Generates a plot of the posterior mean of the latent coordinates (x) from a DPCD model fit, aligned to a specified target matrix using a Procrustes transformation.
plot_objects(mcmc_samples, target_matrix, show_clusters = TRUE, ...)A scatter plot (for 2-dimensional latent space) or pairs plot (for higher dimensions) of the object configuration.
An object of class mcmc or mcmc.list containing posterior samples from a DPCD model fit using run_dpcd(). Variable x must be included in the output parameters.
A matrix used as the target for aligning the posterior latent coordinates (x) via a Procrustes transformation.
Logical argument indicating whether to colour points by their cluster membership. If TRUE, then z must be included in mcmc_samples.
Additional arguments to be passed to plot() (2 dimensions) or pairs() (higher dimensions).
Since the latent coordinates are non-identifiable due to invariance of Euclidean distances to rotation, reflection, and translation, this function first aligns the posterior samples of x to a specified target matrix using a Procrustes transformation. Then, it computes the posterior mean of the aligned latent coordinates and generates a plot. If show_clusters is set to TRUE, points are coloured according to their cluster memberships, which is estimated through maximizing the posterior expected adjusted Rand index (Fritsch and Ickstadt, 2009).
Fritsch, Arno & Ickstadt, Katja. (2009). An Improved Criterion for Clustering Based on the Posterior Similarity Matrix. Bayesian Analysis. 4. doi:10.1214/09-BA414.
target_matrix <- cmdscale(dis_mat_example, k = 2)
plot_objects(mcmc_example, target_matrix, show_clusters = TRUE)
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