title: How to only print (adjusted) R-squared of regression model?
body: <p>I am a beginner with R. I have a data set on air pollution. The columns are site, measured concentration and 80 variables (v1-v80) that might influence the concentration. 
  I want to make a model with forward stepwise regression based on R-squared/adj with my own code (so I do not want to use something like step() or regsubset()). The dependent variable is concentration and the variables v1-v80 as independent variables. I wrote the following code for the first step (data set is simplified):</p>
  
  <pre><code>  site concentration         v1         v2         v3
  1    1   -0.84085548  1.7114409 -0.2857736 -1.0803926
  2    2    1.38435934 -0.6029080  0.1381082 -0.1575344
  3    3   -1.25549186 -0.4721664  1.2276303 -1.0717600
  
  for (j in names(df)){
    model &lt;- lm(concentration ~ df[[j]], data = df)
    print(j)
    print(summary(model))
  }
  </code></pre>
  
  <p>This works well, but I am only interested in R-squared and adjusted R-squared. I tried to only have (adjusted) R-squared printed with:</p>
  
  <pre><code>for (j in names(df)){
    model &lt;- lm(concentration ~ df[[j]], data = df)
    print(j)
    print(summary(model$r.squared))
    print(summary(model$adj.r.squared))
  }
  </code></pre>
  
  <p>But then I get as output (this is only a part):</p>
  
  <pre><code>[1] "v1"
  Length  Class   Mode 
       0   NULL   NULL 
  Length  Class   Mode
       0   NULL   NULL
  [1] "v2"
  Length  Class   Mode 
       0   NULL   NULL 
  Length  Class   Mode
       0   NULL   NULL  
  </code></pre>
  
  <p>Etcetera.</p>
  
  <p>How can I get as output only the name of the relevant variable and (adjusted) R-squared for every model that is produced in the for-loop?</p>
  
  <p>Thanks!</p>