Direction of Estimate coefficient in a Log Regression - r

Question

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
P.S.
Thank you here are my head results (I relabeled PicSex to Sex)
> head(Posture)
[1] 1 1 1 1 1 1
Levels: 0 1
> head(Sex)
[1] 0 0 0 0 0 0
Levels: 0 1
So the level order is the same, but on Sex it still added a 1 but on posture not. Still very confused about the directions.

Answer 1

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