title: R - extract t-values from a list of multiple lms
body: <p>I have a dataframe a vector with a dependent variable. For example like this:</p>
  
  <pre><code>a &lt;- rnorm(100,1,1)
  b &lt;- rnorm(100,2,2)
  c &lt;- rnorm(100,3,3)
  x &lt;- rbind(a,b,c)
  
  y &lt;- rnorm(100,5,10)
  </code></pre>
  
  <p>Now I need to run a linear regression for every row. I did it like this:</p>
  
  <pre><code>lm.list &lt;- apply(x,1, function(x) lm(x ~ y))
  </code></pre>
  
  <p>I managed to extract the coefficients by adding "$coefficients" like this:</p>
  
  <pre><code>lm.list &lt;- apply(x,1, function(x) lm(x ~ y)$coefficients)
  </code></pre>
  
  <p>I then get the coefficients for each row. Now I would like to know the significance of those coefficients. So I need to extract either the t-values, the standard errors or the p-values. I dont know how to do it and i dont want to run the lm-function for each row 1 by 1 and copy paste the t-values. Is there a way to do this? </p>
  
  <p>And as a bonus question: is it possible to use Newey-West standard errors for the coeffient calculation for each row? I know how it works for single lms but could not find a way to use it with the apply function for many rows.</p>
  
  <p>Thanks in advance!</p>