Re: [R] stepAIC invalid scope argument

From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>
Date: Tue 16 Aug 2005 - 01:33:22 EST

On Mon, 15 Aug 2005, Adaikalavan Ramasamy wrote:

> You are right, it works fine with a different name. Its a bad habit that
> I need to shake off.
>
> The error message said that the second argument was invalid. The second
> argument in stepAIC and addterm is 'scope' and thus the title.

OK, we'll improve the error message ....

>
> Thank you again.
>
> Regards, Adai
>
>
>
> On Mon, 2005-08-15 at 15:23 +0100, Prof Brian Ripley wrote:
>> Try not to use the name of an R object ... the error is caused by using
>> 'df' as the second argument to eval().
>>
>> It works with DF in place of df.
>>
>> I don;t understand your subject line: that is not the error message you
>> received.
>>
>> On Mon, 15 Aug 2005, Adaikalavan Ramasamy wrote:
>>
>>> I am trying to replicate the first example from stepAIC from the MASS
>>> package with my own dataset but am running into error. If someone can
>>> point where I have gone wrong, I would appreciate it very much.
>>>
>>> Here is an example :
>>>
>>> set.seed(1)
>>> df <- data.frame( x1=rnorm(1000), x2=rnorm(1000), x3=rnorm(1000) )
>>> df$y <- 0.5*df$x1 + rnorm(1000, mean=8, sd=0.5)
>>> # pairs(df); head(df)
>>>
>>> lo <- aov( y ~ 1, data=df )
>>> hi <- aov( y ~ .^2, data=df )
>>> mid <- aov( y ~ x2 + x3, data=df )
>>>
>>> Running any of the following commands
>>>
>>> stepAIC( mid, scope=list(upper = ~x1 + x2 + x3 , lower = ~1) )
>>> stepAIC( mid, scope=list(upper = hi , lower = lo) )
>>> addterm( mid, ~ x1 + x2 + x3 )
>>> addterm( lo, hi )
>>>
>>> gives the same error message :
>>> Error in eval(expr, envir, enclos) : invalid second argument
>>>
>>> Here is a traceback of the first failed command :
>>> 14: eval(predvars, data, env)
>>> 13: model.frame.default(formula = y ~ x2 + x3 + x1, data = df, drop.unused.levels = TRUE)
>>> 12: model.frame(formula = y ~ x2 + x3 + x1, data = df, drop.unused.levels = TRUE)
>>> 11: eval(expr, envir, enclos)
>>> 10: eval(mf, parent.frame())
>>> 9: lm(formula = y ~ x2 + x3 + x1, data = df, method = "model.frame")
>>> 8: eval(expr, envir, enclos)
>>> 7: eval(fcall, env, parent.frame())
>>> 6: model.frame.lm(fob, xlev = object$xlevels)
>>> 5: model.frame(fob, xlev = object$xlevels)
>>> 4: stats:::add1.lm(object, scope = scope, scale = scale)
>>> 3: addterm.lm(fit, scope$add, scale = scale, trace = max(0, trace - 1), k = k, ...)
>>> 2: addterm(fit, scope$add, scale = scale, trace = max(0, trace - 1), k = k, ...)
>>> 1: stepAIC(mid, scope = list(upper = ~x1 + x2 + x3, lower = ~1))
>>>
>>> Any pointers would be much appreciated. Thank you.
>>>
>>> Regards, Adai
>>>
>>> ______________________________________________
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>>>
>>
>
>

-- 
Brian D. Ripley,                  ripley@stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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Received on Tue Aug 16 01:42:49 2005

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