<p>Thanks for clarifying a bit. I think what you are expecting is something similar to the output of an ANOVA? But this will not be possible with your data, as you have two random effects specified.</p>
<p>As you are running a logistic regression, you should read up a bit on how to interpret them. (I'm just putting this here because you said you were new to this)</p>
<p><a href="https://stats.idre.ucla.edu/stata/output/logistic-regression-analysis/" rel="nofollow noreferrer">https://stats.idre.ucla.edu/stata/output/logistic-regression-analysis/</a></p>
<p>Now, if you want to test the contribution of one of your factors to to the model, you have to create nested models, and compare them with a likelihood-ratio test using the <code>anova()</code> function in R.</p>
<p>For example, let's say that you had the same model you had above, but without any interactions specified:</p>
<pre><code>m1 &lt;- glmer(ACC~Group+M_O+Lblock+ (1| Subject) + (1| hand),data = learndata_long3,family=&quot;binomial&quot;)
</code></pre>
<p>And then one without the Group predictor:</p>
<pre><code>m2 &lt;- glmer(ACC~M_O+Lblock+ (1| Subject) + (1| hand),data = learndata_long3,family=&quot;binomial&quot;)
</code></pre>
<p>Then we compare whether having the <code>Group</code> predictor significantly improved the model:</p>
<pre><code>anova(m1,m2)
</code></pre>
<p>This will give your a p-value telling you if the addition of <code>Group</code> significantly improves model fit.</p>
<p>If this seems all like a lot, which it is if you're not familiar with model comparison, I'd recommend looking at this tutorial from Bodo Winter. It's directed at people who are new mixed-models, and want a conceptual foundation of what is going on. I don't know what field you are in, but I think the examples are pretty accessible to everyone.</p>
<p><a href="https://arxiv.org/abs/1308.5499" rel="nofollow noreferrer">https://arxiv.org/abs/1308.5499</a></p>
<p>Please let me know if you need any other clarifications or have any questions during the tutorial.</p>
glmer extracts coefficient only for main (predictor) factor, not contrast
<p>here is the GLMER model</p>
<pre><code>model &lt;- glmer(ACC~Group*M_O*Lblock+ (1| Subject) + (1| hand),data = learndata_long3,family=&quot;binomial&quot;) 
</code></pre>
<p>while the 'Lblock' factor has 9 levels, others have 2 levels.</p>
<p>The results generate like this:</p>
<pre><code>     summary(model)$coefficients

                        Estimate Std. Error     z value      Pr(&gt;|z|)
(Intercept)          0.437931021 0.16334362  2.68104155  7.339340e-03
Group1              -0.032138148 0.14961572 -0.21480463  8.299196e-01
M_O1                 0.135726477 0.04115871  3.29763642  9.750230e-04
Lblock1              0.301264476 0.08343952  3.61057288  3.055214e-04
Lblock2              0.623913565 0.08247767  7.56463576  3.889529e-14
Lblock3              1.022046512 0.08235930 12.40960689  2.317880e-35
Lblock4              1.399407518 0.08337615 16.78426631  3.181367e-63
Lblock5              1.741198402 0.08541505 20.38514752  2.265326e-92
Lblock6              2.065315516 0.08843600 23.35378765 1.261292e-120
Lblock7              2.268393650 0.09075950 24.99345703 7.201546e-138
Lblock8              2.637079325 0.09707420 27.16560426 1.656429e-162
</code></pre>
<p>ALL I want is extract each factor, like&quot;</p>
<p>Estimate : Group / M_O / Lblock</p>
<p>how can I do? just sum up and then mean the block? or ?</p>
<p>Very new to these fields, thanks for your help</p>