R help - Feb 2012 - stepwise variable selection with multiple dependent variables

Good Day,

I fit a multivariate linear regression model with 3 dependent variables and
several predictors using the lm function.  I would like to use stepwise variable
selection to produce a set of candidate models.  However, when I pass the fitted
lm object to step() I get the following error:

Error from R:
Error in drop1.mlm(fit, scope$drop, scale = scale, trace = trace, k = k,  : 
  no 'drop1' method for "mlm" models

My dependent data is in the matrix ymat where ymat is 35 rows by 3 columns.  The
predictors are in X where X is 35 by 6

The steps I used were:
m.fit <- lm(ymat ~ ., data=X)
m.step <- step(m.fit)

If variable selection is not possible with step() is there another package that
will perform variable selection in a multivariate setting?

System information:
platform       x86_64-apple-darwin9.8.0     
arch           x86_64                       
os             darwin9.8.0                  
system         x86_64, darwin9.8.0          
status                                      
major          2                            
minor          13.1                         
year           2011                         
month          07                           
day            08                           
svn rev        56322                        
language       R                            
version.string R version 2.13.1 (2011-07-08)

Thanks in advance.

"Anova.mlm" would be one way to do model selection.



On Fri, Feb 10, 2012 at 4:29 PM, Fugate, Michael L <fugate at lanl.gov>
wrote:
> Good Day,
>
> I fit a multivariate linear regression model with 3 dependent variables and
several predictors using the lm function. ?I would like to use stepwise variable
selection to produce a set of candidate models. ?However, when I pass the fitted
lm object to step() I get the following error:
>
> Error from R:
> Error in drop1.mlm(fit, scope$drop, scale = scale, trace = trace, k = k, ?:
> ?no 'drop1' method for "mlm" models
>
> My dependent data is in the matrix ymat where ymat is 35 rows by 3 columns.
?The predictors are in X where X is 35 by 6
>
> The steps I used were:
> m.fit <- lm(ymat ~ ., data=X)
> m.step <- step(m.fit)
>
> If variable selection is not possible with step() is there another package
that will perform variable selection in a multivariate setting?
>
> System information:
> platform ? ? ? x86_64-apple-darwin9.8.0
> arch ? ? ? ? ? x86_64
> os ? ? ? ? ? ? darwin9.8.0
> system ? ? ? ? x86_64, darwin9.8.0
> status
> major ? ? ? ? ?2
> minor ? ? ? ? ?13.1
> year ? ? ? ? ? 2011
> month ? ? ? ? ?07
> day ? ? ? ? ? ?08
> svn rev ? ? ? ?56322
> language ? ? ? R
> version.string R version 2.13.1 (2011-07-08)
>
> Thanks in advance.
>
> ______________________________________________
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