mod_grid: Modified GRID Analysis

In cgmguru: Advanced Continuous Glucose Monitoring Analysis with High-Performance C++ Backend

Modified GRID Analysis

Description

Constructs a modified GRID series by reapplying the GRID logic with a designated gap (e.g., 60 minutes) and analysis window in hours (e.g., 2 hours). It reassigns GRID events under these constraints to produce a modified grid suitable for downstream maxima mapping and episode analysis.

Usage

mod_grid(df, grid_point_df, hours = 2, gap = 15)

Arguments

df	A dataframe containing continuous glucose monitoring (CGM) data. Must include columns: id: Subject identifier (string or factor) time: Time of measurement (POSIXct) gl: Glucose value (integer or numeric, mg/dL)
grid_point_df	A dataframe with column start_index (start points for re-applied GRID)
hours	Time window in hours for analysis (default: 2)
gap	Gap threshold in minutes for event detection (default: 15). This parameter defines the minimum time interval between consecutive GRID events.

Value

A list containing:

• mod_grid_vector: Tibble with modified GRID results (mod_grid)

• episode_counts: Tibble with episode counts per subject (id, episode_counts)

• episode_start: Tibble with all episode starts with columns:

  • id: Subject identifier

  • time: Timestamp at which the event occurs; equivalent to df$time[index]

  • gl: Glucose value at the event; equivalent to df$gl[index]

  • index: R-based (1-indexed) row number(s) in df denoting where the event occurs

Units and sampling

- gap is minutes; hours is hours; time is POSIXct.

References

Park, Sang Ho, et al. "Identification of clinically meaningful automatically detected postprandial glucose excursions in individuals with type 1 diabetes using personal continuous glucose monitoring." Diabetes Research and Clinical Practice (2025): 112951.

Park, Soojin, et al. "High-Amplitude and Prolonged Glucose Excursions as a Key Determinant of Discordance Between Glucose Management Indicator and Glycated Hemoglobin in Type 1 Diabetes." Diabetes Care (2026): dc252820. https://doi.org/10.2337/dc25-2820

See Also

grid, find_max_after_hours, find_new_maxima

Other GRID pipeline: detect_between_maxima(), find_local_maxima(), find_max_after_hours(), find_max_before_hours(), find_min_after_hours(), find_min_before_hours(), find_new_maxima(), grid(), maxima_grid(), start_finder(), transform_df()

Examples

# Load sample data
library(iglu)
data(example_data_5_subject)
data(example_data_hall)

# First, get grid points
grid_result <- grid(example_data_5_subject, gap = 60, threshold = 130)

# Perform modified GRID analysis
mod_result <- mod_grid(example_data_5_subject, grid_result$grid_vector, hours = 2, gap = 60)
print(paste("Modified grid points:", nrow(mod_result$mod_grid_vector)))

# Modified analysis with different parameters
mod_result_1h <- mod_grid(example_data_5_subject, grid_result$grid_vector, hours = 1, gap = 40)

# Analysis on larger dataset
large_grid <- grid(example_data_hall, gap = 60, threshold = 130)
large_mod_result <- mod_grid(example_data_hall, large_grid$grid_vector, hours = 2, gap = 60)
print(paste("Modified grid points in larger dataset:", nrow(large_mod_result$mod_grid_vector)))

cgmguru documentation built on July 8, 2026, 9:06 a.m.