logistic.display need a rather tidy model ie all independent variables must
be the original name not any function of a variable in the dataset of the
model.

If data1 is your dataset containing y, 'gender' and 'age', what you need to
do is

> data1$age1 <- factor(data1$age)
> result1<-glm(y ~ age1 + gender + CD4,family = binomial, data=data1)
> logistic.display(result1)

Note that 'gender', like 'y', is already dichotomized (0,1). There is no
need to 'factor'

Virasakdi C


2011/12/14 ©sªY <lm_meng...@163.com>

>
> Hi sir:
> I follow your suggestion:
>
> result<-glm(y ~ factor(age) + factor(gender) + CD4,family = binomial)
> logistic.display(result)
>
> Error in coeff[, 1] : incorrect number of dimensions
>
>
>
>
>
>
> At 2011-12-14 01:59:36,"Jorge I Velez" <jorgeivanve...@gmail.com> wrote:
>
> Hi there,
>
> Try
>
> require(epicalc)
> logistic.display(result)
>
> HTH,
> Jorge
>
> On Tue, Dec 13, 2011 at 7:16 AM, ©sªY <> wrote:
>
>> Hi all:
>> My data has 3 variables:
>> age(3levels : <30y=1  30-50y=2, >50y=3)
>> gender(Male=0, Female=1)
>> CD4 cell count(raw lab measurement)
>> y(1:death  0:alive)
>>
>> I perform logistic regression to find out the factors that influence y.
>>
>> result<-glm(y ~ factor(age) + factor(gender) + CD4,family = binomial)
>>
>> >From the result,I can get OR(Odds Ratio) of gender  via exp(Estimate of
>> Female, since Male is regarded as reference group and has no result).But
>> how can I compute the 95%CI of OR of gender?
>>
>>
>> Thanks a lot for your help.
>>
>> My best!
>>
>>
>>
>>
>>        [[alternative HTML version deleted]]
>>
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>>
>
>
>
>
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