Direction of Estimate coefficient in a Log Regression

I'm analysing ordinal logistic regression and I'm wondering, how to know which direction the estimate coefficient has? My Variables are just 0, 1 for Women, Men and 0,1,2,4 for different postures. So my question is, how do I know, if the estimate describes the change from 0 to 1 or the change from 1 to 0, talking about gender?

The output added a 1 to PicSex, is it a sign, that this one has a 1->0 direction? See the code for that.

Thank you for any help


Cumulative Link Mixed Model fitted with the Laplace approximation

formula: Int ~ PicSex + Posture + (1 | PicID)
data:    x

Random effects:
 Groups Name        Variance Std.Dev.
 PicID  (Intercept) 0.0541   0.2326  
Number of groups:  PicID 16 

Coefficients:
        Estimate Std. Error z value Pr(>|z|)    
PicSex1   0.3743     0.1833   2.042   0.0411 *  
Posture  -1.1232     0.1866  -6.018 1.77e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1






1 answer

  • answered 2019-11-08 22:16 StupidWolf

    Your sex has two levels,0 or 1. So PicSex1 means the effect of PicSex being 1 compared to PicSex being 0. I show an example below using the wine dataset:

    library(ordinal)
    DATA = wine
    > head(DATA$temp)
    [1] cold cold cold cold warm warm
    Levels: cold warm
    

    Here cold comes first in Levels, so it is set as the reference in any linear models.First we verify the effect of cold vs warm

    do.call(cbind,tapply(DATA$rating,DATA$temp,table))
    #warm has a higher average rating
    

    Fit the model

    # we fit the a model, temp is fixed effect
    summary(clmm(rating ~ temp + contact+(1|judge), data = DATA))
    Cumulative Link Mixed Model fitted with the Laplace approximation
    
    formula: rating ~ temp + contact + (1 | judge)
    data:    DATA
    
     link  threshold nobs logLik AIC    niter    max.grad cond.H 
     logit flexible  72   -81.57 177.13 332(999) 1.03e-05 2.8e+01
    
    Random effects:
     Groups Name        Variance Std.Dev.
     judge  (Intercept) 1.279    1.131   
    Number of groups:  judge 9 
    
    Coefficients:
               Estimate Std. Error z value Pr(>|z|)    
    tempwarm     3.0630     0.5954   5.145 2.68e-07 ***
    contactyes   1.8349     0.5125   3.580 0.000344 ***
    

    Here we see warm being attached to "temp" and as we know, it has a positive coefficient because the rating is better in warm, compared to cold (the reference).

    So if you set another group as the reference, you will see another name attached, and the coefficient is reversed (-3.. compared to +3.. in previous example)

    # we set warm as reference now
    DATA$temp = relevel(DATA$temp,ref="warm")
    
    summary(clmm(rating ~ temp + contact+(1|judge), data = DATA))
    Cumulative Link Mixed Model fitted with the Laplace approximation
    
    formula: rating ~ temp + contact + (1 | judge)
    data:    DATA
    
     link  threshold nobs logLik AIC    niter    max.grad cond.H 
     logit flexible  72   -81.57 177.13 269(810) 1.14e-04 1.8e+01
    
    Random effects:
     Groups Name        Variance Std.Dev.
     judge  (Intercept) 1.28     1.131   
    Number of groups:  judge 9 
    
    Coefficients:
               Estimate Std. Error z value Pr(>|z|)    
    tempcold    -3.0630     0.5954  -5.145 2.68e-07 ***
    contactyes   1.8349     0.5125   3.580 0.000344 ***
    

    So always check what is the reference before you fit the model