MoMA: MoMA - Modern Multivariate Analysis in R

Description Details Members Methods See Also

During initialization of an SFPCA object, R calls the C++-side function, cpp_multirank_BIC_grid_search, and wraps the results returned. The SFPCA object also records penalty levels and selection schemes of tuning parameters. Several helper methods are provivded to facilitate access to results. Initialization is delegated to moma_sfpca.

The following table lists the supported methods for R6 objects generated by moma_*pca, moma_*cca and moma_*lda family of functions.

	`get_mat_by_index`	`interpolate`	`print`	`plot`	`X_project`	`Y_project`	`left_project`
`moma_*pca`	Yes	Yes	Yes	Yes			Yes
`moma_*cca`	Yes		Yes	Yes	Yes	Yes
`moma_*lda`	Yes		Yes	Yes	Yes

center,scale: The attributes "scaled:center" and "scaled:scale" of function scale. The numeric centering and scalings used (if any) of the data matrix.
grid_result: A 5-D list containing the results evaluated on the parameter grid.
select_scheme_list: A list with elements select_scheme_alpha_u, select_scheme_alpha_v, select_scheme_lambda_u, select_scheme_lambda_v. Each of them is either 0 or 1. 0 stands for grid search and 1 stands for BIC search. Please see the select_scheme argument in the function moma_sfpca.

get_mat_by_index

Arguments

For moma_*pca: alpha_u, alpha_v, lambda_u, lambda_v.

For moma_*cca and moma_*lda: alpha_x, alpha_y, lambda_x, lambda_y.

Indices of the parameters in the paramter grid, which have been specified during initialization.

Functionality

Obtain the right and left penalized sigular vectors located by the set of indices (alpha_u, alpha_v, lambda_u, lambda_v) or (alpha_x, alpha_y, lambda_x, lambda_y). Penalized sigular vectors are packed into matrices U and V.

Value

For moma_*pca, it is a list containing U, V, d, chosen_lambda_u, chosen_lambda_u, chosen_lambda_v, chosen_alpha_u, chosen_alpha_v.

For moma_*lda, it is a list containing X_PC_loadings, Y_group_scores, d, chosen_lambda_x, chosen_lambda_y, chosen_alpha_x, chosen_alpha_y.

For moma_*cca, it is a list containing X_PC_loadings, Y_PC_loadings, d, chosen_lambda_x, chosen_lambda_y, chosen_alpha_x, chosen_alpha_y.

*_project

Arguments

For left_project (available for moma_*pca): newX, alpha_u, alpha_v, lambda_u, lambda_v.

For X_project (available for moma_*cca and moma_*lda)): newX, alpha_x, alpha_y, lambda_x, lambda_y.

For Y_project (available for moma_*cca): newY, alpha_x, alpha_y, lambda_x, lambda_y.

newX or newY is a new raw data matrix (un-centered and un-scaled) of an appropriate number of columns. (alpha_u, alpha_v, lambda_u, lambda_v) or (alpha_x, alpha_y, lambda_x, lambda_y) is the set of indices to locate the penalalized singular vectors used to form the basis of the low dimensional space.

Functionality

Project the new data into the space spaned by the penalized left singular vectors, after scaling and centering as needed.

Value

A list containing the scaled and centered data (as needed) scaled_data and the projected data proj_data.

interpolate

Arguments

alpha_u, alpha_v, lambda_u, lambda_v, exact.

exact is a logical scalar. (alpha_u, alpha_v, lambda_u, lambda_v) is the set of new paramters of interest.

When exact = TRUE, a new solution will be calculated exactly with the parameters set to (alpha_u, alpha_v, lambda_u, lambda_v).

When exact = FALSE, an approximate solution will be given by taking the average of two solutions whose parameters' values are "closest" to the ones specified by the user.

Functionality