An object of class spectral_fit represents a fitted PLS or XLS
regression model for a single component sequence. It is produced internally
by calibrate and is accessible via
object$final_model$model.
A spectral_fit object is a list with the following elements:
method: Thefit_constructorobject passed to the fitting call. Seefit_plsrandfit_xlsr.explained_variance: A list with two matrices:x_variance(three rows:pls_var,x_expl_var,x_expl_var_cum- absolute, relative, and cumulative relative explained variance of X per component) andy_variance(relative explained variance of the response per component).x_means: Named numeric vector of column means of the input spectral matrixX.weights: Matrix of PLS weights (one row per component).scores: Matrix of scores (one column per component).sd_scores: Named numeric vector of standard deviations for each score column.scaled_scores: Matrix of scores scaled by their standard deviations.x_loadings: Matrix of X loadings (one row per component).projection_m: Projection matrix that maps new spectra onto the score space.intercept: Named numeric scalar; the intercept of the regression model (equal to the mean ofY).coefficients: Matrix of regression coefficients (one row per component, one column per wavelength).fitted_y: Matrix of fitted response values (one column per component).cal_error: Matrix with three columns: number of components, root mean squared error of calibration, and largest residual.x_residuals: Matrix of spectral residuals (one column per component).n_observations: Integer; number of observations used for fitting.y_quantiles: Named numeric vector of the 0th, 25th, 50th, 75th, and 100th percentiles of the responseY.