asmbPLSDA.fit {asmbPLS} | R Documentation |

## 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. " |

`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. " |

`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")
```

*asmbPLS*version 1.0.0 Index]