mbpls | R Documentation |

Function to perform a multiblock partial least squares (PLS) of several explanatory blocks *(X_1, …, X_k)* defined as an object of class `ktab`

, to explain a dependent dataset $Y$ defined as an object of class `dudi`

mbpls(dudiY, ktabX, scale = TRUE, option = c("uniform", "none"), scannf = TRUE, nf = 2)

`dudiY` |
an object of class |

`ktabX` |
an object of class |

`scale` |
logical value indicating whether the explanatory variables should be standardized |

`option` |
an option for the block weighting. If |

`scannf` |
logical value indicating whether the eigenvalues bar plot should be displayed |

`nf` |
integer indicating the number of kept dimensions |

A list containing the following components is returned:

`call` |
the matching call |

`tabY` |
data frame of dependent variables centered, eventually scaled (if scale=TRUE) and weighted (if option="uniform") |

`tabX` |
data frame of explanatory variables centered, eventually scaled (if scale=TRUE) and weighted (if option="uniform") |

`TL, TC` |
data frame useful to manage graphical outputs |

`nf` |
numeric value indicating the number of kept dimensions |

`lw` |
numeric vector of row weights |

`X.cw` |
numeric vector of column weighs for the explanalatory dataset |

`blo` |
vector of the numbers of variables in each explanatory dataset |

`rank` |
maximum rank of the analysis |

`eig` |
numeric vector containing the eigenvalues |

`lX` |
matrix of the global components associated with the whole explanatory dataset (scores of the individuals) |

`lY` |
matrix of the components associated with the dependent dataset |

`Yc1` |
matrix of the variable loadings associated with the dependent dataset |

`cov2` |
squared covariance between lY and TlX |

`Tc1` |
matrix containing the partial loadings associated with each explanatory dataset (unit norm) |

`TlX` |
matrix containing the partial components associated with each explanatory dataset |

`faX` |
matrix of the regression coefficients of the whole explanatory dataset onto the global components |

`XYcoef` |
list of matrices of the regression coefficients of the whole explanatory dataset onto the dependent dataset |

`bip` |
block importances for a given dimension |

`bipc` |
cumulated block importances for a given number of dimensions |

`vip` |
variable importances for a given dimension |

`vipc` |
cumulated variable importances for a given number of dimensions |

Stéphanie Bougeard (stephanie.bougeard@anses.fr) and Stéphane Dray (stephane.dray@univ-lyon1.fr)

Bougeard, S., Qannari, E.M., Lupo, C. and Hanafi, M. (2011). From multiblock partial least squares to multiblock redundancy analysis. A continuum approach. *Informatica*, 22(1), 11-26

Bougeard, S. and Dray S. (2018) Supervised Multiblock Analysis in R with the ade4 Package. *Journal of Statistical Software*, **86** (1), 1-17. doi: 10.18637/jss.v086.i01

`mbpls`

, `testdim.multiblock`

,
`randboot.multiblock`

data(chickenk) Mortality <- chickenk[[1]] dudiY.chick <- dudi.pca(Mortality, center = TRUE, scale = TRUE, scannf = FALSE) ktabX.chick <- ktab.list.df(chickenk[2:5]) resmbpls.chick <- mbpls(dudiY.chick, ktabX.chick, scale = TRUE, option = "uniform", scannf = FALSE) summary(resmbpls.chick) if(adegraphicsLoaded()) plot(resmbpls.chick)

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