Description Usage Arguments Value Examples

View source: R/non_collinear_vars.R

Select a set of predictors with minimal multicollinearity using the variance
inflation factor (VIF) as criteria to remove collinear variables. The
algorithm will: **(i)** compute the VIF value of the correlation matrix
containing the variables selected in `...`

; **(ii)** arrange the
VIF values and delete the variable with the highest VIF; and **(iii)**
iterate step **ii** until VIF value is less than or equal to
`max_vif`

.

1 2 3 4 5 6 | ```
non_collinear_vars(
.data,
...,
max_vif = 10,
missingval = "pairwise.complete.obs"
)
``` |

`.data` |
The data set containing the variables. |

`...` |
Variables to be submitted to selection. If |

`max_vif` |
The maximum value for the Variance Inflation Factor (threshold) that will be accepted in the set of selected predictors. |

`missingval` |
How to deal with missing values. For more information,
please see |

A data frame showing the number of selected predictors, maximum VIF value, condition number, determinant value, selected predictors and removed predictors from the original set of variables.

1 2 3 4 5 6 | ```
library(metan)
# All numeric variables
non_collinear_vars(data_ge2)
# Select variables and choose a VIF threshold to 5
non_collinear_vars(data_ge2, EH, CL, CW, KW, NKE, max_vif = 5)
``` |

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