asmbPLS.fit {asmbPLS} | R Documentation |

## 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 |

`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)
```

*asmbPLS*version 1.0.0 Index]