StpcaModel-class: Structured PCA Model
In JimSkinner/spca: Structured PCA

Structured PCA Model

X: The matrix of data
n: The number of samples
k: Latent dimensionality
d: Simensionality of observations
beta: Covariance function hyperparameters
K: covariance matrix generated from the covariance function and hyperparameters
KD: Partial derivatived of K w.r.t each of the hyperparameters
locs: Locations (t_i)
covFn: Covariance function
covFnD: Function giving partial derivatives of covariance function w.r.t hyperparameters
muHat: MAP estimate of mu
WHat: MAP estimate of W
sigSqHat: MAP estimate of sigSq
Vmean: The posterior mean of each latent variable
VVar: The posterior variance around each latent variable
logEvidence: log of the approximate evidence
logPosteriors: sequence of posteriors obtained in fitting. Un-normalised if sparse
logEvidenceD: Partial derivatives of evidence w.r.t hyperparameters
H: The precision matrix for the gaussian approximation to the posterior around each w
maxim: the maximisation object returned in hyperparameter tuning
thetaConv: Whether theta has been optimised to convergence (if false, beta has probably converged, not theta)
theta: betaHist history of betas in optimisation
convergence: History of logEvidence and logPosteror; used to assess convergence
sparse: whether this is a sparse stpca model
b: The sparsity hyperparameter in a sparse model.

crossvalidate(nFolds = 3, nThreads = 1): Perform k-fold cross-validation (possibly multithreaded) to determine the held out log likelihood of examples. This could be used as an alternative to computing the approximate evidence in model selection.
decode(Vnew): Decode points in the latent space into observations
encode(Xnew): Encode a matrix of observations into latent distributions.
set_b(bNew): Setter for 'b'
set_beta(betaNew): Sets beta to a new value, recomputes K, KD and H.
set_locs(locsNew): Sets the locations, and recomputes K. Be careful with this method. Does not update theta
set_sparse(spNew, b): Setter for 'sparse'
set_X(Xnew): Sets X, updates mu. Does not opdate anything else so it is recommended to call update() or something. Be careful with this method.
simulate(n = 1, Wknown = TRUE): Simulates new synthetic data from a fitted model.
update(tune.maxit = 10, tune.tol = 1e-05, EM.maxit = 500, EM.bftol = 1e-05, ...): The major method for performing inference. This iterates between updating theta (using update_theta) and updating beta (using update_beta).
update_beta(...): Optimises the approximate evidence with respect to the hyperparameters.
update_theta(maxit = 500, bftol = 1e-05): Finds the MAP theta using EM.