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" <jorgeivanve...@gmail.com> wrote:
Hi there,
Try
require(epicalc)
logistic.display(result)
HTH,
Jorge
On Tue, Dec 13, 2011 at 7:16 AM, ÃÏÐÀ <> 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!
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