Dummy variables in Factors with more than 2 levels

By default, R uses the 'opposite' approach: the intercept is the mean of the
first level, and the other parameters of the differences between the first
level and that level. See ?contrasts
Hank
On May 21, 2008, at 5:59 AM, carlos ramirez wrote:

Hi All,

Sorry to bother with a
basic question.

I was wondering how R
manages dummy variables when computing factors with more than 2 levels.
For
instance in my study I have the variable 'stress' with 3 levels
('pre-tonic', 'tonic',
and 'pos-tonic' coded, '1', 2' and '3' respectively).

Programs such as SPSS transform
nominal and ordinal categories into sets of dichotomies ( dummy variables)
in
such a way that a computed dummy variable 1 (dummy pre-tonic) will assign
1 to
all pre-tonic stress and '0' to all the others. Dummy variable 2 (dummy
tonic)
assigns '1' to all tonic data and '0' to the rest. By default SPSS leaves
the
last level as the 'reference category' (in this case post-tonic) for
comparison.
Using what is called the 'indicator contrast'. Thus, the coding ends up
being
something like the example below



------------------------------------------------
 Dummy variables              Value     Coding                         (1)
 (2)Stress                  1     1.000   .000                  2      .000
 1.000                  3      .000   .000



Thus, in the outcome,  Beta (B) and Exp (B) do not present the odds
ratio of the dependent variable in relation to the independent variable
but odds
ratio of the dummy variables with respect to the reference category
(post-tonic
in this case).



 When I run the mix log model in R I get an
outpost like the following.



Generalized linear
mixed model fit using Laplace

Formula: Identif ~ (1
| Subj) + (1 | Item) + Place +  Stress
+      Voicing

  Data: idcrg1

 Family: binomial(logit link)

 AIC
BIC logLik deviance

 1163 1211 -572.6     1145

Random effects:

 Groups Name        Variance Std.Dev.

 Subj
(Intercept) 0.63178  0.79485

 Item
(Intercept) 0.88192  0.93910

number of obs: 1476,
groups: Subj, 41; Item, 36



 Estimated scale
(compare to  1 )  0.888108



 Fixed effects:

           Estimate Std. Error z value Pr(>|z|)

(Intercept)   1.9920
0.4948   4.026 5.67e-05 ***

Place2       -0.7253     0.4376
-1.658   0.0974 .

Place3       -0.1389     0.4478
-0.310   0.7565

Stress2       0.8765     0.4493
1.951   0.0511 .

Stress3      -0.2386     0.4298
-0.555   0.5788

Voicing2      0.6937
0.3601   1.927   0.0540 .


---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' '
1




Correlation of Fixed
Effects:

        (Intr) Place2 Place3  Strss2 Strss3

Place2   -0.466

Place3   -0.447
0.511

Stress2  -0.426 -0.035 -0.002  0.026

Stress3  -0.451
0.004 -0.006  0.017  0.485


Voicing2 -0.356  0.004 -0.023
0.008  0.020  0.011






Based on the index
that appears on Stress in the Fixed Effects outcome (Stress2 and Stress3;
same
for Place2 and Place3) .

Am I correct to assume
that the reference category in this case was the first level and not the
last
as it is done in SPSS?

Does R create dummy
variables to calculate the regression?




Thanks for your time. I'd
appreciate any help you could provide.

Sincerely,Carlos