Version 2.0.0 of collinear includes changes that may
disrupt existing workflows, and results from previous versions may not
be reproducible due to enhancements in the automated selection
algorithms. Please refer to the Changelog for details.
Multicollinearity hinders the interpretability of linear and machine learning models.
The collinear package combines four methods for easy
management of multicollinearity in modelling data frames with numeric
and categorical variables:
These methods are combined in the function collinear(),
which serves as single entry point for most of the functionalities in
the package. The article How
It Works explains how collinear() works in detail.
If you find this package useful, please cite it as:
Blas M. Benito (2024). collinear: R Package for Seamless Multicollinearity Management. Version 2.0.0. doi: 10.5281/zenodo.10039489
collinear() and preference_order() support
both categorical and numeric responses and predictors, and can handle
several responses at once.vif_select() and cor_select().future and progressr for enhanced performance
and user experience.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_v1.1.1"
)The function collinear() provides all tools required for
a fully fledged multicollinearity filtering workflow. The code below
shows a small example workflow.
#parallelization setup
future::plan(
future::multisession,
workers = parallelly::availableCores() - 1
)
#progress bar (does not work in Rmarkdown)
#progressr::handlers(global = TRUE)
#example data frame
df <- collinear::vi[1:5000, ]
#there are many NA cases in this data frame
sum(is.na(df))
#> [1] 3391#numeric and categorical predictors
predictors <- collinear::vi_predictors
collinear::identify_predictors(
df = df,
predictors = predictors
)
#> $numeric
#> [1] "topo_slope" "topo_diversity"
#> [3] "topo_elevation" "swi_mean"
#> [5] "swi_max" "swi_min"
#> [7] "swi_range" "soil_temperature_mean"
#> [9] "soil_temperature_max" "soil_temperature_min"
#> [11] "soil_temperature_range" "soil_sand"
#> [13] "soil_clay" "soil_silt"
#> [15] "soil_ph" "soil_soc"
#> [17] "soil_nitrogen" "solar_rad_mean"
#> [19] "solar_rad_max" "solar_rad_min"
#> [21] "solar_rad_range" "growing_season_length"
#> [23] "growing_season_temperature" "growing_season_rainfall"
#> [25] "growing_degree_days" "temperature_mean"
#> [27] "temperature_max" "temperature_min"
#> [29] "temperature_range" "temperature_seasonality"
#> [31] "rainfall_mean" "rainfall_min"
#> [33] "rainfall_max" "rainfall_range"
#> [35] "evapotranspiration_mean" "evapotranspiration_max"
#> [37] "evapotranspiration_min" "evapotranspiration_range"
#> [39] "cloud_cover_mean" "cloud_cover_max"
#> [41] "cloud_cover_min" "cloud_cover_range"
#> [43] "aridity_index" "humidity_mean"
#> [45] "humidity_max" "humidity_min"
#> [47] "humidity_range" "country_population"
#> [49] "country_gdp"
#>
#> $categorical
#> [1] "koppen_zone" "koppen_group" "koppen_description"
#> [4] "soil_type" "biogeo_ecoregion" "biogeo_biome"
#> [7] "biogeo_realm" "country_name" "country_income"
#> [10] "continent" "region" "subregion"#multicollinearity filtering
selection <- collinear::collinear(
df = df,
response = c(
"vi_numeric", #numeric response
"vi_categorical" #categorical response
),
predictors = predictors,
max_cor = 0.75,
max_vif = 5,
quiet = TRUE
)The output is a named list of vectors with selected predictor names when more than one response is provided, and a character vector otherwise.
selection
#> $vi_numeric
#> [1] "growing_season_length" "soil_temperature_max" "soil_temperature_range"
#> [4] "solar_rad_max" "rainfall_max" "subregion"
#> [7] "biogeo_realm" "swi_range" "rainfall_min"
#> [10] "soil_nitrogen" "continent" "cloud_cover_range"
#> [13] "topo_diversity"
#> attr(,"validated")
#> [1] TRUE
#> attr(,"response")
#> [1] "vi_numeric"
#>
#> $vi_categorical
#> [1] "rainfall_mean" "swi_mean" "soil_temperature_max"
#> [4] "soil_type" "humidity_max" "solar_rad_max"
#> [7] "country_gdp" "swi_range" "rainfall_range"
#> [10] "country_population" "soil_soc" "region"
#> [13] "country_income" "topo_diversity" "topo_slope"
#> attr(,"validated")
#> [1] TRUE
#> attr(,"response")
#> [1] "vi_categorical"The output of collinear() can be easily converted into
model formulas.
formulas <- collinear::model_formula(
predictors = selection
)
formulas
#> $vi_numeric
#> vi_numeric ~ growing_season_length + soil_temperature_max + soil_temperature_range +
#> solar_rad_max + rainfall_max + subregion + biogeo_realm +
#> swi_range + rainfall_min + soil_nitrogen + continent + cloud_cover_range +
#> topo_diversity
#> <environment: 0x5afafd703c68>
#>
#> $vi_categorical
#> vi_categorical ~ rainfall_mean + swi_mean + soil_temperature_max +
#> soil_type + humidity_max + solar_rad_max + country_gdp +
#> swi_range + rainfall_range + country_population + soil_soc +
#> region + country_income + topo_diversity + topo_slope
#> <environment: 0x5afafd703c68>These formulas can be used to fit models right away.
#linear model
m_vi_numeric <- stats::glm(
formula = formulas[["vi_numeric"]],
data = df,
na.action = na.omit
)
#random forest model
m_vi_categorical <- ranger::ranger(
formula = formulas[["vi_categorical"]],
data = na.omit(df)
)If you encounter bugs or issues with the documentation, please file a issue on GitHub.