On May 27, 2015, at 2:49 PM, Kengo Inagaki wrote:
> Thank you very much for your rapid response. I sincerely appreciate your
> input.
> I am sorry for sending the previous email in HTML format.
>
> with(a, table(Sex, Therapy1) ) shows the following.
> Therapy1
> Sex no yes
> female 6 7
> male 7 5
>
> and with(a, table(Therapy1, Outcome) )
> elicit the following
>
> Outcome
> Sex Alive Death
> female 4 9
> male 9 3
>
> Outcome
> Therapy1 Alive Death
> no 4 9
> yes 9 3
Then what about:
with(a, table(Sex, Therapy1, Outcome) )
--
David
>
> As there is no zero cells, it does not seem to be complete separation.
> I really appreciate comments.
>
> Kengo Inagaki
> Memphis, TN
>
>
> 2015-05-27 13:57 GMT-05:00 David Winsemius <dwinsem...@comcast.net>:
>>
>> On May 27, 2015, at 10:10 AM, Kengo Inagaki wrote:
>>
>>> I am currently working on a health care related project using R. I am
>>> learning R while working on data analysis.
>>>
>>> Below is the part of the data in which i am encountering a problem.
>>>
>>>
>>> Case# Sex Therapy1 Therapy2 Outcome
>>>
>>> 1 male no
>>> no Alive
>>>
>>
>> snipped mangled data sent in HTML
>>
>>>
>>>
>>> "Outcome" is the response variable and "Sex", "Therapy1", "Therapy2" are
>>> predictor variables.
>>>
>>> All of the predictors are significantly associated with the outcome by
>>> univariate analysis.
>>>
>>> Logistic regression runs fine with most of the predictors when "Sex" and
>>> "Therapy1" are not included at the same time (This is a part of table that
>>> I cut out from a larger table for ease of
>>>
>>> presentation and there are more predictors that i tested).
>>
>> Please examine the data before reaching for ridge regression:
>>
>> What does this show: ...
>>
>> with(a, table(Sex, Therapy1) )
>>
>> I predict you will see a zero cell entry. The read about "complete
>> separation" and the so-called "Hauck-Donner effect".
>>
>> --
>> David.
>>>
>>> However, when "Sex" and "Therapy1" are included in logistic regression
>>> model at the same time, standard error inflates and p value gets close to 1.
>>>
>>> The formula used is,
>>>
>>>
>>>
>>>> Model<-glm(Outcome~Sex+Therapy1,data=a,family=binomial) #I assigned a
>>> vector "a" to represent above table.
>>>
>>>
>>>
>>> After doing some reading, I suspect this might be collinearity, as vif
>>> values (using "vif()" function in car package) were sky high (8,875,841 for
>>> both "Sex" and "Therapy1").
>>>
>>> Learning that ridge regression may be a solution, I attempted using
>>> logisticRidge {ridge} using the following formula, but i get the
>>> accomapnying error message.
>>>
>>>
>>>
>>>> logisticRidge(a$Outcome~a$Sex+a$Therapy1)
>>>
>>>
>>>
>>> Error in ifelse(y, log(p), log(1 - p)) :
>>>
>>> invalid to change the storage mode of a factor
>>>
>>>
>>>
>>> At this point I do not have an idea how to solve this and would like to
>>> seek help.
>>>
>>> I really really appreciate your input!!!
>>>
>>> [[alternative HTML version deleted]]
>>>
>>
>>
>> David Winsemius
>> Alameda, CA, USA
>>
David Winsemius
Alameda, CA, USA
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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.
