The function all_metrics runs all of the iglu metrics, and returns the results with one column per metric.
Usage
all_metrics(
data,
dt0 = NULL,
inter_gap = 45,
tz = "",
timelag = 15,
lag = 1,
metrics_to_include = c("all", "consensus_only")
)Arguments
- data
DataFrame object with column names "id", "time", and "gl".
- 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
as.POSIXct), but " " is the current time zone, and "GMT" is UTC (Universal Time, Coordinated). Invalid values are most commonly treated as UTC, on some platforms with a warning.- timelag
Integer indicating the time period (# minutes) over which rate of change is calculated. Default is 15, e.g. rate of change is the change in glucose over the past 15 minutes divided by 15.
- lag
Integer indicating which lag (# days) to use. Default is 1.
- metrics_to_include
Returns all metrics computed by iglu or all on the consensus list (Battelino 2023)
Details
All iglu functions are calculated within the all_metrics function, and the resulting tibble is returned with one row per subject and a column for each metric. Time dependent functions are calculated together using the function optimized_iglu_functions with two exceptions: PGS and episodes are calculated within all_metrics because their structure does not align with optimized_iglu_functions. Note that episodes related outputs included in all_metrics are only average episodes per day. To get the average duration and glucose, please use the standalone episodes function
For metric specific information, please see the corresponding function documentation.
References
Battelino T, Alexander CM, Amiel SA, et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol. 2023;11(1):42-57. doi:10.1016/S2213-8587(22)00319-9 .
# Specify the meter frequency and change the interpolation gap to 30 min all_metrics(example_data_1_subject, dt0 = 5, inter_gap = 30)
Examples
data(example_data_1_subject)
all_metrics(example_data_1_subject)
#> # A tibble: 1 × 62
#> id ADRR COGI CV eA1C hypo_lv1 hypo_lv2 hypo_extended hyper_lv1
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Subject 1 15.1 93.0 26.9 5.94 0.0899 0 0 1.44
#> # ℹ 53 more variables: hyper_lv2 <dbl>, hypo_lv1_excl <dbl>,
#> # hyper_lv1_excl <dbl>, GMI <dbl>, GRI <dbl>, GRADE <dbl>, GRADE_eugly <dbl>,
#> # GRADE_hyper <dbl>, GRADE_hypo <dbl>, HBGI <dbl>, LBGI <dbl>,
#> # hyper_index <dbl>, hypo_index <dbl>, IGC <dbl>, IQR <dbl>, J_index <dbl>,
#> # M_value <dbl>, MAD <dbl>, MAGE <dbl>, active_percent <dbl>, ndays <drtn>,
#> # start_date <dttm>, end_date <dttm>, above_140 <dbl>, above_180 <dbl>,
#> # above_250 <dbl>, below_54 <dbl>, below_70 <dbl>, in_range_63_140 <dbl>, …