title: Stargazer drops first regressor if first model is univariate plm, but not if first model is multivariate plm
body: <p>I use the <code>stargazer</code> package to make LaTeX tables of panel models (<code>plm</code> package). If the first <code>plm</code> model is univariate, then <code>stargazer</code> drops that model's regressors, but still displays the model.</p>
  
  <p>If I swap the model order so that the first model is multivariate then all models display. I don't have this problem with <code>lm</code> models. Is there a work around? Does it matter that <code>lm</code> includes an intercept while <code>plm</code> does not?</p>
  
  <p>Thanks! Here's an example.</p>
  
  <pre><code>dat &lt;- data.frame(y=runif(1000),
                    x1=runif(1000),
                    x2=runif(1000),
                    id=rep(1:50, each=20),
                    time=rep(1:20, times=50)
                    )
  pdat &lt;- pdata.frame(dat, index=c("id", "time"))
  plms &lt;- list()
  plms[[1]] &lt;- plm(y ~ x1, data=pdat)
  plms[[2]] &lt;- plm(y ~ x1 + x2, data=pdat)
  plms[[3]] &lt;- plm(y ~ x2, data=pdat)
  
  # drops first regressor if first model is univariate
  stargazer(plms[1:3], type="text")
  
  # but not if first model is multivariate
  stargazer(plms[c(2, 1, 3)], type="text")
  </code></pre>
  
  <p>This yields the following.</p>
  
  <pre><code>&gt; # drops first regressor if first model is univariate
  &gt; stargazer(plms[1:3], type="text")
  
  ========================================================================
                                   Dependent variable:                    
               -----------------------------------------------------------
                                            y                             
                       (1)                 (2)                 (3)        
  ------------------------------------------------------------------------
  x1                                      0.004                           
                                         (0.032)                          
  
  x2                                     -0.008              -0.008       
                                         (0.032)             (0.032)      
  
  ------------------------------------------------------------------------
  Observations        1,000               1,000               1,000       
  R2                 0.00002             0.0001              0.0001       
  Adjusted R2        0.00002             0.0001              0.0001       
  F Statistic  0.016 (df = 1; 949) 0.038 (df = 2; 948) 0.059 (df = 1; 949)
  ========================================================================
  Note:                                        *p&lt;0.1; **p&lt;0.05; ***p&lt;0.01
  
  &gt; # but not if first model is multivariate
  &gt; stargazer(plms[c(2, 1, 3)], type="text")
  
  ========================================================================
                                   Dependent variable:                    
               -----------------------------------------------------------
                                            y                             
                       (1)                 (2)                 (3)        
  ------------------------------------------------------------------------
  x1                  0.004               0.004                           
                     (0.032)             (0.032)                          
  
  x2                 -0.008                                  -0.008       
                     (0.032)                                 (0.032)      
  
  ------------------------------------------------------------------------
  Observations        1,000               1,000               1,000       
  R2                 0.0001              0.00002             0.0001       
  Adjusted R2        0.0001              0.00002             0.0001       
  F Statistic  0.038 (df = 2; 948) 0.016 (df = 1; 949) 0.059 (df = 1; 949)
  ========================================================================
  Note:                                        *p&lt;0.1; **p&lt;0.05; ***p&lt;0.01
  </code></pre>