Yes,it works well.
Thanks for your help.
At 2011-12-14 13:06:14,"Jorge I Velez" <jorgeivanve...@gmail.com> wrote:
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 ÃÏÐÀ <>
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 ÃÏÐÀ <>
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, 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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