[R-sig-ME] Dummy variables in Factors with more than 2 levels

[Martin Henry H. Stevens]

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
>
>
> _________________________________________________________________
>
>
>         [[alternative HTML version deleted]]
>
> <ATT00001.txt>

Dr. Hank Stevens, Associate Professor
338 Pearson Hall
Botany Department
Miami University
Oxford, OH 45056

Office: (513) 529-4206
Lab: (513) 529-4262
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http://www.cas.muohio.edu/~stevenmh/
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