docstr: Returns the difference between the metric and the Lowess estimate of metric from frequency
  
  :param metiric: Optional[np.array], metric to analyze, defaults to DP
  
  :param freq: Optional[np.array], Word frequencies
  :return: np.array, frequency-adjusted metric
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code: def get_adjusted_metric_df(self, metric=None, freq=None):
      """
      Returns the difference between the metric and the Lowess estimate of metric from frequency
  
      :param metiric: Optional[np.array], metric to analyze, defaults to DP
  
      :param freq: Optional[np.array], Word frequencies
      :return: np.array, frequency-adjusted metric
      """
      if metric is None:
          metric = self.dp()
      elif metric == "DA":
          metric = self.da()
      else:
          metric = self.get_df()[metric]
  
      if freq is None:
          freq = self.get_frequency()
      freq_est_metric = self.__fit_predict(freq, metric)
      adjusted_metric = metric - freq_est_metric
      return pd.DataFrame(
          {
              "Frequency": freq,
              "Metric": metric,
              "Estimate": freq_est_metric,
              "Residual": adjusted_metric,
          },
          index=self.get_names(),
      )
  
imports: from typing import Optional, Callable, List
  import numpy
  import pandas
  from scattertext.termranking import AbsoluteFrequencyRanker
  from scipy.sparse import csc_matrix, csr_matrix
  from sklearn.preprocessing import StandardScaler
  from scattertext.smoothing.lowess import Lowess
  from scattertext.termranking.TermRanker import TermRanker
  from scattertext.TermDocMatrix import TermDocMatrix
label_desc: Use image node_id 15 for calling the Dispersion obj's underlying member method code with example usage: obj.get_adjusted_metric_df(metric, freq) and returns: pd