

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
[hidden email]
Tel. ext. 020 31084006, int. 54006
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide http://www.Rproject.org/postingguide.htmland provide commented, minimal, selfcontained, reproducible code.


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
[hidden email]
Tel. ext. 020 31084006, int. 54006
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide http://www.Rproject.org/postingguide.htmland provide commented, minimal, selfcontained, reproducible code.


Hello Desmond,
The only way to not drop cases with incomplete data would be some sort
of imputation for the missing covariates.
JoAnn
Desmond Campbell wrote:
> 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
>
>
>

JoAnn Álvarez
Biostatistician
Department of Biostatistics
D2220 Medical Center North
Vanderbilt University School of Medicine
1161 21st Ave. South
Nashville, TN 372322158
http://biostat.mc.vanderbilt.edu/JoAnnAlvarez______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide http://www.Rproject.org/postingguide.htmland provide commented, minimal, selfcontained, reproducible code.


Dear Desmond,
a somewhat analogous question has been posed recently (about 2 weeks
ago) on the sigmixedmodel 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.
 completecase analysis may give seriously weakened and *biased*
results. Pairwisecompletecase 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
>
>
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide http://www.Rproject.org/postingguide.htmland provide commented, minimal, selfcontained, reproducible code.


Dear JoAnn,
Thank you very much for your reply.
If that is the case I am surprised.
I would have though ML could incorporate study cases with some missingness
in them.
Furthermore I believe ML estimates should generally be more robust than
complete case based estimates.
For unbiased estimates I think
ML requires the data is MAR,
complete case requires the data is MCAR
Maybe it is more difficult to make the ML estimate on incomplete data than
I imagine. My knowledge is patchy.
Thanks again.
regards
Desmond
> Hello Desmond,
>
> The only way to not drop cases with incomplete data would be some sort
> of imputation for the missing covariates.
>
> JoAnn
>
> Desmond Campbell wrote:
>> 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
>>
>>
>>
>
>
> 
> JoAnn Álvarez
> Biostatistician
> Department of Biostatistics
> D2220 Medical Center North
> Vanderbilt University School of Medicine
> 1161 21st Ave. South
> Nashville, TN 372322158
>
> http://biostat.mc.vanderbilt.edu/JoAnnAlvarez>
>
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide http://www.Rproject.org/postingguide.htmland provide commented, minimal, selfcontained, reproducible code.


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: 20100405 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 sigmixedmodel 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.
 completecase analysis may give seriously weakened and *biased*
results. Pairwisecompletecase 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
> [hidden email]
> Tel. ext. 020 31084006, int. 54006
>
>
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide http://www.Rproject.org/postingguide.htmland provide commented, minimal, selfcontained, reproducible code.


On Mon, 5 Apr 2010, Desmond D Campbell wrote:
> 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.
This is an illustration of why Rubin's hierarchy, while useful, doesn't displace actual thinking about the problem.
The maximumlikelihood problem for which the MAR assumption is sufficient involves specifying the joint likelihood for the outcome and all predictor variables, which is basically the same problem as multiple imputation. Multiple imputation averages the estimate over the distribution of the unknown values; maximum likelihood integrates out the unknown values, but for reasonably large sample sizes the estimates will be equivalent (by asymptotic linearity of the estimator). Standard error calculation is probably easier with multiple imputation.
Also, it is certainly not true that a completecase regression analysis requires MCAR. For example, if the missingness is independent of Y given X, the completecase distribution will have the same mean of Y given X as the population and so will have the same bestfitting regression. This is a stronger assumption than you need for multiple imputation, but not a lot stronger.
thomas
> 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: 20100405 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 sigmixedmodel 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.
>
>  completecase analysis may give seriously weakened and *biased*
> results. Pairwisecompletecase 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
>> [hidden email]
>> Tel. ext. 020 31084006, int. 54006
>>
>>
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rhelp> PLEASE do read the posting guide http://www.Rproject.org/postingguide.html> and provide commented, minimal, selfcontained, reproducible code.
>
Thomas Lumley Assoc. Professor, Biostatistics
[hidden email] University of Washington, Seattle
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide http://www.Rproject.org/postingguide.htmland provide commented, minimal, selfcontained, reproducible code.


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: [hidden email] [mailto: [hidden email]] On
> Behalf Of Desmond D Campbell
> Sent: Monday, April 05, 2010 3:19 PM
> To: Emmanuel Charpentier
> Cc: [hidden email]; 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: 20100405 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 sigmixedmodel 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.
>
>  completecase analysis may give seriously weakened and *biased*
> results. Pairwisecompletecase 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
>> [hidden email]
>> Tel. ext. 020 31084006, int. 54006
>>
>>
>>
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rhelp> PLEASE do read the posting guide http://www.Rproject.org/postingguide.html> and provide commented, minimal, selfcontained, reproducible code.
>
>

Desmond Campbell
UCL Genetics Institute
[hidden email]
Tel. ext. 020 31084006, int. 54006
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide http://www.Rproject.org/postingguide.htmland provide commented, minimal, selfcontained, reproducible code.


Dear Thomas,
Thanks for your reply.
Yes you are quite right (your example) complete case does not require
MCAR, however as well as being a bit less robust than ML it is throwing
away data.
Missing Data in Clinical Studies, Geert Molenberghs, Michael Kenward,
have a nice section in chapter 3 or 4 where they rubbish Complete Case
and Last Case Carried Forward.
Ah well, I don't have time to do anything clever so I'm just going to go
along with the complete case logistic regression.
regards
Desmond
Thomas Lumley wrote:
> On Mon, 5 Apr 2010, Desmond D Campbell wrote:
>
>> 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.
>
> This is an illustration of why Rubin's hierarchy, while useful,
> doesn't displace actual thinking about the problem.
>
> The maximumlikelihood problem for which the MAR assumption is
> sufficient involves specifying the joint likelihood for the outcome
> and all predictor variables, which is basically the same problem as
> multiple imputation. Multiple imputation averages the estimate over
> the distribution of the unknown values; maximum likelihood integrates
> out the unknown values, but for reasonably large sample sizes the
> estimates will be equivalent (by asymptotic linearity of the
> estimator). Standard error calculation is probably easier with
> multiple imputation.
>
>
> Also, it is certainly not true that a completecase regression
> analysis requires MCAR. For example, if the missingness is
> independent of Y given X, the completecase distribution will have the
> same mean of Y given X as the population and so will have the same
> bestfitting regression. This is a stronger assumption than you need
> for multiple imputation, but not a lot stronger.
>
> thomas
>
>
>> 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: 20100405 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 sigmixedmodel 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.
>>
>>  completecase analysis may give seriously weakened and *biased*
>> results. Pairwisecompletecase 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
>>> [hidden email]
>>> Tel. ext. 020 31084006, int. 54006
>>>
>>>
>>
>> ______________________________________________
>> [hidden email] mailing list
>> https://stat.ethz.ch/mailman/listinfo/rhelp>> PLEASE do read the posting guide
>> http://www.Rproject.org/postingguide.html>> and provide commented, minimal, selfcontained, reproducible code.
>>
>
> Thomas Lumley Assoc. Professor, Biostatistics
> [hidden email] University of Washington, Seattle

Desmond Campbell
UCL Genetics Institute
[hidden email]
Tel. ext. 020 31084006, int. 54006
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https://stat.ethz.ch/mailman/listinfo/rhelpPLEASE do read the posting guide http://www.Rproject.org/postingguide.htmland provide commented, minimal, selfcontained, reproducible code.

