title: UseMethod("predict") : no applicable method for 'predict' applied to an object of class "train"
body: <p>I have a model (<code>fit</code>), based on historic information until last month. Now I would like to predict using my model for the current month. When I try to invoke the following code:</p>
  
  <pre><code>predicted &lt;- predict(fit, testData[-$Readmit])
  </code></pre>
  
  <p>I get the following error:</p>
  
  <pre><code>Error in UseMethod("predict") : no applicable method for 'predict'
      applied to an object of class "train"
  </code></pre>
  
  <p><strong>Notes:</strong></p>
  
  <ol>
  <li>The fit model was created via: <code>train</code> function from <a href="http://topepo.github.io/caret/training.html" rel="noreferrer">caret</a> package, using random forest algorithm</li>
  <li><p>The <code>predict</code> is a generic function that will invoke the specific predict function based on the first input argument. In my case it will be: </p>
  
  <p><code>&gt;fit$modelInfo$label</code></p>
  
  <p><code>[1] "Random Forest"</code></p></li>
  </ol>
  
  <p>Therefore the predict method invoked will be: <a href="http://www.inside-r.org/packages/cran/randomforest/docs/predict.randomForest" rel="noreferrer">predict.randomForest</a>. See [caret documentation][3] for more info.</p>
  
  <p>Here the summary source code for generating the model and invoking it: </p>
  
  <pre><code># Script-1: create a model:
  fit &lt;- train(testData[-$Readmit], testData$Readmit)
  saveRDS(fit, modelFileName) # save the fit object into a file
  
  # Script-2: predict
  fit &lt;- readRDS(modelFileName) # Load the model (generated previously)
  predicted &lt;- predict(fit, testData[-$Readmit])
  </code></pre>
  
  <p><strong>Note</strong>: The execution time for generating the model is about 3 hours, that is why I save the object for reusing after that.</p>
  
  <p>The data set from the training model as the following structure:</p>
  
  <pre><code>&gt; str(fit$trainingData)
  'data.frame':   29955 obs. of  27 variables:
  $ Acuity                : Factor w/ 3 levels "Elective  ","Emergency ",..: 2 2 2 1 1 2 2 2 1 1 ...
  $ AgeGroup              : Factor w/ 10 levels "100-105","65-70",..: 8 6 9 9 5 4 9 2 3 2 ...
  $ IsPriority            : int  0 0 0 0 0 0 0 0 0 0 ...
  $ QNXTReferToId         : int  115 1703712 115 3690 1948 115 109 512 481 1785596 ...
  $ QNXTReferFromId       : int  1740397 1724801 1711465 1704170 1714272 1731911 1535 1712758 1740614 1760252 ...
  $ iscasemanagement      : Factor w/ 2 levels "N","Y": 2 1 1 2 2 1 2 1 2 2 ...
  $ iseligible            : Factor w/ 2 levels "N","Y": 2 2 2 2 2 2 2 2 2 2 ...
  $ referralservicecode   : Factor w/ 11 levels "12345","278",..: 1 1 1 9 9 1 1 6 9 9 ...
  $ IsHighlight           : Factor w/ 2 levels "N","Y": 1 1 1 1 1 1 1 1 1 1 ...
  $ admittingdiagnosiscode: num  439 786 785 786 428 ...
  $ dischargediagnosiscode: num  439 0 296 786 428 ...
  $ RealLengthOfStay      : int  3 1 6 1 2 3 3 7 3 2 ...
  $ QNXTPCPId             : int  1740397 1724801 1711465 1704170 1714272 1731911 1535 1712758 1740614 1760252 ...
  $ QNXTProgramId         : Factor w/ 3 levels "QMXHPQ0839     ",..: 1 1 1 1 1 1 1 1 1 1 ...
  $ physicalzipcode       : int  33054 33712 33010 33809 33010 33013 33142 33030 33161 33055 ...
  $ gender                : Factor w/ 2 levels "F","M": 1 1 1 1 2 1 1 2 2 1 ...
  $ ethnicitycode         : Factor w/ 4 levels "ETHN0001       ",..: 4 4 4 4 4 4 4 4 4 4 ...
  $ dx1                   : num  439 786 296 786 428 ...
  $ dx2                   : num  439 292 785 786 428 ...
  $ dx3                   : num  402 0 250 0 0 ...
  $ svc1                  : int  0 120 120 762 762 120 120 120 762 762 ...
  $ svc2                  : int  120 0 0 0 0 0 0 0 0 0 ...
  $ svc3                  : int  0 0 0 0 0 0 0 0 0 0 ...
  $ Disposition           : Factor w/ 28 levels "0","APPEAL &amp; GRIEVANCE REVIEW                                   ",..: 11 11 16 11 11 11 11 11 11 11 ...
  $ AvgIncome             : Factor w/ 10 levels "-1","&gt;100k","0-25k",..: 3 6 3 8 3 4 3 5 4 4 ...
  $ CaseManagerNameID     : int  124 1 1 19 20 1 16 1 43 20 ...
  $ .outcome              : Factor w/ 2 levels "NO","YES": 1 2 2 1 1 1 2 2 1 1    ...
  </code></pre>
  
  <p>now the <code>testData</code> will have the following structure:</p>
  
  <pre><code>&gt; str(testData[-$Readmit])
  'data.frame':   610 obs. of  26 variables:
  $ Acuity                : Factor w/ 4 levels "0","Elective  ",..: 3 2 4 2 2 2 4 3 3 3 ...
  $ AgeGroup              : Factor w/ 9 levels "100-105","65-70",..: 4 3 5 4 2 9 4 2 4 6 ...
  $ IsPriority            : int  0 0 0 0 0 0 1 1 1 1 ...
  $ QNXTReferToId         : int  2140 482 1703785 1941 114 1714905 1703785 98 109 109 ...
  $ QNXTReferFromId       : int  1791383 1729375 1718532 1746336 1718267 1718267 1718532 98 109 109 ...
  $ iscasemanagement      : Factor w/ 2 levels "N","Y": 2 2 2 2 2 2 1 2 2 1 ...
  $ iseligible            : Factor w/ 2 levels "N","Y": 2 2 2 2 2 2 2 2 2 2 ...
  $ referralservicecode   : Factor w/ 7 levels "12345","IPMAT          ",..: 5 1 1 1 1 1 1 5 1 5 ...
  $ IsHighlight           : Factor w/ 2 levels "N","Y": 1 1 1 1 1 1 1 1 1 1 ...
  $ admittingdiagnosiscode: num  11440 11317 11420 11317 1361 ...
  $ dischargediagnosiscode: num  11440 11317 11420 11317 1361 ...
  $ RealLengthOfStay      : int  1 2 4 3 1 1 16 1 1 3 ...
  $ QNXTPCPId             : int  3212 1713678 1738430 1713671 1720569 1791640 1725962 1148 1703290 1705009 ...
  $ QNXTProgramId         : Factor w/ 2 levels "QMXHPQ0839     ",..: 1 1 1 1 1 1 1 1 1 1 ...
  $ physicalzipcode       : int  34744 33175 33844 33178 33010 33010 33897 33126 33127 33125 ...
  $ gender                : Factor w/ 2 levels "F","M": 2 1 2 1 2 2 2 1 1 2 ...
  $ ethnicitycode         : Factor w/ 1 level "No Ethnicity   ": 1 1 1 1 1 1 1 1 1 1 ...
  $ dx1                   : num  11440 11317 11420 11317 1361 ...
  $ dx2                   : num  11440 11317 11420 11317 1361 ...
  $ dx3                   : num  0 1465 0 11326 0 ...
  $ svc1                  : int  52648 27447 50040 27447 55866 55866 51595 0 99221 300616 ...
  $ svc2                  : int  76872 120 50391 120 120 38571 120 762 120 0 ...
  $ svc3                  : int  762 0 120 0 0 51999 0 0 0 762 ...
  $ Disposition           : Factor w/ 14 levels "0","DENIED- Not Medically Necessary                             ",..: 3 5 3 4 3 3 5 3 3 5 ...
  $ AvgIncome             : Factor w/ 10 levels "-1","&gt;100k","0-25k",..: 6 7 5 9 3 3 6 4 3 4 ...
  $ CaseManagerNameID     : int  1 2 3 4 5 6 7 8 9 7 ...
  </code></pre>
  
  <p>The variable structure is the same, just that some factor variables has different levels because some variable has new values. For example: <code>Acuity</code> in the model has 3-levels and in the testing data 4-levels.</p>
  
  <p>I don't have from upfront a way to know all possible level for all variables.</p>
  
  <p>Any advice, please...</p>
  
  <p>Thanks in advance,</p>
  
  <p>David</p>