Multicollinearity filtering by pairwise correlation threshold

Wraps collinear_select() to automatize multicollinearity filtering via pairwise correlation in dataframes with numeric and categorical predictors.

The argument max_cor determines the maximum variance inflation factor allowed in the resulting selection of predictors.

The argument preference_order accepts a character vector of predictor names ranked from first to last index, or a dataframe resulting from preference_order(). When two predictors in this vector or dataframe are highly collinear, the one with a lower ranking is removed. This option helps protect predictors of interest. If not provided, predictors are ranked from lower to higher multicollinearity.

Please check the section Pairwise Correlation Filtering at the end of this help file for further details.

Usage

cor_select(
  df = NULL,
  response = NULL,
  predictors = NULL,
  preference_order = NULL,
  max_cor = 0.7,
  quiet = FALSE,
  ...
)

Arguments

df: (required; dataframe, tibble, or sf) A dataframe with responses (optional) and predictors. Must have at least 10 rows for pairwise correlation analysis, and 10 * (length(predictors) - 1) for VIF. Default: NULL.
response: (optional; character or NULL) Name of one response variable in df. Used to exclude columns when predictors is NULL, and to filter preference_order when it is a dataframe and contains several responses. Default: NULL.
predictors: (optional; character vector or NULL) Names of the predictors in df. If NULL, all columns except responses and constant/near-zero-variance columns are used. Default: NULL.
preference_order: (optional; character vector, dataframe from preference_order, or NULL) Prioritizes predictors to preserve.
max_cor: (optional; numeric or NULL) Maximum correlation allowed between pairs of predictors. Valid values are between 0.01 and 0.99, and recommended values are between 0.5 (strict) and 0.9 (permissive). Default: 0.7
quiet: (optional; logical) If FALSE, messages are printed. Default: FALSE.
...: (optional) Internal args (e.g. function_name for validate_arg_function_name, a precomputed correlation matrix m, or cross-validation args for preference_order).

Value

character vector of selected predictors

Pairwise Correlation Filtering

cor_select computes the global correlation matrix, orders predictors by preference_order or by lower-to-higher summed correlations, and sequentially selects predictors with pairwise correlations below max_cor.

Author

Blas M. Benito, PhD

Examples

data(vi_smol)

## OPTIONAL: parallelization setup
## irrelevant when all predictors are numeric
## only worth it for large data with many categoricals
# future::plan(
#   future::multisession,
#   workers = future::availableCores() - 1
# )

## OPTIONAL: progress bar
# progressr::handlers(global = TRUE)

#predictors
predictors = c(
  "koppen_zone", #character
  "soil_type", #factor
  "topo_elevation", #numeric
  "soil_temperature_mean" #numeric
)

#predictors ordered from lower to higher multicollinearity
x <- cor_select(
  df = vi_smol,
  predictors = predictors,
  max_cor = 0.7
)
#> 
#> collinear::cor_select()
#> └── collinear::collinear_select()
#>     └── collinear::validate_arg_df(): converted the following character columns to factor:
#>  - koppen_zone
#> 
#> collinear::cor_select()
#> └── collinear::collinear_select()
#>     └── collinear::cor_matrix()
#>         └── collinear::cor_df(): 2 categorical predictors have cardinality > 2 and may bias the multicollinearity analysis. Applying target encoding to convert them to numeric will solve this issue.
#> 
#> collinear::cor_select()
#> └── collinear::collinear_select()
#>     └── collinear::validate_arg_preference_order()
#>         └── collinear::preference_order(): ranking 4 'predictors' from lower to higher multicollinearity.
#> 
#> collinear::cor_select()
#> └── collinear::collinear_select()
#>     └── collinear::validate_arg_preference_order()
#>         └── collinear::preference_order()
#>             └── collinear::cor_matrix()
#>                 └── collinear::cor_df(): 2 categorical predictors have cardinality > 2 and may bias the multicollinearity analysis. Applying target encoding to convert them to numeric will solve this issue.

x
#> [1] "topo_elevation" "soil_type"      "koppen_zone"   
#> attr(,"validated")
#> [1] TRUE


#with custom preference order
x <- cor_select(
  df = vi_smol,
  predictors = predictors,
  preference_order = c(
    "koppen_zone",
    "soil_type"
  ),
  max_cor = 0.7
)
#> 
#> collinear::cor_select()
#> └── collinear::collinear_select()
#>     └── collinear::validate_arg_df(): converted the following character columns to factor:
#>  - koppen_zone
#> 
#> collinear::cor_select()
#> └── collinear::collinear_select()
#>     └── collinear::cor_matrix()
#>         └── collinear::cor_df(): 2 categorical predictors have cardinality > 2 and may bias the multicollinearity analysis. Applying target encoding to convert them to numeric will solve this issue.
#> 
#> collinear::cor_select()
#> └── collinear::collinear_select()
#>     └── collinear::validate_arg_preference_order()
#>         └── collinear::preference_order(): ranking 2 'predictors' from lower to higher multicollinearity.

x
#> [1] "koppen_zone"    "soil_type"      "topo_elevation"
#> attr(,"validated")
#> [1] TRUE

#with automated preference order
df_preference <- preference_order(
  df = vi_smol,
  response = "vi_numeric",
  predictors = predictors
)
#> 
#> collinear::preference_order()
#> └── collinear::validate_arg_df(): converted the following character columns to factor:
#>  - koppen_zone
#> 
#> collinear::preference_order()
#> └── collinear::f_auto(): selected function 'f_numeric_rf()' to compute preference order.

df_preference
#>     response             predictor            f    metric  score rank
#> 1 vi_numeric           koppen_zone f_numeric_rf R-squared 0.8174    1
#> 2 vi_numeric             soil_type f_numeric_rf R-squared 0.6249    2
#> 3 vi_numeric soil_temperature_mean f_numeric_rf R-squared 0.4201    3
#> 4 vi_numeric        topo_elevation f_numeric_rf R-squared 0.3754    4

x <- cor_select(
  df = vi_smol,
  predictors = predictors,
  preference_order = df_preference,
  max_cor = 0.7
)
#> 
#> collinear::cor_select()
#> └── collinear::collinear_select()
#>     └── collinear::validate_arg_df(): converted the following character columns to factor:
#>  - koppen_zone
#> 
#> collinear::cor_select()
#> └── collinear::collinear_select()
#>     └── collinear::cor_matrix()
#>         └── collinear::cor_df(): 2 categorical predictors have cardinality > 2 and may bias the multicollinearity analysis. Applying target encoding to convert them to numeric will solve this issue.

x
#> [1] "koppen_zone"    "soil_type"      "topo_elevation"
#> attr(,"validated")
#> [1] TRUE

#OPTIONAL: disable parallelization
#future::plan(future::sequential)