On Jan 12, 2013, at 5:00 PM, peter dalgaard wrote:
On Jan 12, 2013, at 23:33 , Rolf Turner wrote:
We don't do people's homework for them.
But since you seem to have put in at least a little bit of your
own effort ..... It is perfectly possible for there to be an
interaction
without there being main effects.
Consider two factors A and B each with two levels. Let mu_11 be
the population mean when A is at level 1 and B is at level 1, and so
on.
Suppose mu_11 = 1, mu_12 = -1, mu_21 = -1, and mu_22 = 1.
Then there are no main effects; A averages to 0, as does B.
But there is an elephant-ful of interaction.
Also note that coefficients for main effects in the present of
interactions have a different interpretation, depending on the
coding of contrasts. In the summary table you cite, the value 7.101
is actually the effect of Sex within TestNumber1 and the interaction
terms are the differences between that effect and those of Sex
within the other two groups. Only if the latter terms are set to
zero, the coefficient for Sex becomes the Sex effect for all groups.
(All assuming that you haven't been messing with options("contrasts"))
I will step over the line (or ellipse) that defines my professional
credentials and say that one should never attempt the maneuver
described in the subject line. Instead one should construct and
compare the effect estimates. With R that is most compactly done with
'predict' methods.
--
David.
Best,
Peter D.
cheers,
Rolf Turner
cheers,
Rolf Turner
On 01/13/2013 10:56 AM, theundergrad wrote:
Hi,
I am trying to interpret the coefficients in the model:
RateOfMotorPlay ~
TestNumber + Sex + TestNumber * Sex where there are thee different
tests and
Sex is (obviously) binary. My results are: Residuals:
Min 1Q Median 3Q Max
-86.90 -26.28 -7.68 22.52 123.74
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.430 6.248 4.710 4.80e-06 ***
TestNumber2 56.231 8.837 6.364 1.47e-09 ***
TestNumber3 75.972 10.061 7.551 1.82e-12 ***
SexM 7.101 9.845 0.721 0.472
TestNumber2:SexM -16.483 13.854 -1.190 0.236
TestNumber3:SexM -24.571 15.343 -1.601 0.111
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 40.97 on 188 degrees of freedom
Multiple R-squared: 0.3288, Adjusted R-squared: 0.3109
F-statistic: 18.42 on 5 and 188 DF, p-value: 7.231e-15
I am looking for one number that will represent the significance
of the
interaction term. I was thinking of doing an F test comparing this
model to
one without the interaction. When I do this, I get a highly
significant
result. I am not exactly sure how to interpret this. In
particular, it seems
strange to me to have a significant interaction term without both
independent variables being significant. Any advice would be highly
appreciated.
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--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd....@cbs.dk Priv: pda...@gmail.com
______________________________________________
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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
Alameda, CA, USA
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
