Hi lm_mengxin,

If that's the case, just use as.factor():

>  fit <- glm(case ~ as.factor(induced) + as.factor(spontaneous),
family=binomial, data=infert)
>  logistic.display(fit)

                               OR lower95ci upper95ci     Pr(>|Z|)
as.factor(induced)1      1.585398 0.7972313  3.152769 1.888869e-01
as.factor(induced)2      2.281856 0.9784062  5.321787 5.620567e-02
as.factor(spontaneous)1  3.630192 1.8855353  6.989152 1.145482e-04
as.factor(spontaneous)2 10.525317 4.4444045 24.926241 8.745128e-08

Also, take a look at ?factor and ?glm.

HTH,
Jorge.-


2011/12/13 孟欣 <>

> Yes,you are right.
>
> But if I wanna treat "induced" and "spontaneous" as factors, how can I
> get the corresponding OR?
>
>
>
>
>
> At 2011-12-14 12:54:30,"Jorge I Velez" <> wrote:
>
> I forgot to mention (sorry for double posting) that str(infert) shows
> that "induced" and "spontaneous" are not factors:
>
> 'data.frame': 248 obs. of  8 variables:
>  $ education     : Factor w/ 3 levels "0-5yrs","6-11yrs",..: 1 1 1 1 2 2 2
> 2 2 2 ...
>  $ age           : num  26 42 39 34 35 36 23 32 21 28 ...
>  $ parity        : num  6 1 6 4 3 4 1 2 1 2 ...
>  $ induced       : num  1 1 2 2 1 2 0 0 0 0 ...
>  $ case          : num  1 1 1 1 1 1 1 1 1 1 ...
>  $ spontaneous   : num  2 0 0 0 1 1 0 0 1 0 ...
>  $ stratum       : int  1 2 3 4 5 6 7 8 9 10 ...
>  $ pooled.stratum: num  3 1 4 2 32 36 6 22 5 19 ...
>
> This explains why you did not see reference levels in those variables.
>
> HTH,
> Jorge.-
>
>
>
> On Tue, Dec 13, 2011 at 11:48 PM, Jorge I Velez <> wrote:
>
>> Hi,
>>
>> Are you sure? That's not what I got:
>>
>> > require(epicalc)
>> > ?logistic.display
>> >  model0 <- glm(case ~ induced + spontaneous, family=binomial,
>> data=infert)
>> > logistic.display(model0)
>>
>> Logistic regression predicting case
>>
>>                          crude OR(95%CI)  adj. OR(95%CI)    P(Wald's test)
>> induced (cont. var.)     1.05 (0.74,1.5)  1.52 (1.02,2.27)  0.042
>>
>> spontaneous (cont. var.) 2.9 (1.97,4.26)  3.31 (2.19,5.01)  < 0.001
>>
>>                          P(LR-test)
>>  induced (cont. var.)     0.042
>>
>> spontaneous (cont. var.) < 0.001
>>
>> Log-likelihood = -139.806
>> No. of observations = 248
>> AIC value = 285.612
>>
>>
>> My impression is that you did something else and you are not telling us
>> the full story.  Here is my sessionInfo():
>>
>> R version 2.14.0 Patched (2011-11-12 r57642)
>> Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
>>
>> locale:
>> [1] US.UTF-8
>>
>> attached base packages:
>> [1] grid      splines   stats     graphics  grDevices utils     datasets
>>  methods
>> [9] base
>>
>> other attached packages:
>>  [1] ggplot2_0.8.9    proto_0.3-9.2    reshape_0.8.4    plyr_1.6
>>  [5] epicalc_2.14.0.0 nnet_7.3-1       MASS_7.3-16      survival_2.36-10
>>  [9] maptools_0.8-10  lattice_0.20-0   foreign_0.8-47   sp_0.9-91
>> [13] maps_2.2-2
>>
>> loaded via a namespace (and not attached):
>> [1] tools_2.14.0
>> *
>> *
>>
>>
>> Could you please share your sessionInfo() as well as your OS, i.e., see
>> http://www.r-project.org/posting-guide.html?
>>
>> HTH,
>> Jorge.-
>>
>>
>> 2011/12/13 孟欣 <>
>>
>>>  According to the example of logistic.display:
>>> model0 <- glm(case ~ induced + spontaneous, family=binomial,
>>> data=infert)
>>> summary(model0)
>>>  logistic.display(model0)
>>>
>>>  induced: 3levels 0,1,2
>>>  spontaneous: 3levels 0,1,2
>>>
>>> So if 0 is reference, we should get 2 OR for
>>> " induced1"," induced2"," spontaneous1"," spontaneous2"
>>>
>>> But the acturally OR is as the following,which is not what I expected:
>>>
>>>                          crude OR(95%CI)  adj. OR(95%CI)    P(Wald's
>>> test) P(LR-test)
>>> induced (cont. var.)     1.05 (0.74,1.5)  1.52 (1.02,2.27)  0.042
>>>    0.042
>>> spontaneous (cont. var.) 2.9 (1.97,4.26)  3.31 (2.19,5.01)  < 0.001
>>>    < 0.001
>>>
>>>
>>> Can anyone give me some suggestions?
>>>
>>> Many thanks!
>>>
>>>
>>>
>>>
>>>
>>>
>>> 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" <> wrote:
>>>
>>> Hi there,
>>>
>>> Try
>>>
>>> require(epicalc)
>>> logistic.display(result)
>>>
>>> HTH,
>>> Jorge
>>>
>>> On Tue, Dec 13, 2011 at 7:16 AM, 孟欣 <> 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]]
>>>>
>>>> ______________________________________________
>>>> 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.
>>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>
>
>
>

	[[alternative HTML version deleted]]

