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.
C
On Tue, Nov 15, 2011 at 9:00 AM, Catarina Miranda
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 example and in my rea
... 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
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 s
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 o
5 matches
Mail list logo
