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-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Desmond D Campbell
Sent: Monday, April 05, 2010 3:19 PM
To: Emmanuel Charpentier
Cc: r-help@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 à 11:34 +0100, Desmond Campbell a écrit :
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.campb...@ucl.ac.uk
Tel. ext. 020 31084006, int. 54006
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
--
Desmond Campbell
UCL Genetics Institute
d.campb...@ucl.ac.uk
Tel. ext. 020 31084006, int. 54006
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
