class_docstr: Factor results class
  
  For result summary, scree/loading plots and factor rotations
  
  Parameters
  ----------
  factor : Factor
      Fitted Factor class
  
  Attributes
  ----------
  uniqueness : ndarray
      The uniqueness (variance of uncorrelated errors unique to
      each variable)
  communality : ndarray
      1 - uniqueness
  loadings : ndarray
      Each column is the loading vector for one factor
  loadings_no_rot : ndarray
      Unrotated loadings, not available under maximum likelihood
      analysis.
  eigenvals : ndarray
      The eigenvalues for a factor analysis obtained using
      principal components; not available under ML estimation.
  n_comp : int
      Number of components (factors)
  nbs : int
      Number of observations
  fa_method : str
      The method used to obtain the decomposition, either 'pa' for
      'principal axes' or 'ml' for maximum likelihood.
  df : int
      Degrees of freedom of the factor model.
  
  Notes
  -----
  Under ML estimation, the default rotation (used for `loadings`) is
  condition IC3 of Bai and Li (2012).  Under this rotation, the
  factor scores are iid and standardized.  If `G` is the canonical
  loadings and `U` is the vector of uniquenesses, then the
  covariance matrix implied by the factor analysis is `GG' +
  diag(U)`.
  
  Status: experimental, Some refactoring will be necessary when new
      features are added.
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docstr: The standard errors of the loadings.
  
  Standard errors are only available if the model was fit using
  maximum likelihood.  If `endog` is not provided, `nobs` must be
  provided to obtain standard errors.
  
  These are asymptotic standard errors.  See Bai and Li (2012)
  for conditions under which the standard errors are valid.
  
  The standard errors are only applicable to the original,
  unrotated maximum likelihood solution.
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code: def load_stderr(self):
      """
      The standard errors of the loadings.
  
      Standard errors are only available if the model was fit using
      maximum likelihood.  If `endog` is not provided, `nobs` must be
      provided to obtain standard errors.
  
      These are asymptotic standard errors.  See Bai and Li (2012)
      for conditions under which the standard errors are valid.
  
      The standard errors are only applicable to the original,
      unrotated maximum likelihood solution.
      """
  
      if self.fa_method.lower() != "ml":
          msg = "Standard errors only available under ML estimation"
          raise ValueError(msg)
  
      if self.nobs is None:
          msg = "nobs is required to obtain standard errors."
          raise ValueError(msg)
  
      v = np.outer(self.uniqueness, np.ones(self.loadings.shape[1]))
      return np.sqrt(v / self.nobs)
  
imports: import warnings
  import numpy
  from numpy.linalg import eigh, inv, norm, matrix_rank
  import pandas
  from scipy.optimize import minimize
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.base.model import Model
  from statsmodels.iolib import summary2
  from statsmodels.graphics.utils import _import_mpl
  from .factor_rotation import rotate_factors, promax
label_desc: Use image node_id 13 for calling the FactorResults obj's underlying member method code with example usage: obj.load_stderr() and returns: np