Description Usage Arguments Details Value References Examples

The function cv_measures produces CV subtype values in a tibble object.

1 | ```
cv_measures(data, dt0 = NULL, inter_gap = 45, tz = "" )
``` |

`data` |
DataFrame object with column names "id", "time", and "gl". Should only be data for 1 subject. In case multiple subject ids are detected, the warning is produced and only 1st subject is used. |

`dt0` |
The time frequency for interpolation in minutes, the default will match the CGM meter's frequency (e.g. 5 min for Dexcom). |

`inter_gap` |
The maximum allowable gap (in minutes) for interpolation. The values will not be interpolated between the glucose measurements that are more than inter_gap minutes apart. The default value is 45 min. |

`tz` |
A character string specifying the time zone to be used. System-specific (see |

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

Missing values will be linearly interpolated when close enough to non-missing values.

CVmean:

Calculated by first taking the coefficient of variation of each day's glucose measurements, then taking the mean of all the coefficient of variation. That is, for x days we compute cv_1 ... cv_x daily coefficient of variations and calculate

*1/x * ∑ [(cv_i)]*CVsd:

Calculated by first taking the coefficient of variation of each day's glucose measurements, then taking the standard deviation of all the coefficient of variations. That is, for d days we compute cv_1 ... cv_d daily coefficient of variations and calculate SD([cv_1, cv_2, ... cv_d])

When a data.frame object is passed, then a tibble object with three columns: subject id and corresponding CV subtype values is returned.

Umpierrez, et.al. (2018) Glycemic Variability: How to Measure and Its Clinical
Implication for Type 2 Diabetes
*The American Journal of Medical Sciences* **356** .518-527,
doi: 10.1016/j.amjms.2018.09.010.

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