title: How does the function multinom from R package nnet compute the multinomial probability weights?
body: <p>I know the theoretical answer to the question of my title, which is discussed <a href="http://en.wikipedia.org/wiki/Multinomial_logistic_regression#As_a_set_of_independent_binary_regressions" rel="nofollow noreferrer">here</a> or in <a href="https://stackoverflow.com/questions/17283595/fitted-values-for-multinom-in-r-coefficients-for-reference-category">this</a> previous question on Stack Overflow. My problem is that, even considering some numerical roundings, the  probability weights I compute using the coefficients fitted in the <code>R</code> function <code>multinom</code> are quite different from the weights directly obtained from the same function (through <code>predict(fit, newdata = dat, "probs")</code>). I tried to numerically compute these weights in <code>Java</code> and <code>R</code>, and in both implementations I obtain the same results, which are in fact different from the values returned from <code>predict</code>.</p>
  
  <p>Do you know how I may discover the implementation of the function <code>predict(..., "probs")</code> for the <code>R</code> function <code>multinom</code>?</p>