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How to effectively remove Outliers from a binary logistic regression in R
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Marcus Tullius  
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 More options Sep 5 2012, 3:40 am
From: "Marcus Tullius" <tull...@europe.com>
Date: Wed, 05 Sep 2012 03:40:56 -0400
Local: Wed, Sep 5 2012 3:40 am
Subject: [R] How to effectively remove Outliers from a binary logistic regression in R
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Hallo there,

 greetings from Germany.

 I have a simple question for you.

 I have run a binary logistic model, but there are lots of outliers distorting the real results.

 I have tried to get rid of the outliers using the following commands:

 remove = -c(56, 303, 365, 391, 512, 746, 859, 940, 1037, 1042, 1138, 1355)
 MIGRATION.rebuild <- glm(MIGRATION, subset=remove)
 influence(MIGRATION.rebuild)
 influence.measures(MIGRATION.rebuild)

 BUT it did not work.

 My question is:

 *Do you know a simple R-command which erases outliers and rebuilds the model without them?*

 I am including my model below so that you may have an idea of how I am trying to do it.

 Thanks in advance for your help.

 Francisco M. da Rocha

        [[alternative HTML version deleted]]

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Jim Lemon  
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 More options Sep 5 2012, 6:17 am
From: Jim Lemon <j...@bitwrit.com.au>
Date: Wed, 05 Sep 2012 20:15:09 +1000
Local: Wed, Sep 5 2012 6:15 am
Subject: Re: [R] How to effectively remove Outliers from a binary logistic regression in R
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On 09/05/2012 05:40 PM, Marcus Tullius wrote:

Hi Francisco,
Your model didn't make it to the help list, but I think that the problem
is in your attempt to use the "subset" argument in glm. The vector is
supposed to include the indices of the values that you _want_ in the
analysis, and it looks like you are trying to remove the values that you
_don't_ want. Say you have 2000 rows in your data frame in the model.
The "subset" argument should look something like this:

glm(MIGRATION,
  subset=!(1:2000 %in% c(56,303,365,391,512,746,859,940,1037,1042,1138,
1355))

Jim

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