Ajay ohri wrote:
Whats the R equivalent for Proc logistic in SAS ?
glm with the appropriate family (binomial) and link, I guess.
There is a book 'R for SAS and SPSS users' forthcoming
http://www.springer.com/statistics/computational/book/978-0-387-09417-5
Is there a stepwise
method there ?
See
library(MASS)
?stepAIC
for an example; the following might provide a useful read
on stepwise methods:
http://www.pitt.edu/~wpilib/statfaq/regrfaq.html
How to create scoring models in R , for larger datasets (200 mb), Is
there a way to compress and use datasets (like options compress=yes;)
Fit the model using glm and 'score' using the predict method.
200 Mb isn't that large anymore, but see Thomas Lumley's biglm
package for a bounded-memory version if you're working on
limited hardware.
HTH,
Tobias
On Wed, Sep 10, 2008 at 11:12 AM, Peter Dalgaard
<[EMAIL PROTECTED]> wrote:
Rolf Turner wrote:
For one thing your call to glm() is wrong --- didn't you notice the
warning messages about ``non-integer #successes in a binomial glm!''?
You need to do either:
glm(r/k ~ x, family=binomial(link='cloglog'), data=bin_data,
offset=log(y), weights=k)
or:
glm(cbind(r,k-r) ~ x, family=binomial(link='cloglog'), data=bin_data,
offset=log(y))
You get the same answer with either, but this answer still does not agree
with your
SAS results. Perhaps you have an error in your SAS syntax as well. I
wouldn't know.
The data created in the data step are not those used in the analysis.
Changing to
data nelson;
<etc>
gives the same result as R on the versions I have available:
Analysis Of Parameter
Estimates
Standard Wald 95%
Confidence Chi-
Parameter DF Estimate Error Limits
Square Pr > ChiSq
Intercept 1 -3.5866 2.2413 -7.9795
0.8064 2.56 0.1096
x 1 0.9544 2.8362 -4.6046
6.5133 0.11 0.7365
Scale 0 1.0000 0.0000 1.0000
1.0000
and
Call:
glm(formula = r/k ~ x, family = binomial(link = "cloglog"), data = bin_data,
weights = k, offset = log(y))
Deviance Residuals: 1 2 3 4 0.5407 -0.9448
-1.0727 0.7585
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.5866 2.2413 -1.600 0.110
x 0.9544 2.8362 0.336 0.736
cheers,
Rolf Turner
On 10/09/2008, at 10:37 AM, sandsky wrote:
Hello,
I have different results from these two softwares for a simple binomial
GLM
problem.
From Genmod in SAS: LogLikelihood=-4.75, coeff(intercept)=-3.59,
coeff(x)=0.95
From glm in R: LogLikelihood=-0.94, coeff(intercept)=-3.99,
coeff(x)=1.36
Is there anyone tell me what I did wrong?
Here are the code and results,
1) SAS Genmod:
% r: # of failure
% k: size of a risk set
data bin_data;
input r k y x;
os=log(y);
cards;
1 3 5 0.5
0 2 5 0.5
0 2 4 1.0
1 2 4 1.0
;
proc genmod data=nelson;
model r/k = x / dist = binomial link =cloglog offset = os ;
<Results from SAS>
Log Likelihood -4.7514
Parameter DF Estimate Error Limits
Square Pr > ChiSq
Intercept 1 -3.6652 1.9875 -7.5605 0.2302
3.40 0.0652
x 1 0.8926 2.4900 -3.9877 5.7728
0.13 0.7200
Scale 0 1.0000 0.0000 1.0000 1.0000
2) glm in R
bin_data <-
data.frame(cbind(y=c(5,5,4,4),r=c(1,0,0,1),k=c(3,2,2,2),x=c(0.5,0.5,1.0,1.0)))
glm(r/k ~ x, family=binomial(link='cloglog'), data=bin_data,
offset=log(y))
<Results from R>
Coefficients:
(Intercept) x
-3.991 1.358
'log Lik.' -0.9400073 (df=2)
######################################################################
Attention:\ This e-mail message is privileged and confid...{{dropped:9}}
______________________________________________
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.
--
O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907
______________________________________________
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.
______________________________________________
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.
