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.l...@gmail.com
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.
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
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West Hartford, CT
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