On Apr 17, 2013, at 7:23 AM, Liviu Andronic <landronim...@gmail.com> wrote:
> 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
If you only want up to say second order interactions:
> summary(lm(mpg ~ (cyl + disp + hp + drat) ^ 2, data = mtcars))
Call:
lm(formula = mpg ~ (cyl + disp + hp + drat)^2, data = mtcars)
Residuals:
Min 1Q Median 3Q Max
-3.5487 -1.6998 0.0894 1.2366 4.6138
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.816e+01 4.199e+01 2.338 0.0294 *
cyl -1.656e+01 1.226e+01 -1.351 0.1910
disp 1.333e-03 1.634e-01 0.008 0.9936
hp -1.936e-01 2.260e-01 -0.857 0.4014
drat -8.913e+00 8.745e+00 -1.019 0.3197
cyl:disp 2.134e-02 1.071e-02 1.992 0.0595 .
cyl:hp 3.074e-02 1.970e-02 1.560 0.1337
cyl:drat 2.590e+00 2.601e+00 0.996 0.3307
disp:hp -3.846e-04 3.906e-04 -0.985 0.3359
disp:drat -3.518e-02 3.951e-02 -0.890 0.3834
hp:drat 1.210e-02 5.432e-02 0.223 0.8259
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.717 on 21 degrees of freedom
Multiple R-squared: 0.8623, Adjusted R-squared: 0.7967
F-statistic: 13.15 on 10 and 21 DF, p-value: 6.237e-07
This is covered in ?formula
Regards,
Marc Schwartz
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