BioThermR (version 0.1.0)

plot_thermal_density: Visualize Temperature Distribution (Density Plot)

Description

Generates a probability density plot of the temperature values within the image. This visualization is critical for assessing the homogeneity of the subject's temperature and identifying potential artifacts (e.g., bimodal distributions often indicate poor background removal).

Usage

plot_thermal_density(
  img_obj,
  use_processed = TRUE,
  show_peak = TRUE,
  show_max = TRUE,
  show_min = TRUE,
  digits = 2,
  color = "skyblue",
  point_size = 2,
  point_color = "red",
  point_label_color = "black",
  point_label_size = 2
)

Value

A ggplot object. Layers can be added subsequently.

Arguments

img_obj

A 'BioThermR' object.

use_processed

Logical. If TRUE (default), uses the 'processed' matrix (masked data). If FALSE, uses the 'raw' matrix.

show_peak

Logical. If TRUE, highlights and labels the peak density value (Mode). Default is TRUE.

show_max

Logical. If TRUE, highlights and labels the maximum temperature value. Default is TRUE.

show_min

Logical. If TRUE, highlights and labels the minimum temperature value. Default is TRUE.

digits

Integer. Number of decimal places to round the labels to. Default is 2.

color

String. Fill color for the density area curve. Default is "skyblue".

point_size

Numeric. Size of the points marking Peak/Min/Max. Default is 2.

point_color

String. Color of the points marking Peak/Min/Max. Default is "red".

point_label_color

String. Color of the text labels. Default is "black".

point_label_size

Numeric. Size of the text labels. Default is 2.

Details

The function computes the kernel density estimate of the valid pixels (ignoring NAs). It can optionally annotate key statistical landmarks:

• Peak: The mode of the distribution (most frequent temperature).

• Max/Min: The hottest and coldest points in the ROI.

Text labels are automatically repelled using 'ggrepel' to ensure they do not overlap.

Examples

# \donttest{
# Load raw data
img_obj <- system.file("extdata", "C05.raw", package = "BioThermR")
img <- read_thermal_raw(img_obj)

# Apply automated segmentation
img <- roi_segment_ebimage(img, keep_largest = TRUE)

# Density plot
plot_thermal_density(img)
# }