class_docstr: A basic class for Principal Component Analysis (PCA).
  
  p is the number of dimensions, while N is the number of data points
docstr: clips out all data points that are more than a certain number
  of standard deviations from the mean.
  
  sigs can be either a single value or a length-p sequence that
  specifies the number of standard deviations along each of the
  p dimensions.
docstr_tok: ["clips","out","all","data","points","that","are","more","than","a","certain","number","of","standard","deviations","from","the","mean",".","sigs","can","be","either","a","single","value","or","a","length-p","sequence","that","specifies","the","number","of","standard","deviations","along","each","of","the","p","dimensions","."]
code: def sigclip(self, sigs):
      """
      clips out all data points that are more than a certain number
      of standard deviations from the mean.
  
      sigs can be either a single value or a length-p sequence that
      specifies the number of standard deviations along each of the
      p dimensions.
      """
      if np.isscalar(sigs):
          sigs = sigs * np.ones(self.N.shape[1])
      sigs = sigs * np.std(self.N, axis=1)
      n = self.N.shape[0]
      m = np.all(np.abs(self.N) < sigs, axis=1)
      self.A = self.A[m]
      self.__calc()
      return n - sum(m)
  
file_path: statsmodels/statsmodels/sandbox/pca.py
imports: import numpy
label_desc: Use image node_id 10 for calling the Pca obj's underlying member method code with example usage: obj.sigclip(sigs) and returns: unknown