Private API reference

generative_model

_mk_Sigmaxy(assemble_Sigmaxy, Sigmaxx, …)	Generate the between-set covariance matrix \(\Sigma_{XY}\) (i.e.
_find_latent_mode_vectors(assemble_Sigmaxy, …)	Find between-set weight vectors.
_find_latent_mode_vectors_qr(Sigmaxx, …)	Finds random latent mode vectors using the QR algorithm.
_find_latent_mode_vectors_opti(Sigmaxx, …)	Find latent mode vectors using an optimization algorithm that maximizes the minimum eigenvalue of the proposed joint covariance matrix.
_find_latent_mode_vectors_pc1(Sigmaxx, …)	Selects the first principal component axes as weight vectors.
_add_lowvariance_subspace_components(U, …)	Add a component from the low-variance subspace to the weight vectors living in the dominant high-variance subspace.
_generate_random_dominant_subspace_rotations(U, …)	Generates random weight vectors in the dominant subspaces (of dimension qx and qy).
_generate_dominant_subspace_rotations_from_opti(U, …)	Generates weight vectors in the dominant subspaces (of dimension qx and qy) using a random entry in a list of predefined rotation matrices.
_assemble_Sigmaxy_pls(Sigmaxx, Sigmayy, U_, …)	Generates the between-set covariance matrix.
_assemble_Sigmaxy_cca(Sigmaxx, Sigmayy, U_, …)	Generates the between-set covariance matrix for CCA.
_Sigmaxy_negative_min_eval(uvrot, …[, …])	Find the negative of the minimum eigenvalue of (the Schur complement of) the joint covariance matrix \(\Sigma\).
_variance_explained_by_latent_modes(Sigmaxx, …)	Calculates the amount of explained variance by the between-set associations and checks if they surpass a threshold.
calc_schur_complement(A, B_or_px[, C, D, kind])	Calculate Schur complement of a matrix.
_check_subspace_dimension(Sigmaxx, qx)	Interpret arguments qx and qy in mk_Sigma_model().

metric

metrics.mk_betweenAssocRelError(ds)	Evaluate the relative error of the between-set association strength.
metrics.mk_betweenAssocRelError_cv(ds, cv_assoc)	Evaluate the relative error of the cross-validated between-set association strength.
metrics.mk_meanBetweenAssocRelError(ds, cv_assoc)	Evaluate the relative error of the between-set association strength as the average of the in-sample and cross-validated value.
metrics.mk_weightError(ds[, suffix])	Evaluate the weight error.
metrics.mk_scoreError(ds)	Evaluate the score error.
metrics.mk_loadingError(ds)	Evaluate the loading error.
metrics.mk_crossloadingError(ds)	Evaluate the cross-loading error.

sample_size

interpolation.calc_n_required(metric, …[, …])	Calculate required sample sizes for a given metric.
interpolation.calc_n_required_all_metrics(ds)	Calculate n_required for 5 commonly used metrics, as well as maximum across metrics.
interpolation.calc_max_n_required(*n_requireds)	Finds the maximum sample size across datasets.
linear_model.do_fit_lm(ds, n_reqs[, …])	Fits a linear model to outcome data.
linear_model.prep_data_for_lm(ds, n_reqs, …)	Prepare outcome data for use with linear model.
linear_model.fit_linear_model(criterion, model)	Fit a linear model to outcome data.
linear_model.get_lm_coefs(model, criterion, …)	Get linear model coefficients.
linear_model._save_linear_model(model[, …])	Save linear model.

data

loaders.load_metaanalysis_outcomes(px, py, n)	Load previously generated outcomes for a specific parameter set.
loaders.load_metaanalysis([data_home, fetch])	Load table of metaanalysis features.
loaders._fetch_synthanad(fname, path)	Retrieve file from remote repository.
loaders._check_data_home(data_home[, subfolder])	Check data_home directory
loaders._check_outcome_data_exists(fname, path)	Check if a given outcome data file exists and possibly retrieve it if not.

util

check_positive_definite(Sigma, noise_level)	Check if a matrix is positive definite.
align_weights(v, vtrue[, copy, return_sign])	Align vectors in rows of v such that they have a positive dot-product with vtrue.
nPerFtr2n(nPerFtr[, py])	Convert a DataArray with elements representing “n_per_ftr” into “n”