If we look at the p-values of some of the variables, the values seem to be pretty high, which means they aren't significant. That means we can drop those variables from the model. Before dropping the variables, as discussed above, we have to see the multicollinearity between the variables. We do that by calculating the VIF value. Variance Inflation Factor or VIF is a quantitative value that says how much the feature variables are correlated with each other. It is an extremely important parameter to test our linear model.

The goal of performance is to provide lightweight tools to assess and check the quality of your model. It includes functions such as r2() for many models (including logistic, mixed and Bayesian models), icc() or helpers to check_convergence(), check_overdipsersion() or check_zero-inflation() (see a complete list of functions here). In this posting, we want to focus on multicollinearity. Multicollinearity "is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others" (source), i.e. two or more predictors are more or less strongly correlated (also described as non-independent covariates). Multicollinearity may lead to severly biased regression coefficients and standard errors.

Variable reduction is a crucial step for accelerating model building without losing the potential predictive power of the data. With the advent of Big Data and sophisticated data mining techniques, the number of variables encountered is often tremendous making variable selection or dimension reduction techniques imperative to produce models with acceptable accuracy and generalization. The temptation to build an ecological model using all available information (i.e., all variables) is hard to resist. Ample time and money are exhausted gathering data and supporting information. Analytical limitations require us to think carefully about the variables we choose to model, rather than adopting a naive approach where we blindly use all information to understand complexity. The purpose of this post is to illustrate the use of some techniques to effectively manage the selection of explanatory variables consequently leading to a parsimonious model with highest possible prediction accuracy.

Variable reduction is a crucial step for accelerating model building without losing the potential predictive power of the data. With the advent of Big Data and sophisticated data mining techniques, the number of variables encountered is often tremendous making variable selection or dimension reduction techniques imperative to produce models with acceptable accuracy and generalization. The temptation to build an ecological model using all available information (i.e., all variables) is hard to resist. Ample time and money are exhausted gathering data and supporting information. Analytical limitations require us to think carefully about the variables we choose to model, rather than adopting a naive approach where we blindly use all information to understand complexity. The purpose of this post is to illustrate the use of some techniques to effectively manage the selection of explanatory variables consequently leading to a parsimonious model with highest possible prediction accuracy.