compute_diagnostics: Compute model diagnostics

In mnirs: Muscle Near-Infrared Spectroscopy Processing and Analysis

Compute model diagnostics

Description

Compute model diagnostics

Usage

compute_diagnostics(x, t, fitted, n_params = 1L, env = rlang::caller_env())

Arguments

x	A numeric vector of the response variable.
t	An optional numeric vector of the predictor variable (e.g. time). Default is seq_along(x).
fitted	A numeric vector of the predicted values.
n_params	Integer; total number of estimated coefficients in the model (default 1L). For linear models pass the number of regression coefficients (e.g. 2L for lm(x ~ t)). For non-linear models ("monoexponential", "sigmoidal"), pass the number of free parameters fit by the solver.
env	The calling environment or a defused call, used to report errors and warnings as coming from the user-facing function rather than the validator.

Details

r2

Squared Pearson correlation between observed and fitted values. Equals the classic 1 - SSres / SStot for OLS linear fits (matches summary(lm)$r.squared); a bounded ⁠[0, 1]⁠ pseudo-R^2 for non-linear fits such as "monoexponential" and "sigmoidal".

adj_r2

Adjusted R^2 penalised by n_params. Appropriate for OLS linear models; interpret with caution for non-linear fits.

aic, aicc, bic

Information criteria derived from a Gaussian log-likelihood with the maximum-likelihood residual variance sigma_hat^2 = SSres / n_obs. The effective parameter count is k = n_params + 1 (the +1 accounts for the estimated residual variance). Values match stats::AIC() and stats::BIC() for lm and nls fits. aicc is the small-sample correction and is NA when n_obs - k - 1 <= 0.

Value

A 1-row data.frame with columns n_obs, n_params, r2, adj_r2, rmse, cv_rmse, snr, aic, aicc, and bic.

mnirs documentation built on Sept. 13, 2026, 1:06 a.m.