Hi there,
I have a linear model with one factor having three levels.
I want to check if the different levels significantly differ from the overall
mean (using contr.sum).
However one level (the last) is omitted in the standard procedure.
To illustrate this:
x <- as.factor(c(1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3))
y <-
c(1.1,1.15,1.2,1.1,1.1,1.1,1.2,1.2,1.2,2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9,3,3.1)
test <- data.frame(x,y)
reg1 <- lm(y~C(x,contr.sum),data=test)
summary(reg1)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.63333 0.06577 24.834 8.48e-15 ***
C(x, contr.sum)1 -0.48333 0.10792 -4.479 0.00033 ***
C(x, contr.sum)2 -0.48333 0.08936 -5.409 4.70e-05 ***
Is it possible to get the effect for the third level (against the overall mean)
in the table too.
I figured out:
reg2 <- lm(y~C(relevel(x,3),contr.sum),data=test)
summary(reg2)
C(relevel(x, 3), contr.sum)1 0.96667 0.07951 12.158 8.24e-10 ***
C(relevel(x, 3), contr.sum)2 -0.48333 0.10792 -4.479 0.00033 ***
The first row now test the third level against the overall mean, but I find
this approach not so convenient.
Moreover, I wonder if it is meaningful at all regarding the cumulation of alpha
error. Would a Bonferroni correction be sensible?
Greetings and thanks in advance
Jürgen
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