MFAIRSingleFactor object contains the key information about the fitted single factor MFAI model.

Value

MFAIRSingleFactor class.

Y_missing: Logical. Whether the main data matrix Y is partially observed.
n_obs: Integer. Total number of observed entries in Y.
mu: An vector of length N representing the inferred loading, corresponding to the posterior mean of z in the single factor MFAI model.
a_sq: Numeric. The posterior variance of the loading z. For fully observed Y, all N elements of the loading share the same posterior variance, then a_sq is a single number. For Y with missing data, the elements have different posterior variances, then a_sq is a vector of length N.
nu: An vector of length M representing the inferred factor, corresponding to the posterior mean of w in the single factor MFAI model.
b_sq: Numeric. The posterior variance of the factor w. For fully observed Y, all M elements of the factor share the same posterior variance, then b_sq is a single number. For Y with missing data, the elements have different posterior variances, then b_sq is a vector of length M.
tau: Numeric. Precision parameter this pair of loading/factor.
beta: Numeric. Precision parameter for this loading z.
FX: An vector of length N representing the prior mean of z, corresponding to F(X) in the single factor MFAI model.
tree_0: Numeric. Tree_0 is defined as the mean of mu vector.
tree_list: A list containing multiple decision trees, corresponding to function F(.) in the single factor MFAI model.
project: Character. Name of the project (for record keeping).