Plotting

pl.all_weights(model[, views, clip, ...])	Plot the weight matrices.
pl.covariates_factor_scatter(model, factor[, ...])	Plot a factor against one or two covariate dimensions.
pl.factor(model[, factor, show_featurenames, ...])	Plot factor values (y-axis) for each sample (x-axis).
pl.factor_correlation(model[, figsize])	Plot the correlation between factors.
pl.factor_significance(model[, n_factors, ...])	Plot an overview of the factors summarizing the PCGSE results along with the variance explained per factor.
pl.factor_sparsity_histogram(model[, bins, ...])	Plot a histogram of probabilities that factors are non-zero for views with SnS prior.
pl.factors_covariate(model, covariate1[, ...])	Plot every factor against one or two covariates.
pl.factors_scatter(model, x, y[, groups, ...])	Plot two factors against each other and color by covariates.
pl.gp_covariate(model[, ci_opacity, group, ...])	Plot the fitted GP mean for each factor in each group at the data covariate locations.
pl.overview(data[, group_by, missingcolor, ...])	Generate an overview plot of missing data across different views and groups.
pl.smoothness(model[, figsize])	Plot the smoothness of the GP for each factor.
pl.top_weights(model[, n_features, views, ...])	Plot the top weights for a given factor and view.
pl.training_curve(model[, linecolor, ...])	Plot the training curve: -ELBO vs epoch.
pl.variance_explained(model[, group_by, figsize])	Plot the variance explained per factor in each group and view.
pl.weight_sparsity_histogram(model[, bins, ...])	Plot a histogram of probabilities that weights are non-zero for views with SnS prior.
pl.weights(model[, n_features, views, factors, ...])	Plot the weights for a given factor and view.