R help - Dec 2010 - What is the SAS equivalent of this R glm() code?

 
 
Hello Everyone,
 
I'm trying to use SAS to replicate some results obtained in R. I was
wondering if anyone call tell me the SAS equivalent of the code that appears
below.
 
fm.glm.x <- glm(resp ~ . - 1, data = as.data.frame(mm.x),  
na.action = na.exclude, family = binomial(link = "probit"))
summary(fm.glm.x)

Thanks,
 
Paul 


	[[alternative HTML version deleted]]

ummm...

This is an R list. Shouldn't you be posting this query on a SAS list?

-- Bert

(and why would you want to do this anyway?! ... but we won't go there.)

On Fri, Dec 3, 2010 at 1:42 PM, Paul Miller <pjmiller_57 at yahoo.com>
wrote:
>
>
> Hello Everyone,
>
> I'm trying to use SAS to replicate some results obtained in R. I was
wondering if anyone call tell me the SAS equivalent of the code that appears
below.
>
> fm.glm.x <- glm(resp ~ . - 1, data = as.data.frame(mm.x),
> na.action = na.exclude, family = binomial(link = "probit"))
> summary(fm.glm.x)
>
> Thanks,
>
> Paul
>
>
> ? ? ? ?[[alternative HTML version deleted]]
>
>
> ______________________________________________
> R-help at 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.
>
>


-- 
Bert Gunter
Genentech Nonclinical Biostatistics

On Dec 3, 2010, at 22:42 , Paul Miller wrote:
>  
>  
> Hello Everyone,
>  
> I'm trying to use SAS to replicate some results obtained in R. I was
wondering if anyone call tell me the SAS equivalent of the code that appears
below.
>  
> fm.glm.x <- glm(resp ~ . - 1, data = as.data.frame(mm.x),  
> na.action = na.exclude, family = binomial(link = "probit"))
> summary(fm.glm.x)
Well, you're not saying what "." contains, so no-one can foresee
all implications, but the closest parallels are usually found with PROC GENMOD.
Something like

  proc genmod data=ingot2;
  model r/n=t / dist=binomial link=probit;
  run;

(shamelessly lifted from a Google search)

Notes: 

- I don't think you can avoid spelling out the list of variables in
"."

- "-1" (no intercept) is obtained by adding "noint" after
the "/" in the model statement

- for binary regression, I believe you may have to explicitly generate a
variable n=1

- beware differences in contrast parametrizations for categorical variables




> 
> Thanks,
>  
> Paul 
> 
> 
> 	[[alternative HTML version deleted]]
> 
> ______________________________________________
> R-help at 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.
-- 
Peter Dalgaard
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com