plot.precision_profile: Plot method for precision_profile objects

Description

Creates publication-ready visualization of precision profile results, showing CV vs concentration with the fitted model curve.

Usage

# S3 method for precision_profile
plot(
  x,
  show_ci = TRUE,
  show_targets = TRUE,
  show_points = TRUE,
  point_alpha = 0.8,
  point_size = 3,
  line_colors = NULL,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  log_x = FALSE,
  ...
)
# S3 method for precision_profile
autoplot(
  object,
  show_ci = TRUE,
  show_targets = TRUE,
  show_points = TRUE,
  point_alpha = 0.8,
  point_size = 3,
  line_colors = NULL,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  log_x = FALSE,
  ...
)

Value

A ggplot object that can be further customized.

Arguments

x: An object of class precision_profile.
show_ci: Logical; if TRUE (default), displays prediction interval bands for the fitted curve.
show_targets: Logical; if TRUE (default), displays horizontal lines at functional sensitivity target CV values.
show_points: Logical; if TRUE (default), displays the observed data points.
point_alpha: Numeric; transparency of points (0-1, default: 0.8).
point_size: Numeric; size of points (default: 3).
line_colors: Named character vector with colors for "fitted", "ci", and "target". Defaults to a clean color scheme.
title: Character; plot title. If NULL (default), generates an automatic title.
xlab: Character; x-axis label. If NULL, uses "Concentration".
ylab: Character; y-axis label. If NULL, uses "CV (%)".
log_x: Logical; if TRUE, uses logarithmic scale for x-axis (default: FALSE).
...: Additional arguments (currently ignored).
object: An object of class precision_profile.

Details

The precision profile plot displays:

Observed points: CV values at each tested concentration
Fitted curve: Model-predicted CV across the concentration range
Prediction intervals: Confidence bands showing uncertainty
Target lines: Horizontal lines at functional sensitivity thresholds

The plot helps visualize:

How measurement precision changes with concentration
Model fit quality (points should follow the curve)
Functional sensitivity estimates (intersection of curve with target lines)

Examples

Run this code

# See ?precision_profile for complete examples

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