Thanks a lot for your answers and reading suggestions, now I know my guess
was completely wrong.
I guess in my case it will be more informative to keep the unordered
factors. That way I can know not only that days differ in general, but also
get information on which day is differing from day 1.
Hello;
I am having a problems with the interpretation of models using ordered or
unordered predictors.
I am running models in lmer but I will try to give a simplified example
data set using lm.
Both in the example and in my real data set I use a predictor variable
referring to 3 consecutive days
Ordered factors use orthogonal polynomial contrasts by default. The .L and
.Q stand for the linear and quadratic terms. Unordered factors use
treatment contrasts although (they're actually not contrasts), that are
interpreted as you described.
If you do not know what this means, you need to do
... In addition, the following may also be informative.
f - paste(day, 1:3)
contrasts(ordered(f))
.L .Q
[1,] -7.071068e-01 0.4082483
[2,] -7.850462e-17 -0.8164966
[3,] 7.071068e-01 0.4082483
contrasts(factor(f))
day 2 day 3
day 1 0 0
day 2 1 0
On Tue, Nov 15, 2011 at 9:00 AM, Catarina Miranda
catarina.mira...@gmail.com wrote:
Hello;
I am having a problems with the interpretation of models using ordered or
unordered predictors.
I am running models in lmer but I will try to give a simplified example
data set using lm.
Both in the
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