title: Removing multivariate outliers after using robust Mahalanobis distance
body: <p>I have a data set of 167 observations of 3 variables. I am trying to assess and remove multivariate outliers using the robust Mahalanobis distance. To do this I have been playing around with the <code>Moutlier</code> function from the <code>chemometrics</code> package.</p>
  
  <p>I am pretty new to R, so I have some questions: </p>
  
  <p>Here is my code: </p>
  
  <pre><code>data&lt;-isotopes  
    x=data[,c(3:5)] #columns 3-5 contain the data I need to assess  
    require(robustbase)  
    res &lt;- Moutlier(data[,c(3:5)] ,quantile=0.975,plot=TRUE)  
    res  
    which(res$rd&gt;res$cutoff) 
  </code></pre>
  
  <p>1) Has anyone used this code before, have I done this correctly?  </p>
  
  <p>2) Do I need to standardize the data before doing this function, or does it not matter?  </p>
  
  <p>3) After using the <code>which</code> function, the result was:    </p>
  
  <pre><code># [1]  13  28  29  47  79  84  89  91 104 128 153 154 
  </code></pre>
  
  <p>Does this mean that these numbers correspond to observation #13, #28, #29, etc?<br>
  So those would be the outliers that I want to remove, for all 3 variables? </p>
  
  <p>Thank you very much to anyone who can help...  </p>