non_collinear_vars: Select a set of predictors with minimal multicollinearity
Description
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.
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.
Arguments
.data
The data set containing the variables.
...
Variables to be submitted to selection. If ... is null then
all the numeric variables from .data are used. It must be a single
variable name or a comma-separated list of unquoted variables names.
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 stats::cor().