title: trying to use package bootstrap to run a jackknife on my Random Forest model
body: <p>I'm having trouble trying to figure out the following:  I am running Random Forest for classification of habitat use and have GPS data from 17 animals.  My data frame depicts different habitat variables such as aspect and canopy cover at each used animal location and each unused, random location.  Each used location is also identified by the ID number of the animal ( this column is called "lynx").  A column called "usvsa" codes used locations as 1 and unused locations as 0.  Here's the top of my spatial points data frame called sdata3:</p>
  
  <pre><code>lynx usvsa   aspect canopy_cover clearcut_area       cti deciduous dist_draw dist_ridge 
  311    1 252.3302      55.3704             0  7.311823         0   90.0000  484.66483            
  311    1 263.1394      55.1528             0  6.857203         0  324.4996  305.94116            
  311    1 249.6992      72.9272             0  6.612025         0  364.9658  212.13203            
  311    1 194.4459      50.4428             0  6.330615         0  108.1665   67.08204     
  </code></pre>
  
  <p>Ok.  So, I'd like to use Jackknifing to run Random Forest 17 times (since I have 17 individuals), leaving one animal out each run.  The idea is to compare the results of each random forest run to make sure no one animal is having a disproportionately large effect on the model results.  I've been reading about package "bootstrap"  and the jackknife function: <code>jackknife(x, theta, ...)</code> </p>
  
  <p>I get that I need to write a function for theta but I can't figure out how to put it all together so that each run of Random Forest leaves one animal out.  Here is my Random Forest Model: <code>randomForest(y ~ ., data=sdata3, ntree=b, importance=TRUE,norm.votes=TRUE, proximity=TRUE)</code>  I'd like to compare the importance values and oob error of each run.
  Any tips would be appreciated!!</p>