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Modified GRID Analysis

Source: R/cgmguru-functions-docs.R
mod_grid.Rd

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)))
#> [1] "Modified grid points: 13866"

# 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)))
#> [1] "Modified grid points in larger dataset: 34890"