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List:       r-help
Subject:    [R] Fastbw() function: grouping of variables
From:       "Mourik-2, M.S.M. van" <M.S.M.vanMourik-2 () umcutrecht ! nl>
Date:       2013-04-30 14:15:47
Message-ID: E5F7A1BADD8F404EBE447CB35C77924E12BFAC4F () EXMB2502 ! ds ! umcutrecht ! nl
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Dear R users,

For the purpose of validating a prediction model using validate() from the rms \
package, I am running into some trouble with using the fastbw() function breaking up \
natural groups of variables.

Is there any way I can specify to keep certain variable together? In particular, if \
interactions are included I would also like to keep the main effects in the model. \
Another example is a group of comorbidity variables that I would like to be assessed \
all together and not as separate components as this makes more sense from the \
perspective of interpretation.

The Force = ... option seems to only work for pre-specified variables and does not \
solve my problem. In the case of the comorbidites, the variables are not mutually \
exclusive, so I would be very difficult to recode them as a factor variable with \
multiple levels.

Examples:
fit1 <-lrm(outcome~age + gender + med_a + med_b + med_a:med_b, x = TRUE, y = TRUE, \
data = example) fastbw(fit1, type = "residual).
--> fastbw may drop med_a and keep the med_a:med_b interaction instead.

fit2 <-lrm(outcome~age + gender +  comorb_a + comorb_b + comorb_c + other predictors, \
x = TRUE, y = TRUE, data = example) fastbw(fit2, type = "residual).
--> fastbw may drop some of the comorbidities, but  I would like to consider them as \
one group.

Any help is much appreciated!

Maaike


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