Assess Deconvolution Quality

Calculates comprehensive quality metrics for a peak deconvolution fit, including goodness-of-fit statistics, information criteria, per-peak quality, and residual diagnostics.

Usage

assess_deconv_quality(x, y, result, models)

Arguments

x

Numeric vector of x-axis values.

y

Numeric vector of observed y-axis values.

result

Deconvolution result list from optimize_deconvolution().

models

List of peak_model objects used in deconvolution.

Value

A list of class deconv_quality containing:

• goodness_of_fit: R-squared, RMSE, MAE, chi-squared

• information_criteria: AIC, BIC, AICc

• peak_quality: Per-peak purity, overlap, area

• residual_analysis: Autocorrelation, heteroscedasticity, normality tests

• overall_grade: Letter grade (A/B/C/D/F)

• convergence_info: Optimization convergence details

See also

Other peak-deconvolution: add_param_jitter(), check_quality_gates(), initialize_peak_params(), optimize_deconvolution()

Examples

# Create synthetic data and fit
x <- seq(0, 20, by = 0.1)
true_y <- 1.5 * exp(-0.5 * ((x - 8) / 1)^2) +
  0.8 * exp(-0.5 * ((x - 12) / 1.5)^2)
y <- true_y + rnorm(length(x), sd = 0.05)

models <- list(gaussian_peak_model(), gaussian_peak_model())
init_params <- list(
  list(height = 1.2, center = 7.5, width = 1.2),
  list(height = 0.6, center = 12.5, width = 1.8)
)

result <- optimize_deconvolution(x, y, models, init_params)
quality <- assess_deconv_quality(x, y, result, models)
print(quality)
#> Deconvolution Quality Assessment
#> ================================
#> 
#> Overall Grade: B (good)
#> 
#> Goodness of Fit:
#>   R-squared: 0.9868
#>   RMSE:      0.0510
#>   MAE:       0.0400
#> 
#> Optimization:
#>   Converged:   Yes
#>   Iterations:  20
#>   Optimizer:   lbfgsb
#>   Final SSE:   0.5231