[R] Collinearity in Moderated Multiple Regression

[Nikhil Kaza]

My usual strategy of dealing with multicollinearity is to drop the  
offending variable or transform one them. I would also check vif  
functions in car and Design.

I think you are looking for lm.ridge in MASS package.


Nikhil Kaza
Asst. Professor,
City and Regional Planning
University of North Carolina

nikhil.list at gmail.com

On Aug 3, 2010, at 9:51 AM, haenlein at gmail.com wrote:

> I'm sorry -- I think I chose a bad example. Let me start over again:
>
> I want to estimate a moderated regression model of the following form:
> y = a*x1 + b*x2 + c*x1*x2 + e
>
> Based on my understanding, including an interaction term (x1*x2)  
> into the
> regression in addition to x1 and x2 leads to issues of  
> multicollinearity,
> as x1*x2 is likely to covary to some degree with x1 (and x2). One
> recommendation I have seen in this context is to use mean centering,  
> but
> apparently this does not solve the problem (see: Echambadi, Raj and  
> James
> D. Hess (2007), "Mean-centering does not alleviate collinearity  
> problems in
> moderated multiple regression models," Marketing science, 26 (3),  
> 438 -
> 45). So my question is: Which R function can I use to estimate this  
> type of
> model.
>
> Sorry for the confusion caused due to my previous message,
>
> Michael
>
>
>
>
>
>
> On Aug 3, 2010 3:42pm, David Winsemius <dwinsemius at comcast.net> wrote:
>> I think you are attributing to "collinearity" a problem that is due  
>> to
>> your small sample size. You are predicting 9 points with 3 predictor
>> terms, and incorrectly concluding that there is some "inconsistency"
>> because you get an R^2 that is above some number you deem  
>> surprising. (I
>> got values between 0.2 and 0.4 on several runs.
>
>
>
>> Try:
>
>> x1
>> x2
>> x3
>
>
>> y
>> model
>> summary(model)
>
>
>
>> # Multiple R-squared: 0.04269
>
>
>
>> --
>
>> David.
>
>
>
>> On Aug 3, 2010, at 9:10 AM, Michael Haenlein wrote:
>
>
>
>
>> Dear all,
>
>
>
>> I have one dependent variable y and two independent variables x1  
>> and x2
>
>> which I would like to use to explain y. x1 and x2 are design  
>> factors in an
>
>> experiment and are not correlated with each other. For example assume
>> that:
>
>
>
>> x1
>> x2
>> cor(x1,x2)
>
>
>
>> The problem is that I do not only want to analyze the effect of x1  
>> and x2
>> on
>
>> y but also of their interaction x1*x2. Evidently this interaction  
>> term
>> has a
>
>> substantial correlation with both x1 and x2:
>
>
>
>> x3
>> cor(x1,x3)
>
>> cor(x2,x3)
>
>
>
>> I therefore expect that a simple regression of y on x1, x2 and  
>> x1*x2 will
>
>> lead to biased results due to multicollinearity. For example, even  
>> when y
>> is
>
>> completely random and unrelated to x1 and x2, I obtain a  
>> substantial R2
>> for
>
>> a simple linear model which includes all three variables. This  
>> evidently
>
>> does not make sense:
>
>
>
>> y
>> model
>> summary(model)
>
>
>
>> Is there some function within R or in some separate library that  
>> allows me
>
>> to estimate such a regression without obtaining inconsistent results?
>
>
>
>> Thanks for your help in advance,
>
>
>
>> Michael
>
>
>
>
>
>> Michael Haenlein
>
>> Associate Professor of Marketing
>
>> ESCP Europe
>
>> Paris, France
>
>
>
>> [[alternative HTML version deleted]]
>
>
>
>> ______________________________________________
>
>> 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.
>
>
>
>
>> David Winsemius, MD
>
>> West Hartford, CT
>
>
>
>
> 	[[alternative HTML version deleted]]
>
> ______________________________________________
> 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.