Sir:
I find out that for 2 level factor, if I set it to "factor", then I'll get
error reply.
For the instance last mail, if I use:
result1<-glm(y ~ gender,family = binomial);#gender has 2 levels
logistic.display(result1)
Error in coeff[, 1] : incorrect number of dimensions
if I use:
result1<-glm(y ~ factor(age),family = binomial); #age has 3 levels
logistic.display(result1)
It works well.
So I have a suggestion that no matter the number of levels, if I set it to
factor, logistic.disply can all works well.
logistic.display
At 2011-12-14 13:53:21,"Virasakdi Chongsuvivatwong" <cvira...@gmail.com> wrote:
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 ÃÏÐÀ <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, 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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