asmbPLS.fit {asmbPLS}

asmbPLS for block-structured data

Description

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

Usage

asmbPLS.fit(
  X.matrix,
  Y.matrix,
  PLS.comp,
  X.dim,
  quantile.comb,
  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 (continuous variable). The outcome could be imputed survival time. For survival time with right-censored survival time and event indicator, the right censored time could be imputed by meanimp.

PLS.comp

Number of PLS components in asmbPLS.

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.

center

A logical value indicating whether 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 and Y.matrix. The default is TRUE.

maxiter

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

Value

asmbPLS.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 mean of each predictor for scaling.

Y_col_mean

The mean of outcome matrix for scaling.

X_col_sd

A matrix containing the standard deviation of each predictor for scaling. Predictor with sd = 0 will be set to 1.

Y_col_sd

The standard deviation of outcome matrix for scaling.

center

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

scale

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

Examples

## Use the example dataset
data(asmbPLS.example)
X.matrix = asmbPLS.example$X.matrix
Y.matrix = asmbPLS.example$Y.matrix
PLS.comp = asmbPLS.example$PLS.comp
X.dim = asmbPLS.example$X.dim
quantile.comb = asmbPLS.example$quantile.comb
 
## asmbPLS fit
asmbPLS.results <- asmbPLS.fit(X.matrix = X.matrix, 
                               Y.matrix = Y.matrix, 
                               PLS.comp = PLS.comp, 
                               X.dim = X.dim, 
                               quantile.comb = quantile.comb)