On Aug 3, 2010, at 9:51 AM, haenl...@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.
> RSiteSearch("moderation models") # 3 hits
> RSiteSearch("moderated models") #12 hits
> RSiteSearch("moderat* models") 139 hits
--
David.
Sorry for the confusion caused due to my previous message,
Michael
On Aug 3, 2010 3:42pm, David Winsemius <dwinsem...@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@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
>
>
>
David Winsemius, MD
West Hartford, CT
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