How to analyze panel data in R using logistic regression (glmmML)?
<p>Currently, I am conducting research on the probability of a CEO leaving the company (binary variable=&quot;1&quot; if left). My data is unbalanced panel data for 50 companies with 51 individuals in it for the period 2013-2015.</p>
<p>I was trying to run two regression models (fixed and random effects) using the <code>glmmML</code> package. However, I get the following warning:</p>
<pre><code>pdata &lt;- plm.data(ceodata, index=c(&quot;id&quot;,&quot;year&quot;))
fixed &lt;- glmmboot(left ~ age+tenure+boardappr+ROE, 
   family=binomial(link=&quot;logit&quot;), data=pdata, cluster=id)
    Warning messages: 
 1: In model.response(mf, &quot;numeric&quot;) : using type = &quot;numeric&quot; with a factor response will be ignored
 2: In glmmbootFit(X, Y, weights, start.coef, cluster, offset, family,  :[glmmboot:] Information non-positive definite. No variance!
 3: In Ops.factor(Y, res$fitted) : ‘/’ not meaningful for factors
 4: In Ops.factor(Y, log(ifelse(Y == 0, 1, Y/res$fitted))) ‘*’ not meaningful for factors
 5: In Ops.factor(1, Y) : ‘-’ not meaningful for factors
 6: In Ops.factor(1, Y) : ‘-’ not meaningful for factors
</code></pre>
<p>When I change the set of variables I get the same warning and meaningless regression results. I was wondering if I am doing something wrong or there is an issue with the data I use?
Perhaps, someone can share the code to run fixed and random effect models as well as Hausman test for logistic regression?</p>
<p>P.S. The data I use look like this:</p>
<p><img src="https://i.stack.imgur.com/PwgUU.png" alt="Here is the overview of data I use" /></p>