[R] too many var in lm

[Eik Vettorazzi]

Hi Carol,
I unsuccessfully tried to get credits right for the following quote (and
they make a lot of fuzz about having citations right around here), so I
have to stick with the plain line:
"Statistics means never having to say you're certain".

Bioconductor has a mailing list on its own, there might be more
qualified advice.

Best.


Am 18.08.2011 14:18, schrieb carol white:
> Thanks Eik for your reply.
> 
> Sure I know the classification method. However, at this stage, I'm working on feature selection. So lmfit and eBays in Limma are more preferable than anova in stats? 
> 
> 
> Best wishes,
> 
> Haleh
> 
> 
> 
> ----- Original Message -----
> From: Eik Vettorazzi <E.Vettorazzi at uke.uni-hamburg.de>
> To: carol white <wht_crl at yahoo.com>
> Cc: 
> Sent: Thursday, August 18, 2011 10:56 AM
> Subject: Re: [R] too many var in lm
> 
> Hi Carol,
> methods for classifying observations are legion, starting from logistic
> regression, discriminant analysis, CART, (hierarchical) cluster analysis...
> 
> When it comes to analysing gene expressions, www.bioconductor.org might
> be the place to visit, especially the limma-package might be promising.
> 
> Regards,
> Eik
> 
> 
> Am 18.08.2011 10:30, schrieb carol white:
>> Thanks Eik for your reply.
>>
>> I have seen that one way to select variables discriminating 2 categories of patients is anova. I saw that the anova function should be applied to an object like the one obtained from lm. so that's why I wanted to apply anova(lm(y~.)). Would you have any suggestions, comments?
>>
>> Regards,
>>
>> Carol
>>
>>
>>
>> ----- Original Message -----
>> From: Eik Vettorazzi <E.Vettorazzi at uke.uni-hamburg.de>
>> To: carol white <wht_crl at yahoo.com>
>> Cc: "r-help at stat.math.ethz.ch" <r-help at stat.math.ethz.ch>
>> Sent: Wednesday, August 17, 2011 3:39 PM
>> Subject: Re: [R] too many var in lm
>>
>> Hi Carol,
>> it might be another question if it is sensible to use 2100 regression
>> parameters, but you can use . to regress one response against all other
>> variables in a data frame as in:
>>
>> lm(formula = mpg ~ ., data = mtcars)
>>
>> and you can even exclude specific variables using "-"
>> lm(formula = mpg ~ . - wt, data = mtcars)
>>
>> cheers.
>>
>> Am 17.08.2011 15:23, schrieb carol white:
>>> Hello,
>>> It might be an easy question but if you have many variables to fit in the lm function, how do you take all without specifying var1+var2+...+var2100 in the terms parameter in response ~ terms?
>>>
>>> Cheers,
>>>
>>> Carol
>>>
>>> ______________________________________________
>>> 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.
>>
> 

-- 
Eik Vettorazzi

Department of Medical Biometry and Epidemiology
University Medical Center Hamburg-Eppendorf

Martinistr. 52
20246 Hamburg

T ++49/40/7410-58243
F ++49/40/7410-57790