title: Why are most of my residuals negative after logistic regression?
body: <p>I have a dataset whereby the outcome variable is split into two groups (patients that had  a side-effect or patients that did not). However, it's a bit uneven. (Those that did = 128 and those  that did not  = 1614). The results are as follows:</p>
  <pre><code>Deviance Residuals: 
      Min       1Q   Median       3Q      Max  
  -1.3209  -0.4147  -0.3217  -0.2455   2.7529  
  
  Coefficients:
                         Estimate Std. Error z value Pr(&gt;|z|)    
  (Intercept)           -7.888112   0.859847  -9.174  &lt; 2e-16 ***
  age                    0.028529   0.009212   3.097  0.00196 ** 
  bmi                    0.095759   0.015265   6.273 3.53e-10 ***
  surgery_axilla_type11  0.923723   0.524588   1.761  0.07826 .  
  surgery_axilla_type21  1.607389   0.600113   2.678  0.00740 ** 
  surgery_axilla_type31  1.544822   0.573972   2.691  0.00711 ** 
  cvd1                   0.624692   0.290005   2.154  0.03123 *  
  rt_axilla1            -0.816374   0.353953  -2.306  0.02109 * 
  </code></pre>
  <p>The outcome looks fine and the results seem to make sense, i.e. higher age, bmi, surgery to the armpits, cardiovascular disease increase side effects but when I inspect my residuals - most of them are negative. So I am assuming my model is under-predicting. I am not sure how to  conduct any  diagnostic plots because for linear regression the plot function in R will check if the residuals look good or if the QQ-plot looks good. I am not really sure how to move beyond checking the results regarding logistic regression.
  Here is my histogram for the residuals below:</p>
  <p><a href="https://i.stack.imgur.com/Qf750.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Qf750.png" alt="enter image description here" /></a></p>