Description Usage Arguments Details Value Note Author(s) References Examples

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

1 2 3 |

`Y` |
Response data matrix with n rows and m columns containing quantitative variables |

`X` |
Explanatory data matrix with n rows and p columns containing quantitative variables |

`K` |
Maximum number of variables to be selected. The default is one minus the number of rows |

`R2thresh` |
Stop the forward selection procedure if the R-square of the model exceeds the stated value. This parameter can vary from 0.001 to 1 |

`adjR2thresh` |
Stop the forward selection procedure if the adjusted R-square of the model exceeds the stated value. This parameter can take any value (positive or negative) smaller than 1 |

`nperm` |
The number of permutation to be used.The default setting is 999 permutation. |

`R2more` |
Stop the forward selection procedure if the difference in model R-square with the previous step is lower than R2more. The default setting is 0.001 |

`alpha` |
Significance level. Stop the forward selection procedure if the p-value of a variable is higher than alpha. The default is 0.05 is TRUE |

`Xscale` |
Standardize the variables in table X to variance 1. The default setting is TRUE |

`Ycenter` |
Center the variables in table Y. The default setting is TRUE |

`Yscale` |
Standardize the variables in table Y to variance 1. The default setting is FALSE. |

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

The forward selection will stop when either K, R2tresh, adjR2tresh, alpha and R2more has its parameter reached.

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 |

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

Stephane Dray [email protected]

Canoco manual p.49

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)
``` |

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