Dear all,
Consider the model below:
> x <- lm(mpg ~ cyl * disp * hp * drat, mtcars)
> summary(x)
Call:
lm(formula = mpg ~ cyl * disp * hp * drat, data = mtcars)
Residuals:
Min 1Q Median 3Q Max
-3.5725 -0.6603 0.0108 1.1017 2.6956
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.070e+03 3.856e+02 2.776 0.01350 *
cyl -2.084e+02 7.196e+01 -2.896 0.01052 *
disp -6.760e+00 3.700e+00 -1.827 0.08642 .
hp -9.302e+00 3.295e+00 -2.823 0.01225 *
drat -2.824e+02 1.073e+02 -2.633 0.01809 *
cyl:disp 1.065e+00 5.034e-01 2.116 0.05038 .
cyl:hp 1.587e+00 5.296e-01 2.996 0.00855 **
disp:hp 7.422e-02 3.461e-02 2.145 0.04769 *
cyl:drat 5.652e+01 2.036e+01 2.776 0.01350 *
disp:drat 1.824e+00 1.011e+00 1.805 0.08990 .
hp:drat 2.600e+00 9.226e-01 2.819 0.01236 *
cyl:disp:hp -1.050e-02 4.518e-03 -2.323 0.03368 *
cyl:disp:drat -2.884e-01 1.392e-01 -2.071 0.05484 .
cyl:hp:drat -4.428e-01 1.504e-01 -2.945 0.00950 **
disp:hp:drat -2.070e-02 9.568e-03 -2.163 0.04600 *
cyl:disp:hp:drat 2.923e-03 1.254e-03 2.331 0.03317 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.245 on 16 degrees of freedom
Multiple R-squared: 0.9284, Adjusted R-squared: 0.8612
F-statistic: 13.83 on 15 and 16 DF, p-value: 2.007e-06
Is there a straightforward way to remove the highest order interaction
terms? Say:
cyl:disp:hp
cyl:disp:drat
cyl:hp:drat
disp:hp:drat
cyl:disp:hp:drat
I know I could do this:
> x <- lm(mpg ~ cyl * disp * hp * drat - cyl:disp:hp - cyl:disp:drat -
> cyl:hp:drat - disp:hp:drat - cyl:disp:hp:drat, mtcars)
But I was hoping for a more elegant solution. Regards,
Liviu
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