Regression-Based Standardization Version — regression

Create a regression_version object that stores regression coefficients and covariate functions for regression-based standardization. Inherits from std_version.

Usage

regression_version(
  scores_class,
  version_id,
  coefs,
  covar_fns,
  raw_scores_fn = function(x) x,
  post_proc_fn = function(x) x,
  description = ""
)

Arguments

scores_class: A single non-empty string giving the npsych_scores subclass this version applies to (e.g., "MOCATOTS").
version_id: A single non-empty string uniquely identifying this version within its method and scores class.
coefs: A named numeric vector of regression coefficients. Must include "intercept" and "rmse" entries.
covar_fns: A named list of functions. Names must match the non-statistic columns in lookup_table.
raw_scores_fn: An optional function that is applied to the raw scores before standardization. Example: the model fitted to get the updated_2025.06 regression coefficients for TRAILA was fitted to the negative TRAILA values to ensure higher values are better. Therefore raw_scores_fn = \(x) -x for this version.
post_proc_fn: An option post processing function that is applied to standardized scores after the fact. For example, for norms based standardization of TRAILA, the sign of the z-scores are flipped to that larger z-scores are correlated with better performance. Hence, post_proc_fn = \(x) -x.
description: An optional single string describing the version.

Value

An S7 object of class regression_version with properties inherited from std_version plus coefs and covar_fns.