igc: Calculate Index of Glycemic Control
In stevebroll/iglu: Interpreting Glucose Data from Continuous Glucose Monitors

Description Usage Arguments Details Value References Examples

The function igc produces IGC values in a tibble object.

1	igc(data, LLTR = 80, ULTR = 140, a = 1.1, b = 2, c = 30, d = 30)

`data`	DataFrame object with column names "id", "time", and "gl", or numeric vector of glucose values.
`LLTR`	Lower Limit of Target Range, default value is 80 mg/dL.
`ULTR`	Upper Limit of Target Range, default value is 140 mg/dL.
`a`	Exponent, generally in the range from 1.0 to 2.0, default value is 1.1.
`b`	Exponent, generally in the range from 1.0 to 2.0, default value is 2.
`c`	Scaling factor, to display Hyperglycemia Index, Hypoglycemia Index, and IGC on approximately the same numerical range as measurements of HBGI, LBGI and GRADE, default value is 30.
`d`	Scaling factor,to display Hyperglycemia Index, Hypoglycemia Index, and IGC on approximately the same numerical range as measurements of HBGI, LBGI and GRADE, default value is 30.

A tibble object with 1 row for each subject, a column for subject id and a column for the IGC values is returned.

IGC is calculated by taking the sum of the Hyperglycemia Index and the Hypoglycemia index. See hypo_index and hyper_index.

A tibble object with two columns: subject id and corresponding IGC value.

Rodbard (2009) Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control, Diabetes Technology and Therapeutics 11 .55-67, doi: 10.1089/dia.2008.0132.

data(example_data_1_subject)
igc(example_data_1_subject)
igc(example_data_1_subject, ULTR = 160)

data(example_data_5_subject)
igc(example_data_5_subject)
igc(example_data_5_subject, LLTR = 75, ULTR = 150)