asmbPLSDA.fit {asmbPLS}

asmbPLS-DA for block-structured data

Description

Function to fit the adaptive sparse multi-block partial least square discriminant analysis (asmbPLS-DA) model with several explanatory blocks (X_1, ..., X_B) as our predictors to explain the categorical outcome Y.

Usage

asmbPLSDA.fit(
  X.matrix,
  Y.matrix,
  PLS.comp,
  X.dim,
  quantile.comb,
  outcome.type,
  center = TRUE,
  scale = TRUE,
  maxiter = 100
)

Arguments

X.matrix

Predictors matrix. Samples in rows, variables in columns

Y.matrix

Outcome matrix. Samples in rows, this is a matrix with one column (binary) or multiple columns (more than 2 levels, dummy variables).

PLS.comp

Number of PLS components in asmbPLS-DA.

X.dim

A vector containing the number of predictors in each block (ordered).

quantile.comb

A matrix containing quantile combinations used for different PLS components, whose row number equals to the number of PLS components used, column number equals to the number of blocks.

outcome.type

The type of the outcome Y. "binary" for binary outcome, and "multiclass" for categorical outcome with more than 2 levels.

center

A logical value indicating whether weighted mean center should be implemented for X.matrix and Y.matrix. The default is TRUE.

scale

A logical value indicating whether scale should be implemented for X.matrix. The default is TRUE.

maxiter

A integer indicating the maximum number of iteration. The default number is 100.

Value

asmbPLSDA.fit returns a list containing the following components:

X_dim

A vector containing the number of predictors in each block.

X_weight

A list containing the weights of predictors for different blocks in different PLS components.

X_score

A list containing the scores of samples in different blocks in different PLS components.

X_loading

A list containing the loadings of predictors for different blocks in different PLS components.

X_super_weight

A matrix containing the super weights of different blocks for different PLS components.

X_super_score

A matrix containing the super scores of samples for different PLS components.

Y_weight

A matrix containing the weights of outcome for different PLS components.

Y_score

A matrix containing the scores of outcome for different PLS components.

X_col_mean

A matrix containing the weighted mean of each predictor for scaling.

Y_col_mean

The weighted mean of outcome matrix for scaling.

X_col_sd

A matrix containing the standard deviation (sd) of each predictor for scaling. sd for predictors with sd = 0 will be changed to 1.

center

A logical value indicating whether weighted mean center is implemented for X.matrix and Y.matrix.

scale

A logical value indicating whether scale is implemented for X.matrix.

Outcome_type

The type of the outcome Y. "binary" for binary outcome, and "multiclass" for categorical outcome with more than 2 levels.

Y_group

Original Y.matrix.

Examples

## Use the example dataset
data(asmbPLSDA.example)
X.matrix = asmbPLSDA.example$X.matrix
Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary
Y.matrix.multiclass = asmbPLSDA.example$Y.matrix.morethan2levels
X.dim = asmbPLSDA.example$X.dim
PLS.comp = asmbPLSDA.example$PLS.comp
quantile.comb = asmbPLSDA.example$quantile.comb
 
## asmbPLSDA fit for binary outcome
asmbPLSDA.fit.binary <- asmbPLSDA.fit(X.matrix = X.matrix, 
                                      Y.matrix = Y.matrix.binary, 
                                      PLS.comp = PLS.comp, 
                                      X.dim = X.dim, 
                                      quantile.comb = quantile.comb,
                                      outcome.type = "binary")

## asmbPLSDA fit for categorical outcome with more than 2 levels
asmbPLSDA.fit.multiclass <- asmbPLSDA.fit(X.matrix = X.matrix, 
                                          Y.matrix = Y.matrix.multiclass, 
                                          PLS.comp = PLS.comp, 
                                          X.dim = X.dim, 
                                          quantile.comb = quantile.comb,
                                          outcome.type = "multiclass")