General Effect Modelling

Two-step modeling with separation of sources of variation through analysis of variance and subsequent multivariate modeling through a range of unsupervised and supervised statistical methods. Separation can focus on removal of interfering effects or isolation of effects of interest. EF Mosleth et al. (2021) and EF Mosleth et al. (2020) .

General Effect Modelling - gemR

Installation

# Install release version from CRAN 
install.packages("gemR")  
# Install development version from GitHub  
devtools::install_github("khliland/gemR")

Contents

• General Effect Modelling
• Convenience functions
  • Knock-in (isolation/extraction) of effects and residuals from a GEM model.
  • Knock-out (removal) of effect from a dataset using a GEM model.
• Supervised analyses of effects and residuals
  • Partial Least Squares
  • Elastic net
  • Neural Networks
• Confidence intervals