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Hi Barjesh,
I am not sure which data you analyze, but once I had a similar situation
and it was a multicolinearity issue. I realized this after finding a
huge correlation between the independent variables, then I dropped one
of them and signs of slopes made sense.
Beforehand, have a glance at your correlation matrix of independent
variables to see the relationship between them.
Not sure how helpful my advice will be, but it did the trick for me back
then ;)
Cheers,
Filipe Botelho
-----Mensagem original-----
De: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
Em nome de Michael Friendly
Enviada em: sexta-feira, 10 de junho de 2011 12:51
Para: Barjesh Kochar
Cc: r-help@r-project.org
Assunto: Re: [R] Linear multivariate regression with Robust error
On 6/10/2011 12:23 AM, Barjesh Kochar wrote:
> Dear all,
>
> i am doing linear regression with robust error to know the effect of
> a (x) variable on (y)other if i execute the command i found positive
> trend.
> But if i check the effect of number of (x.x1,x2,x3)variables
> on same (y)variable then the positive effect shwon by x variable turns
> to negative. so plz help me in this situation.
>
> Barjesh Kochar
> Research scholar
>
You don't give any data or provide any code (as the posting guide
requests) , so I have to guess that you
have just rediscovered Simpson's paradox -- that the coefficient of a
variable in a marginal regression can have an opposite sign to that in
a joint model with other predictors. I have no idea what you mean
by 'robust error'.
One remedy is an added-variable plot which will show you the partial
contributions of each predictor in the joint model, as well as whether
there are any influential observations that are driving the estimated
coefficients.
--
Michael Friendly Email: friendly AT yorku DOT ca
Professor, Psychology Dept.
York University Voice: 416 736-5115 x66249 Fax: 416 736-5814
4700 Keele Street Web: http://www.datavis.ca
Toronto, ONT M3J 1P3 CANADA
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