title: Probability results from Multinomial Regression nnet package
body: <p>Good afternoon, I have a problem with the output I get when performing a logistic regression with NNET package. I want to predict <code>Category</code> with <code>HS_TR (Return Period)</code> and <code>SLR (Sea Level Rise)</code>. 
  The multinomial model, called <code>fit</code>, has been calculated with the information from the <code>x.sub</code> subset. There are 4 different categories possible 1,2,3 or 4.</p>
  
  <p><code>x.sub</code>:</p>
  
  <pre><code>   POINTID  HS_TR  SLR  Category
         4     10    0.0     3
         4     10    0.6     4
         4     50    0.0     3
         4     50    0.6     4
         4    100    0.0     4
         4    100    0.6     4
  </code></pre>
  
  <p>When I run the model> <code>fit &lt;- multinom(Category ~ HS_TR + SLR, x.sub, maxit=3000)</code> I get the results :</p>
  
  <pre><code>Coefficients:
      (Intercept)       HS_TR         SLR 
      -30.5791517   0.4130478  62.0976951 
  
      Residual Deviance: 0.0001820405 
      AIC: 6.000182
  </code></pre>
  
  <p>Now that I have the multinomial, I want to know the predicted category for a specific scenario (d3) of SLR and HS_TR. I define d3 and apply the prediction and I get reasonable result:</p>
  
  <pre><code>d3&lt;-data.frame("HS_TR"=c(10),"SLR"=c(0))
  prediction &lt;-(predict(fit,d3))
  </code></pre>
  
  <p>I get</p>
  
  <pre><code>&gt; prediction
  [[1]]
  [1] 3 
  Level: 3
  </code></pre>
  
  <p>However, when I calculate the probability of getting the prediction <code>prediction &lt;-(predict(fit,d3, type="probs"))</code>, I get the following:</p>
  
  <pre><code>&gt; prediction
  [[1]]
  1 
  0 
  </code></pre>
  
  <p>Which makes no sense since it says that there is probability 0. Since the model I run gives a prediction of the <code>CATEGORY</code>, I don't understand why then, the probability is 0. Does someone know why I get it? </p>
  
  <p>If someone knows how I could work on the problem so that I can solve it. Thank you in advance.</p>