View source: R/non_collinear_vars.R
non_collinear_vars | R Documentation |
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
.
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.
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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