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README.md
In collinear: Automated Multicollinearity Management
collinear Automated Multicollinearity Management 
Summary
The R package collinear provides a comprehensive toolkit for smart
multicollinearity management in datasets with mixed variable types. The
main function,
collinear(),
integrates five core components:
-
Target
Encoding
(function
target_encoding_lab()):
Transparently converts categorical predictors to numeric when
required, enabling VIF and correlation analysis across mixed data
types.
-
Intelligent Predictor
Ranking
(function
preference_order()):
Prioritizes predictors by their univariate association with the
response to ensure that the most relevant ones are retained during
filtering.
-
Unified Correlation
Framework
(function
cor_df()):
Computes pairwise correlations between any variable types using
Pearson correlation (numeric-numeric), target encoding
(numeric-categorical), and Cramer’s V (categorical-categorical) within
a single, consistent workflow.
-
Adaptive Filtering
Thresholds
(function
collinear()):
Automatically configures correlation and VIF thresholds based on each
dataset’s correlation structure, eliminating guesswork while allowing
manual override.
-
Dual Filtering
Strategy
(function
collinear_select()):
Combines pairwise correlation and Variance Inflation Factor filtering
while considering predictor rankings to manage multicollinearity while
maximizing the predictive power of the resulting selection of
predictors.
These methods, except target encoding are also fully integrated into the
tidymodels implementation
step_collinear().
The package also provides diagnostic functions
(cor_df(),
vif_df(),
collinear_stats())
to help users assess multicollinearity in their data before and after
filtering.
Install
The package collinear can be installed from CRAN.
install.packages("collinear")
The development version can be installed from GitHub.
remotes::install_github(
repo = "blasbenito/collinear",
ref = "development"
)
Previous versions are in the archive_xxx branches of the GitHub
repository.
remotes::install_github(
repo = "blasbenito/collinear",
ref = "archive_v2.0.0"
)
Getting Started
Setup
Most functions in the collinear package support parallelization and
progress bars via future and
progressr. Parallelization,
however, is only advantageous for large datasets including categorical
predictors.
library(collinear)
library(future)
library(progressr)
future::plan(
future::multisession,
workers = future::availableCores() - 1
)
#does not work in Rmarkdown
#progressr::handlers(global = TRUE)
Example Data
The example dataframe
vi_smol
has several response variables and a large set of predictors. Here we
focus on the numeric response vi_numeric, and the complete set of
numeric and categorical predictors, stored in the vector
vi_predictors.
data(vi_smol, vi_predictors)
nrow(vi_smol)
#> [1] 610
length(vi_predictors)
#> [1] 58
Multicollinearity Analysis
The package provides several functions to assess multicollinearity.
The functions
cor_df()
and
vif_df()
generate dataframes with the pairwise correlations and VIF scores of the
predictors, while the functions
cor_stats(),
vif_stats()
(used below) and
collinear_stats()
generate descriptive multicollinearity statistics. Notice that the
function takes a while to execute because computing correlations for the
categorical variables in vi_predictors is computationally expensive.
collinear::vif_stats(
df = vi_smol,
predictors = vi_predictors,
quiet = TRUE
)
#> Warning:
#> collinear::vif_stats()
#> └── collinear::vif_df()
#> └── collinear::cor_matrix()
#> └── collinear::cor_df(): 11 categorical predictors have cardinality > 2 and may bias the multicollinearity analysis. Applying target encoding to convert them to numeric will solve this issue.
#> method statistic value
#> 1 vif n 58.0000
#> 2 vif minimum -194.5080
#> 3 vif quantile_0.05 -11.2993
#> 4 vif quantile_0.25 0.4528
#> 5 vif mean 67.8998
#> 6 vif median 25.2999
#> 7 vif quantile_0.75 90.3970
#> 8 vif quantile_0.95 316.6229
#> 9 vif maximum 629.6362
The quantile 0.75 returned by vif_stats() indicates that 25% of the
predictors have a VIF score higher than 6.5. This suggests substantial
redundancy in the set of predictors.
Multicollinearity Filtering
To reduce multicollinearity in vi_smol we apply
collinear()
with a minimal setup.
x <- collinear::collinear(
df = vi_smol,
response = "vi_numeric",
predictors = vi_predictors
)
#>
#> collinear::collinear()
#> └── collinear::validate_arg_df(): converted the following character columns to factor:
#> - koppen_zone
#> - koppen_group
#> - koppen_description
#> - biogeo_ecoregion
#> - biogeo_biome
#> - biogeo_realm
#> - country_name
#> - continent
#> - region
#> - subregion
#>
#> collinear::collinear()
#> └── collinear::cor_df(): 11 categorical predictors have cardinality > 2 and may bias the multicollinearity analysis. Applying target encoding to convert them to numeric will solve this issue.
#>
#> collinear::collinear(): setting 'max_cor' to 0.6842.
#>
#> collinear::collinear(): setting 'max_vif' to 6.5269.
#>
#> collinear::collinear()
#> └── collinear::preference_order()
#> └── collinear::f_auto(): selected function 'f_numeric_rf()' to compute preference order.
#>
#> collinear::collinear()
#> └── collinear::collinear_select()
#> └── collinear::vif(): some VIF values exceeded 1M and were set to Inf.
#>
#> collinear::collinear(): selected predictors:
#> - rainfall_mean
#> - swi_mean
#> - evapotranspiration_max
#> - evapotranspiration_range
#> - swi_min
#> - soil_soc
#> - humidity_range
#> - topo_elevation
#> - cloud_cover_range
#> - continent
#> - soil_sand
#> - topo_diversity
#> - topo_slope
The function returns an object of class collinear_output, that has its
own print() and summary() methods.
x
#> Result
#> ===================
#> - response: vi_numeric
#> --------------------
#>
#> + df:
#> - rows: 610
#> - cols: 14
#>
#> + preference order:
#> + df:
#> - rows: 58
#> - cols: 6
#> + preference:
#> - rainfall_mean
#> - growing_season_rainfall
#> - growing_season_length
#> - swi_mean
#> - aridity_index
#> + f: f_numeric_rf
#>
#> + selection:
#> - rainfall_mean
#> - swi_mean
#> - evapotranspiration_max
#> - evapotranspiration_range
#> - swi_min
#> - ... (8 ommited)
#>
#> + formulas:
#> - linear: vi_numeric ~ rainfall_mean + swi_mean + evapotranspiration_max + evapotranspiration_range + swi_min + ... (8 terms omitted)
#> - smooth: vi_numeric ~ s(rainfall_mean) + s(swi_mean) + s(evapotranspiration_max) + s(evapotranspiration_range) + s(swi_min) + ... (8 terms omitted)
The object df contains the response and the selected predictors.
colnames(x$vi_numeric$df)
#> [1] "vi_numeric" "rainfall_mean"
#> [3] "swi_mean" "evapotranspiration_max"
#> [5] "evapotranspiration_range" "swi_min"
#> [7] "soil_soc" "humidity_range"
#> [9] "topo_elevation" "cloud_cover_range"
#> [11] "continent" "soil_sand"
#> [13] "topo_diversity" "topo_slope"
The object preference_order contains the ranking of predictors. It is
computed by the function
preference_order()
by assessing the association between the response and the predictors. In
this case, it fits univariate models between the response and each
predictor using
f_numeric_rf,
and returns the R-squared of the observations vs the model predictions.
This ranking ensures that the most important predictors are protected
during multicollinearity filtering.
head(x$vi_numeric$preference_order)
#> response predictor f metric score rank
#> 1 vi_numeric rainfall_mean f_numeric_rf R-squared 0.8835 1
#> 2 vi_numeric growing_season_rainfall f_numeric_rf R-squared 0.8731 2
#> 3 vi_numeric growing_season_length f_numeric_rf R-squared 0.8644 3
#> 4 vi_numeric swi_mean f_numeric_rf R-squared 0.8333 4
#> 5 vi_numeric aridity_index f_numeric_rf R-squared 0.8279 5
#> 6 vi_numeric koppen_zone f_numeric_rf R-squared 0.8174 6
The object selection contains non-collinear predictors chosen by
taking into account their pairwise correlation and VIF against all other
predictors, and their position in the ranking above.
x$vi_numeric$selection
#> [1] "rainfall_mean" "swi_mean"
#> [3] "evapotranspiration_max" "evapotranspiration_range"
#> [5] "swi_min" "soil_soc"
#> [7] "humidity_range" "topo_elevation"
#> [9] "cloud_cover_range" "continent"
#> [11] "soil_sand" "topo_diversity"
#> [13] "topo_slope"
#> attr(,"validated")
#> [1] TRUE
We can check that this selection of predictors shows low
multicollinearity by running the function
vif_df()
on them.
collinear::vif_df(
df = x$vi_numeric$df,
predictors = x$vi_numeric$selection
)
#> vif predictor
#> 1 4.7203 cloud_cover_range
#> 2 3.3447 continent
#> 3 3.1112 evapotranspiration_max
#> 4 3.0627 evapotranspiration_range
#> 5 2.3856 humidity_range
#> 6 2.0747 rainfall_mean
#> 7 1.9928 soil_sand
#> 8 1.7690 soil_soc
#> 9 1.6346 swi_mean
#> 10 1.5984 swi_min
#> 11 1.3531 topo_diversity
#> 12 0.9320 topo_elevation
#> 13 -0.2235 topo_slope
All VIF scores are below 2.5, collinear() did a good job here!
Finally, collinear() also returns modeling formulas to help kick start
exploratory modelling.
x$vi_numeric$formulas
#> $linear
#> vi_numeric ~ rainfall_mean + swi_mean + evapotranspiration_max +
#> evapotranspiration_range + swi_min + soil_soc + humidity_range +
#> topo_elevation + cloud_cover_range + continent + soil_sand +
#> topo_diversity + topo_slope
#> <environment: 0x5adb1977e4b0>
#>
#> $smooth
#> vi_numeric ~ s(rainfall_mean) + s(swi_mean) + s(evapotranspiration_max) +
#> s(evapotranspiration_range) + s(swi_min) + s(soil_soc) +
#> s(humidity_range) + s(topo_elevation) + s(cloud_cover_range) +
#> continent + s(soil_sand) + s(topo_diversity) + s(topo_slope)
#> <environment: 0x5adb1977e4b0>
The function returns linear formulas for numeric outcomes, and
classification formulas for categorical outcomes.
Model Fitting
The output of collinear() can be used right away to fit exploratory
models, as shown below.
m <- lm(
formula = x$vi_numeric$formulas$linear,
data = x$vi_numeric$df,
na.action = na.omit
)
summary(m)
#>
#> Call:
#> lm(formula = x$vi_numeric$formulas$linear, data = x$vi_numeric$df,
#> na.action = na.omit)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.42639 -0.04274 0.00107 0.04804 0.24228
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 3.027e-01 4.557e-02 6.644 6.97e-11 ***
#> rainfall_mean 4.183e-05 7.152e-06 5.849 8.22e-09 ***
#> swi_mean 6.997e-03 4.415e-04 15.850 < 2e-16 ***
#> evapotranspiration_max -1.206e-03 1.694e-04 -7.117 3.22e-12 ***
#> evapotranspiration_range -1.763e-04 1.396e-04 -1.263 0.207122
#> swi_min -2.473e-03 6.162e-04 -4.013 6.77e-05 ***
#> soil_soc -4.845e-04 1.944e-04 -2.492 0.012964 *
#> humidity_range -1.042e-03 6.301e-04 -1.654 0.098731 .
#> topo_elevation -3.991e-05 7.280e-06 -5.481 6.27e-08 ***
#> cloud_cover_range 5.109e-04 3.588e-04 1.424 0.155005
#> continentAsia -2.847e-02 1.227e-02 -2.320 0.020687 *
#> continentEurope 3.893e-02 1.895e-02 2.054 0.040412 *
#> continentNorth America 6.304e-02 1.525e-02 4.134 4.08e-05 ***
#> continentOceania 4.555e-02 1.478e-02 3.083 0.002146 **
#> continentSouth America 5.547e-02 1.242e-02 4.466 9.54e-06 ***
#> soil_sand 5.123e-04 2.821e-04 1.816 0.069887 .
#> topo_diversity 3.318e-03 8.973e-04 3.698 0.000238 ***
#> topo_slope 4.209e-03 1.392e-03 3.024 0.002600 **
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.08237 on 589 degrees of freedom
#> (3 observations deleted due to missingness)
#> Multiple R-squared: 0.8515, Adjusted R-squared: 0.8473
#> F-statistic: 198.7 on 17 and 589 DF, p-value: < 2.2e-16
Integration with tidymodels
The function
step_collinear()
wraps
collinear()
to facilitate its usage in tidymodels recipes. Please notice that
step_collinear() does not perform target encoding, as combining this
functionality with multicollinearity filtering does not fit well with
how recipes works.
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(recipes)
#>
#> Attaching package: 'recipes'
#> The following object is masked from 'package:stats':
#>
#> step
library(parsnip)
library(workflows)
# model formula
vi_formula <- collinear::model_formula(
df = vi_smol,
response = "vi_numeric",
predictors = vi_predictors
)
# recipe
vi_recipe <- recipes::recipe(
formula = vi_formula,
data = vi_smol
) |>
#multicollinearity filtering
collinear::step_collinear(
recipes::all_predictors()
)
#linear model
vi_model <- parsnip::rand_forest() |>
parsnip::set_engine("ranger", importance = "permutation") |>
parsnip::set_mode("regression")
#create and fit workflow
vi_model <- parsnip::linear_reg() |>
parsnip::set_engine("lm")
# create and fit workflow
vi_workflow <- workflows::workflow() |>
workflows::add_recipe(vi_recipe) |>
workflows::add_model(vi_model) |>
workflows::fit(data = vi_smol)
#> Warning:
#> collinear::collinear()
#> └── collinear::cor_df(): 11 categorical predictors have cardinality > 2 and may bias the multicollinearity analysis. Applying target encoding to convert them to numeric will solve this issue.
vi_workflow
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#>
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 1 Recipe Step
#>
#> • step_collinear()
#>
#> ── Model ───────────────────────────────────────────────────────────────────────
#>
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#>
#> Coefficients:
#> (Intercept) rainfall_mean swi_mean
#> 3.027e-01 4.183e-05 6.997e-03
#> evapotranspiration_max evapotranspiration_range swi_min
#> -1.206e-03 -1.763e-04 -2.473e-03
#> soil_soc humidity_range topo_elevation
#> -4.845e-04 -1.042e-03 -3.991e-05
#> cloud_cover_range continentAsia continentEurope
#> 5.109e-04 -2.847e-02 3.893e-02
#> continentNorth America continentOceania continentSouth America
#> 6.304e-02 4.555e-02 5.547e-02
#> soil_sand topo_diversity topo_slope
#> 5.123e-04 3.318e-03 4.209e-03
Citation
If you find this package useful, please cite it as:
*Blas M. Benito (2025). collinear: R Package for Automated
Multicollinearity Management. Version 3.0.0. doi:
10.5281/zenodo.17843578 *
Getting help
If you encounter bugs or issues with the documentation, please file a
issue on GitHub.
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Any scripts or data that you put into this service are public.
collinear documentation built on Dec. 8, 2025, 5:06 p.m.
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collinear Automated Multicollinearity Management
Summary
The R package collinear provides a comprehensive toolkit for smart
multicollinearity management in datasets with mixed variable types. The
main function,
collinear(),
integrates five core components:
-
Target Encoding (function target_encoding_lab()): Transparently converts categorical predictors to numeric when required, enabling VIF and correlation analysis across mixed data types.
-
Intelligent Predictor Ranking (function preference_order()): Prioritizes predictors by their univariate association with the response to ensure that the most relevant ones are retained during filtering.
-
Unified Correlation Framework (function cor_df()): Computes pairwise correlations between any variable types using Pearson correlation (numeric-numeric), target encoding (numeric-categorical), and Cramer’s V (categorical-categorical) within a single, consistent workflow.
-
Adaptive Filtering Thresholds (function collinear()): Automatically configures correlation and VIF thresholds based on each dataset’s correlation structure, eliminating guesswork while allowing manual override.
-
Dual Filtering Strategy (function collinear_select()): Combines pairwise correlation and Variance Inflation Factor filtering while considering predictor rankings to manage multicollinearity while maximizing the predictive power of the resulting selection of predictors.
These methods, except target encoding are also fully integrated into the
tidymodels implementation
step_collinear().
The package also provides diagnostic functions (cor_df(), vif_df(), collinear_stats()) to help users assess multicollinearity in their data before and after filtering.
Install
The package collinear can be installed from CRAN.
install.packages("collinear")
The development version can be installed from GitHub.
remotes::install_github(
repo = "blasbenito/collinear",
ref = "development"
)
Previous versions are in the archive_xxx branches of the GitHub
repository.
remotes::install_github(
repo = "blasbenito/collinear",
ref = "archive_v2.0.0"
)
Getting Started
Setup
Most functions in the collinear package support parallelization and
progress bars via future and
progressr. Parallelization,
however, is only advantageous for large datasets including categorical
predictors.
library(collinear)
library(future)
library(progressr)
future::plan(
future::multisession,
workers = future::availableCores() - 1
)
#does not work in Rmarkdown
#progressr::handlers(global = TRUE)
Example Data
The example dataframe
vi_smol
has several response variables and a large set of predictors. Here we
focus on the numeric response vi_numeric, and the complete set of
numeric and categorical predictors, stored in the vector
vi_predictors.
data(vi_smol, vi_predictors)
nrow(vi_smol)
#> [1] 610
length(vi_predictors)
#> [1] 58
Multicollinearity Analysis
The package provides several functions to assess multicollinearity.
The functions
cor_df()
and
vif_df()
generate dataframes with the pairwise correlations and VIF scores of the
predictors, while the functions
cor_stats(),
vif_stats()
(used below) and
collinear_stats()
generate descriptive multicollinearity statistics. Notice that the
function takes a while to execute because computing correlations for the
categorical variables in vi_predictors is computationally expensive.
collinear::vif_stats(
df = vi_smol,
predictors = vi_predictors,
quiet = TRUE
)
#> Warning:
#> collinear::vif_stats()
#> └── collinear::vif_df()
#> └── collinear::cor_matrix()
#> └── collinear::cor_df(): 11 categorical predictors have cardinality > 2 and may bias the multicollinearity analysis. Applying target encoding to convert them to numeric will solve this issue.
#> method statistic value
#> 1 vif n 58.0000
#> 2 vif minimum -194.5080
#> 3 vif quantile_0.05 -11.2993
#> 4 vif quantile_0.25 0.4528
#> 5 vif mean 67.8998
#> 6 vif median 25.2999
#> 7 vif quantile_0.75 90.3970
#> 8 vif quantile_0.95 316.6229
#> 9 vif maximum 629.6362
The quantile 0.75 returned by vif_stats() indicates that 25% of the
predictors have a VIF score higher than 6.5. This suggests substantial
redundancy in the set of predictors.
Multicollinearity Filtering
To reduce multicollinearity in vi_smol we apply
collinear()
with a minimal setup.
x <- collinear::collinear(
df = vi_smol,
response = "vi_numeric",
predictors = vi_predictors
)
#>
#> collinear::collinear()
#> └── collinear::validate_arg_df(): converted the following character columns to factor:
#> - koppen_zone
#> - koppen_group
#> - koppen_description
#> - biogeo_ecoregion
#> - biogeo_biome
#> - biogeo_realm
#> - country_name
#> - continent
#> - region
#> - subregion
#>
#> collinear::collinear()
#> └── collinear::cor_df(): 11 categorical predictors have cardinality > 2 and may bias the multicollinearity analysis. Applying target encoding to convert them to numeric will solve this issue.
#>
#> collinear::collinear(): setting 'max_cor' to 0.6842.
#>
#> collinear::collinear(): setting 'max_vif' to 6.5269.
#>
#> collinear::collinear()
#> └── collinear::preference_order()
#> └── collinear::f_auto(): selected function 'f_numeric_rf()' to compute preference order.
#>
#> collinear::collinear()
#> └── collinear::collinear_select()
#> └── collinear::vif(): some VIF values exceeded 1M and were set to Inf.
#>
#> collinear::collinear(): selected predictors:
#> - rainfall_mean
#> - swi_mean
#> - evapotranspiration_max
#> - evapotranspiration_range
#> - swi_min
#> - soil_soc
#> - humidity_range
#> - topo_elevation
#> - cloud_cover_range
#> - continent
#> - soil_sand
#> - topo_diversity
#> - topo_slope
The function returns an object of class collinear_output, that has its
own print() and summary() methods.
x
#> Result
#> ===================
#> - response: vi_numeric
#> --------------------
#>
#> + df:
#> - rows: 610
#> - cols: 14
#>
#> + preference order:
#> + df:
#> - rows: 58
#> - cols: 6
#> + preference:
#> - rainfall_mean
#> - growing_season_rainfall
#> - growing_season_length
#> - swi_mean
#> - aridity_index
#> + f: f_numeric_rf
#>
#> + selection:
#> - rainfall_mean
#> - swi_mean
#> - evapotranspiration_max
#> - evapotranspiration_range
#> - swi_min
#> - ... (8 ommited)
#>
#> + formulas:
#> - linear: vi_numeric ~ rainfall_mean + swi_mean + evapotranspiration_max + evapotranspiration_range + swi_min + ... (8 terms omitted)
#> - smooth: vi_numeric ~ s(rainfall_mean) + s(swi_mean) + s(evapotranspiration_max) + s(evapotranspiration_range) + s(swi_min) + ... (8 terms omitted)
The object df contains the response and the selected predictors.
colnames(x$vi_numeric$df)
#> [1] "vi_numeric" "rainfall_mean"
#> [3] "swi_mean" "evapotranspiration_max"
#> [5] "evapotranspiration_range" "swi_min"
#> [7] "soil_soc" "humidity_range"
#> [9] "topo_elevation" "cloud_cover_range"
#> [11] "continent" "soil_sand"
#> [13] "topo_diversity" "topo_slope"
The object preference_order contains the ranking of predictors. It is
computed by the function
preference_order()
by assessing the association between the response and the predictors. In
this case, it fits univariate models between the response and each
predictor using
f_numeric_rf,
and returns the R-squared of the observations vs the model predictions.
This ranking ensures that the most important predictors are protected
during multicollinearity filtering.
head(x$vi_numeric$preference_order)
#> response predictor f metric score rank
#> 1 vi_numeric rainfall_mean f_numeric_rf R-squared 0.8835 1
#> 2 vi_numeric growing_season_rainfall f_numeric_rf R-squared 0.8731 2
#> 3 vi_numeric growing_season_length f_numeric_rf R-squared 0.8644 3
#> 4 vi_numeric swi_mean f_numeric_rf R-squared 0.8333 4
#> 5 vi_numeric aridity_index f_numeric_rf R-squared 0.8279 5
#> 6 vi_numeric koppen_zone f_numeric_rf R-squared 0.8174 6
The object selection contains non-collinear predictors chosen by
taking into account their pairwise correlation and VIF against all other
predictors, and their position in the ranking above.
x$vi_numeric$selection
#> [1] "rainfall_mean" "swi_mean"
#> [3] "evapotranspiration_max" "evapotranspiration_range"
#> [5] "swi_min" "soil_soc"
#> [7] "humidity_range" "topo_elevation"
#> [9] "cloud_cover_range" "continent"
#> [11] "soil_sand" "topo_diversity"
#> [13] "topo_slope"
#> attr(,"validated")
#> [1] TRUE
We can check that this selection of predictors shows low multicollinearity by running the function vif_df() on them.
collinear::vif_df(
df = x$vi_numeric$df,
predictors = x$vi_numeric$selection
)
#> vif predictor
#> 1 4.7203 cloud_cover_range
#> 2 3.3447 continent
#> 3 3.1112 evapotranspiration_max
#> 4 3.0627 evapotranspiration_range
#> 5 2.3856 humidity_range
#> 6 2.0747 rainfall_mean
#> 7 1.9928 soil_sand
#> 8 1.7690 soil_soc
#> 9 1.6346 swi_mean
#> 10 1.5984 swi_min
#> 11 1.3531 topo_diversity
#> 12 0.9320 topo_elevation
#> 13 -0.2235 topo_slope
All VIF scores are below 2.5, collinear() did a good job here!
Finally, collinear() also returns modeling formulas to help kick start
exploratory modelling.
x$vi_numeric$formulas
#> $linear
#> vi_numeric ~ rainfall_mean + swi_mean + evapotranspiration_max +
#> evapotranspiration_range + swi_min + soil_soc + humidity_range +
#> topo_elevation + cloud_cover_range + continent + soil_sand +
#> topo_diversity + topo_slope
#> <environment: 0x5adb1977e4b0>
#>
#> $smooth
#> vi_numeric ~ s(rainfall_mean) + s(swi_mean) + s(evapotranspiration_max) +
#> s(evapotranspiration_range) + s(swi_min) + s(soil_soc) +
#> s(humidity_range) + s(topo_elevation) + s(cloud_cover_range) +
#> continent + s(soil_sand) + s(topo_diversity) + s(topo_slope)
#> <environment: 0x5adb1977e4b0>
The function returns linear formulas for numeric outcomes, and classification formulas for categorical outcomes.
Model Fitting
The output of collinear() can be used right away to fit exploratory
models, as shown below.
m <- lm(
formula = x$vi_numeric$formulas$linear,
data = x$vi_numeric$df,
na.action = na.omit
)
summary(m)
#>
#> Call:
#> lm(formula = x$vi_numeric$formulas$linear, data = x$vi_numeric$df,
#> na.action = na.omit)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.42639 -0.04274 0.00107 0.04804 0.24228
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 3.027e-01 4.557e-02 6.644 6.97e-11 ***
#> rainfall_mean 4.183e-05 7.152e-06 5.849 8.22e-09 ***
#> swi_mean 6.997e-03 4.415e-04 15.850 < 2e-16 ***
#> evapotranspiration_max -1.206e-03 1.694e-04 -7.117 3.22e-12 ***
#> evapotranspiration_range -1.763e-04 1.396e-04 -1.263 0.207122
#> swi_min -2.473e-03 6.162e-04 -4.013 6.77e-05 ***
#> soil_soc -4.845e-04 1.944e-04 -2.492 0.012964 *
#> humidity_range -1.042e-03 6.301e-04 -1.654 0.098731 .
#> topo_elevation -3.991e-05 7.280e-06 -5.481 6.27e-08 ***
#> cloud_cover_range 5.109e-04 3.588e-04 1.424 0.155005
#> continentAsia -2.847e-02 1.227e-02 -2.320 0.020687 *
#> continentEurope 3.893e-02 1.895e-02 2.054 0.040412 *
#> continentNorth America 6.304e-02 1.525e-02 4.134 4.08e-05 ***
#> continentOceania 4.555e-02 1.478e-02 3.083 0.002146 **
#> continentSouth America 5.547e-02 1.242e-02 4.466 9.54e-06 ***
#> soil_sand 5.123e-04 2.821e-04 1.816 0.069887 .
#> topo_diversity 3.318e-03 8.973e-04 3.698 0.000238 ***
#> topo_slope 4.209e-03 1.392e-03 3.024 0.002600 **
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.08237 on 589 degrees of freedom
#> (3 observations deleted due to missingness)
#> Multiple R-squared: 0.8515, Adjusted R-squared: 0.8473
#> F-statistic: 198.7 on 17 and 589 DF, p-value: < 2.2e-16
Integration with tidymodels
The function
step_collinear()
wraps
collinear()
to facilitate its usage in tidymodels recipes. Please notice that
step_collinear() does not perform target encoding, as combining this
functionality with multicollinearity filtering does not fit well with
how recipes works.
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(recipes)
#>
#> Attaching package: 'recipes'
#> The following object is masked from 'package:stats':
#>
#> step
library(parsnip)
library(workflows)
# model formula
vi_formula <- collinear::model_formula(
df = vi_smol,
response = "vi_numeric",
predictors = vi_predictors
)
# recipe
vi_recipe <- recipes::recipe(
formula = vi_formula,
data = vi_smol
) |>
#multicollinearity filtering
collinear::step_collinear(
recipes::all_predictors()
)
#linear model
vi_model <- parsnip::rand_forest() |>
parsnip::set_engine("ranger", importance = "permutation") |>
parsnip::set_mode("regression")
#create and fit workflow
vi_model <- parsnip::linear_reg() |>
parsnip::set_engine("lm")
# create and fit workflow
vi_workflow <- workflows::workflow() |>
workflows::add_recipe(vi_recipe) |>
workflows::add_model(vi_model) |>
workflows::fit(data = vi_smol)
#> Warning:
#> collinear::collinear()
#> └── collinear::cor_df(): 11 categorical predictors have cardinality > 2 and may bias the multicollinearity analysis. Applying target encoding to convert them to numeric will solve this issue.
vi_workflow
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#>
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 1 Recipe Step
#>
#> • step_collinear()
#>
#> ── Model ───────────────────────────────────────────────────────────────────────
#>
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#>
#> Coefficients:
#> (Intercept) rainfall_mean swi_mean
#> 3.027e-01 4.183e-05 6.997e-03
#> evapotranspiration_max evapotranspiration_range swi_min
#> -1.206e-03 -1.763e-04 -2.473e-03
#> soil_soc humidity_range topo_elevation
#> -4.845e-04 -1.042e-03 -3.991e-05
#> cloud_cover_range continentAsia continentEurope
#> 5.109e-04 -2.847e-02 3.893e-02
#> continentNorth America continentOceania continentSouth America
#> 6.304e-02 4.555e-02 5.547e-02
#> soil_sand topo_diversity topo_slope
#> 5.123e-04 3.318e-03 4.209e-03
Citation
If you find this package useful, please cite it as:
*Blas M. Benito (2025). collinear: R Package for Automated Multicollinearity Management. Version 3.0.0. doi: 10.5281/zenodo.17843578 *
Getting help
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