which
are genotypes
I don't like complete case logistic regression because
it is less robust
and throws away info
However I don't have time to do anything clever so I'm just going to go
along with the complete case logistic regression.
Thanks again.
regards
Desmond
Bert Gunter wrote:> Desmond:
>
> The problem with ML with missing data is both the M and the L. In MAR, the
L
> factors into a part involving the missingness parameters and the model
> parameters, and you can maximize the model parameters part without having
> to worry about missingness because they depend only on the observed data.
> (MCAR is even easier, since missingness doesn't change the likelihood).
>
> For informative missingness you have to come up with an L to maximize, and
> this is hard. There's also no way of checking the adequacy of the L
(since
> the data to check it are missing). And when you choose your L, the M may be
> hard to do numerically.
>
> As Emmanuel indicated, Bayes may help, but now I'm at he end of MY
> knowledge.
>
> Note that in many cases, "missing" is actually not missing --
it's
> censoring. And for that, likelihoods can be obtained (and maximized).
>
> Cheers,
>
> Bert Gunter
> Genentech Nonclinical Biostatistics
>
>
>
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at
r-project.org] On
> Behalf Of Desmond D Campbell
> Sent: Monday, April 05, 2010 3:19 PM
> To: Emmanuel Charpentier
> Cc: r-help at r-project.org; Desmond Campbell
> Subject: Re: [R] logistic regression in an incomplete dataset
>
> Dear Emmanuel,
>
> Thank you.
>
> Yes I broadly agree with what you say.
> I think ML is a better strategy than complete case, because I think its
> estimates will be more robust than complete case.
> For unbiased estimates I think
> ML requires the data is MAR,
> complete case requires the data is MCAR
>
> Anyway I would have thought ML could be done without resorting to Multiple
> Imputation, but I'm at the edge of my knowledge here.
>
> Thanks once again,
>
> regards
> Desmond
>
>
> From: Emmanuel Charpentier <charpent <at> bacbuc.dyndns.org>
> Subject: Re: logistic regression in an incomplete dataset
> Newsgroups: gmane.comp.lang.r.general
> Date: 2010-04-05 19:58:20 GMT (2 hours and 10 minutes ago)
>
> Dear Desmond,
>
> a somewhat analogous question has been posed recently (about 2 weeks
> ago) on the sig-mixed-model list, and I tried (in two posts) to give
> some elements of information (and some bibliographic pointers). To
> summarize tersely :
>
> - a model of "information missingness" (i. e. *why* are some data
> missing ?) is necessary to choose the right measures to take. Two
> special cases (Missing At Random and Missing Completely At Random) allow
> for (semi-)automated compensation. See literature for further details.
>
> - complete-case analysis may give seriously weakened and *biased*
> results. Pairwise-complete-case analysis is usually *worse*.
>
> - simple imputation leads to underestimated variances and might also
> give biased results.
>
> - multiple imputation is currently thought of a good way to alleviate
> missing data if you have a missingness model (or can honestly bet on
> MCAR or MAR), and if you properly combine the results of your
> imputations.
>
> - A few missing data packages exist in R to handle this case. My ersonal
> selection at this point would be mice, mi, Amelia, and possibly mitools,
> but none of them is fully satisfying(n particular, accounting for a
> random effect needs special handling all the way in all packages...).
>
> - An interesting alternative is to write a full probability model (in
> BUGS fo example) and use Bayesian estimation ; in this framework,
> missing data are "naturally" modeled in the model used for
analysis.
> However, this might entail *large* work, be difficult and not always
> succeed (numerical difficulties. Furthermore, the results of a Byesian
> analysis might not be what you seek...
>
> HTH,
>
> Emmanuel Charpentier
>
> Le lundi 05 avril 2010 ? 11:34 +0100, Desmond Campbell a ?crit :
>
>> Dear all,
>>
>> I want to do a logistic regression.
>> So far I've only found out how to do that in R, in a dataset of
complete
>>
> cases.
>
>> I'd like to do logistic regression via max likelihood, using all
the
>>
> study cases (complete and
> incomplete). Can you help?
>
>> I'm using glm() with family=binomial(logit).
>> If any covariate in a study case is missing then the study case is
>>
> dropped, i.e. it is doing a complete cases analysis.
>
>> As a lot of study cases are being dropped, I'd rather it did
maximum
>>
> likelihood using all the study cases.
>
>> I tried setting glm()'s na.action to NULL, but then it complained
about
>>
> NA's present in the study cases.
>
>> I've about 1000 unmatched study cases and less than 10 covariates
so
>>
> could use unconditional ML
> estimation (as opposed to conditional ML estimation).
>
>> regards
>> Desmond
>>
>>
>> --
>> Desmond Campbell
>> UCL Genetics Institute
>> D.Campbell at ucl.ac.uk
>> Tel. ext. 020 31084006, int. 54006
>>
>>
>>
>
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>
>
--
Desmond Campbell
UCL Genetics Institute
D.Campbell at ucl.ac.uk
Tel. ext. 020 31084006, int. 54006