Description Usage Arguments Examples

This function constructs every possible linear model with two independent variables, one linear and one exponential term (y = m^2x + ny + b) for nx number of dependent variables and testing every pair of independent variables (nx).

1 2 | ```
model2v4pB(model.data, ny, nx, CV = F, CV_n = 1, r2random = F,
runs = 1000)
``` |

`model.data=data.frame` |
Data.frame that contains both the dependent and independent variables |

`ny=number` |
Number of dependent variables to be tested. |

`nx=number` |
Number of independent variables to be tested. Do not confuse number of available independent variables with number of independent variables OF THE MODEL. |

`CV=boolean` |
T if a cross-validation should be performed, F if not. |

`CV_n=number` |
Number of observations to be left out in the cross-validation process. For example CV = T & CV_n = 1 will perform a 1-leave-out-cross-validation, while the same command with CV_n = 2, will performa a 2-leave-out-cross-validation |

`r2random=boolean` |
T if an R^2 maximum random distribution should be computed with the data. Calculating this random distribution enables a comparison of the observed R^2 values of the best models with a completely random scenario. This distribution shows if the goodness-of-fit values obtained in the models correspond to a significntly higher value than the expected at random (>= value of percentile 95) or not. |

`runs=number` |
Number indicating the number of runs to perform the goodness-of-fit random distribution. |

1 | ```
model2v4pB(df,ny=9,nx=16)
``` |

JonathanVSV/RSModels documentation built on Aug. 22, 2019, 4:16 p.m.

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