docs

R/check_collinearity.R

@@ -73,7 +73,8 @@
 #' This portion of multicollinearity among the component terms of an
 #' interaction is also called "inessential ill-conditioning", which leads to
 #' inflated VIF values that are typically seen for models with interaction
-#' terms _(Francoeur 2013)_.
+#' terms _(Francoeur 2013)_. Centering interaction terms can resolve this
+#' issue _(Kim and Jung 2024)_.
 #'
 #' @section Concurvity for Smooth Terms in Generalized Additive Models:
 #' `check_concurvity()` is a wrapper around `mgcv::concurvity()`, and can be
@@ -91,26 +92,30 @@
 #' @references
 #'
 #' - Francoeur, R. B. (2013). Could Sequential Residual Centering Resolve
-#' Low Sensitivity in Moderated Regression? Simulations and Cancer Symptom
-#' Clusters. Open Journal of Statistics, 03(06), 24-44.
+#'   Low Sensitivity in Moderated Regression? Simulations and Cancer Symptom
+#'   Clusters. Open Journal of Statistics, 03(06), 24-44.
 #'
-#' - James, G., Witten, D., Hastie, T., and Tibshirani, R. (eds.). (2013).
-#' An introduction to statistical learning: with applications in R. New York:
-#' Springer.
+#' - James, G., Witten, D., Hastie, T., and Tibshirani, R. (eds.). (2013). An
+#'   introduction to statistical learning: with applications in R. New York:
+#'   Springer.
+#'
+#' - Kim, Y., & Jung, G. (2024). Understanding linear interaction analysis with
+#'   causal graphs. British Journal of Mathematical and Statistical Psychology,
+#'   00, 1–14.
 #'
 #' - Marcoulides, K. M., and Raykov, T. (2019). Evaluation of Variance
-#' Inflation Factors in Regression Models Using Latent Variable Modeling
-#' Methods. Educational and Psychological Measurement, 79(5), 874–882.
+#'   Inflation Factors in Regression Models Using Latent Variable Modeling
+#'   Methods. Educational and Psychological Measurement, 79(5), 874–882.
 #'
 #' - McElreath, R. (2020). Statistical rethinking: A Bayesian course with
-#' examples in R and Stan. 2nd edition. Chapman and Hall/CRC.
+#'   examples in R and Stan. 2nd edition. Chapman and Hall/CRC.
 #'
 #' - Vanhove, J. (2019). Collinearity isn't a disease that needs curing.
-#' [webpage](https://janhove.github.io/posts/2019-09-11-collinearity/)
+#'   [webpage](https://janhove.github.io/posts/2019-09-11-collinearity/)
 #'
 #' - Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid
-#' common statistical problems: Data exploration. Methods in Ecology and
-#' Evolution (2010) 1:3–14.
+#'   common statistical problems: Data exploration. Methods in Ecology and
+#'   Evolution (2010) 1:3–14.
 #'
 #' @family functions to check model assumptions and and assess model quality
 #'

vignettes/check_model.Rmd

@@ -250,7 +250,7 @@ Our model clearly suffers from multicollinearity, as all predictors have high VI
 
 ### How to fix this?
 
-Usually, predictors with (very) high VIF values should be removed from the model to fix multicollinearity. Some caution is needed for interaction terms. If interaction terms are included in a model, high VIF values are expected. This portion of multicollinearity among the component terms of an interaction is also called "inessential ill-conditioning", which leads to inflated VIF values that are typically seen for models with interaction terms _(Francoeur 2013)_. In such cases, re-fit your model without interaction terms and check this model for collinearity among predictors.
+Usually, predictors with (very) high VIF values should be removed from the model to fix multicollinearity. Some caution is needed for interaction terms. If interaction terms are included in a model, high VIF values are expected. This portion of multicollinearity among the component terms of an interaction is also called "inessential ill-conditioning", which leads to inflated VIF values that are typically seen for models with interaction terms _(Francoeur 2013)_. In such cases, try centering the involved interaction terms, which can reduce multicollinearity _(Kim and Jung 2024)_, or re-fit your model without interaction terms and check this model for collinearity among predictors.
 
 ## Normality of residuals
 
@@ -293,6 +293,8 @@ Gelman A, and Hill J. Data analysis using regression and multilevel/hierarchical
 
 James, G., Witten, D., Hastie, T., and Tibshirani, R. (eds.).An introduction to statistical learning: with applications in R. New York: Springer, 2013
 
+Kim, Y., & Jung, G. (2024). Understanding linear interaction analysis with causal graphs. British Journal of Mathematical and Statistical Psychology, 00, 1–14.
+
 Leys C, Delacre M, Mora YL, Lakens D, Ley C. How to Classify, Detect, and Manage Univariate and Multivariate Outliers, With Emphasis on Pre-Registration. International Review of Social Psychology, 2019
 
 McElreath, R. Statistical rethinking: A Bayesian course with examples in R and Stan. 2nd edition. Chapman and Hall/CRC, 2020