# Forward selection with multivariate Y using a parametric model or by permutation under reducel model

### Description

Performs a forward selection by permutation of residuals under reduced model. Y can be multivariate.

### Usage

1 2 3 4 | ```
forward.sel(Y, X, K = nrow(X) - 1, R2thresh = 0.99, adjR2thresh = 0.99,nperm = 999,
R2more = 0.001, alpha = 0.05, Xscale = TRUE, Ycenter = TRUE, Yscale
= FALSE)
forward.sel.par(Y, X, alpha = 0.05, K = nrow(X)-1, R2thresh = 0.99, R2more = 0.001, adjR2thresh = 0.99, Yscale = FALSE, verbose=TRUE)
``` |

### Arguments

`Y` |
A matrix of n lines and m columns that contains (numeric) response variables. |

`X` |
A matrix of n lines and p columns that contains (numeric) explanatory variables. |

`K` |
This number is the number of variables to be selected in the forward selection. The default setting is one minus the number of row.(See details for more information) |

`R2thresh` |
The number given here is a R2 parameter. If the forward selection has a selection of variable which represent the number presented in the parameter or higher, after the introduction of a variable, the forward selection will stop. The setting of this parameter varies from 0.01 to 1. (See details for more information) |

`adjR2thresh` |
The number given here is a adjusted R2 parameter. If the forward selection has a selection of variable which represent the number presented in the parameter or higher, after the introduction of a variable, the forward selection will stop. The setting of this parameter varies from 0.01 to 1. (See details for more information) |

`nperm` |
The number of permutation to be done on the forward selection. the default setting is 999 permutation. |

`R2more` |
The number given here is a R2 parameter. If the forward selection gets to a point where the R2 given by a variable is lower than R2more it will stops. The default setting is 0.001. (See details for more information) |

`alpha` |
The number given here is a significance level. If the p-value of a variable is higher than alpha, the procedure stops. The default setting is 0.05. (See details for more information) |

`Xscale` |
This parameter scales the data entered as parameter X. The default setting is TRUE |

`Ycenter` |
This parameter centers the data entered as parameter Y. The default setting is TRUE |

`Yscale` |
This parameter scales the data entered as parameter Y. The default setting is FALSE |

`verbose` |
If 'TRUE' more diagnostics are printed. The default setting is TRUE |

### Details

The forward selection will stop when either K, R2tresh, adjR2tresh,
alpha and R2more has its parameter reached. The parametric test for the increase in R-square statistic in forward selection, as implemented in the function `forward.sel.par`

, can be applied as follows.

(a) If Y is univariate, this function implements the standard parametric F-test used in forward selection (FS) in multiple regression.

(b) If Y is multivariate, this function implements FS using the modified F-test described by Miller and Farr (1971). This test requires that

– the Y variables be standardized,

– the error in the response variables be normally distributed. This condition must be verified by the user.

### Value

A dataframe with:

` variables ` |
The names of the variables |

` order ` |
The order of the selection of the variables |

` R2 ` |
The R2 of the variable selected |

` R2Cum ` |
The cumulative R2 of the variables selected |

` AdjR2Cum ` |
The cumulative adjusted R2 of the variables selected |

` F ` |
The F statistic |

` pval ` |
The P-value statistic |

### Note

Not yet implemented for CCA (weighted regression) and with covariables.

### Author(s)

Stephane Dray. For the parametric method, original code of Pierre Legendre and Guillaume Blanchet.

### References

Canoco manual p.49

Miller, J. K., and S. D. Farr. (1971). Bimultivariate redundancy: a comprehensive measure of interbattery relationship. *Multivariate Behavioral Research*, **6**, 313–324.

### Examples

1 2 3 4 5 | ```
x=matrix(rnorm(30),10,3)
y=matrix(rnorm(50),10,5)
forward.sel(y,x,nperm=99, alpha = 0.5)
``` |