On Dec 2, 2011, at 9:51 AM, Tal Galili wrote:
Hello dear all,
I am unable to understand why when I run the following three lines:
set.seed(4254)
a <- data.frame(y = rnorm(40), x=ordered(sample(1:5, 40, T)))
summary(lm(y ~ x, a))
The output I get includes factor levels which are not relevant to
what I am
actually using:
Call:
lm(formula = y ~ x, data = a)
Residuals:
Min 1Q Median 3Q Max
-1.4096 -0.6400 -0.1244 0.5886 2.1891
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.03276 0.15169 -0.216 0.830
x.L -0.28968 0.33866 -0.855 0.398
x.Q -0.38813 0.33851 -1.147 0.259
x.C -0.27183 0.34027 -0.799 0.430
x^4 0.25993 0.33935 0.766 0.449
Those are polynomial contrasts: linear, quadratic, cubic and quartic.
If you don't want contrasts based on ordered factors then just use
regular factors. You should probably be looking at:
?"C"
(...yet another function whose name should be avoided in naming data-
objects.)
--
David.
Residual standard error: 0.9564 on 35 degrees of freedom
Multiple R-squared: 0.08571, Adjusted R-squared: -0.01878
F-statistic: 0.8202 on 4 and 35 DF, p-value: 0.5211
I am guessing that this is having something to do with the contrast
matrix
that is used, but this is not clear to me.
Can anyone suggest a good read, or an explanation?
Thanks.
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