moma_sflda: Sparse and functional LDA

In DataSlingers/MoMA: MoMA - Modern Multivariate Analysis in R

Description

moma_sflda creates an SFLDA R6 object and returns it.

moma_slda is a function for performing one-way sparse LDA.

moma_twslda is a function for performing two-way sparse LDA.

moma_flda is a function for performing one-way functional LDA.

moma_twflda is a function for performing two-way functional LDA.

Usage

moma_sflda(X, ..., Y_factor, center = TRUE, scale = FALSE,
  x_sparse = moma_empty(), y_sparse = moma_empty(),
  x_smooth = moma_smoothness(), y_smooth = moma_smoothness(),
  pg_settings = moma_pg_settings(), max_bic_iter = 5, rank = 1)

moma_slda(X, ..., Y_factor, center = TRUE, scale = FALSE,
  x_sparse = moma_empty(), y_sparse = moma_empty(),
  pg_settings = moma_pg_settings(), max_bic_iter = 5, rank = 1)

moma_twslda(X, ..., Y_factor, center = TRUE, scale = FALSE,
  x_sparse = moma_empty(), y_sparse = moma_empty(),
  pg_settings = moma_pg_settings(), max_bic_iter = 5, rank = 1)

moma_flda(X, ..., Y_factor, center = TRUE, scale = FALSE,
  x_smooth = moma_smoothness(), y_smooth = moma_smoothness(),
  pg_settings = moma_pg_settings(), max_bic_iter = 5, rank = 1)

moma_twflda(X, ..., Y_factor, center = TRUE, scale = FALSE,
  x_smooth = moma_smoothness(), y_smooth = moma_smoothness(),
  pg_settings = moma_pg_settings(), max_bic_iter = 5, rank = 1)

Arguments

X	A data matrix, each row representing a sample, and each column a feature.
...	Force users to specify arguments by names.
Y_factor	A factor representing which group a sample belongs to.
center	A logical value indicating whether the variables should be shifted to be zero centered. Defaults to TRUE.
scale	A logical value indicating whether the variables should be scaled to have unit variance. Defaults to FALSE.
x_sparse	An object of class inheriting from "moma_sparsity_type". Most conveniently specified by functions described in moma_sparsity_options. It specifies the type of sparsity-inducing penalty function used in the model. Note that for moma_scca, these two parameters must not be specified at the same time. For moma_fcca and moma_twfcca, they must not be specified.
y_sparse	An object of class inheriting from "moma_sparsity_type". Most conveniently specified by functions described in moma_sparsity_options. It specifies the type of sparsity-inducing penalty function used in the model. Note that for moma_scca, these two parameters must not be specified at the same time. For moma_fcca and moma_twfcca, they must not be specified.
x_smooth	An object of class inheriting from "moma_smoothness_type". Most conveniently specified by functions described in moma_smoothness. It specifies the type of smoothness terms used in the model. Note that for moma_fcca, these two parameters must not be specified at the same time. For moma_scca and moma_twscca, they must not be specified.
y_smooth	An object of class inheriting from "moma_smoothness_type". Most conveniently specified by functions described in moma_smoothness. It specifies the type of smoothness terms used in the model. Note that for moma_fcca, these two parameters must not be specified at the same time. For moma_scca and moma_twscca, they must not be specified.
pg_settings	An object of class inheriting from "moma_pg_settings". Most conviently specified by functions described in moma_pg_settings. It specifies the type of algorithm used to solve the problem, acceptable level of precision, and the maximum number of iterations allowed.
max_bic_iter	A positive integer. Defaults to 5. The maximum number of iterations allowed in nested greedy BIC selection scheme.
rank	A positive integer. Defaults to 1. The maximal rank, i.e., maximal number of principal components to be used.

Functions

• moma_slda: a function for performing one-way sparse LDA

• moma_twslda: a function for performing two-way sparse LDA

• moma_flda: a function for performing one-way functional LDA

• moma_twflda: a function for performing two-way functional LDA

DataSlingers/MoMA documentation built on Oct. 30, 2019, 5:55 a.m.