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  • pflm.fpca
    • pflm.fpca.FpcaModelParams
    • pflm.fpca.FunctionalDataGenerator
    • pflm.fpca.FunctionalPCA
    • pflm.fpca.FunctionalPCAMuCovParams
    • pflm.fpca.FunctionalPCAUserDefinedParams
    • pflm.fpca.SmoothedModelResult
  • pflm.fpca.utils
    • pflm.fpca.utils.estimate_rho
    • pflm.fpca.utils.get_covariance_matrix
    • pflm.fpca.utils.get_eigen_analysis_results
    • pflm.fpca.utils.get_eigenvalue_fit
    • pflm.fpca.utils.get_fpca_ce_score
    • pflm.fpca.utils.get_fpca_in_score
    • pflm.fpca.utils.get_fpca_phi
    • pflm.fpca.utils.get_measurement_error_variance
    • pflm.fpca.utils.get_raw_cov
    • pflm.fpca.utils.rotate_polyfit2d
    • pflm.fpca.utils.select_num_pcs_fve
    • pflm.fpca.utils.select_num_pcs_ic
  • pflm.smooth
    • pflm.smooth.KernelType
    • pflm.smooth.Polyfit1DModel
    • pflm.smooth.Polyfit2DModel
  • pflm.interp
    • pflm.interp.interp1d
    • pflm.interp.interp2d
  • pflm.utils
    • pflm.utils.flatten_and_sort_data_matrices
    • pflm.utils.trapz
    • pflm.utils.FlattenFunctionalData
  • API reference
  • pflm.fpca.utils
  • pflm.fpca.utils.select_num_pcs_ic

pflm.fpca.utils.select_num_pcs_ic#

select_num_pcs_ic(criterion: Literal['AIC', 'BIC'], y: list[ndarray], t: list[ndarray], obs_grid: ndarray, reg_grid: ndarray, reg_mu: ndarray, mu_obs: ndarray, eig_lambda: ndarray, eig_vector: ndarray, max_components: int = 20, measurement_error_variance: float = 0.0, rho: float | None = None) → tuple[ndarray, int][source][source]#

Select number of PCs via AIC/BIC with fdapace-style early stopping.

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  • select_num_pcs_ic()
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