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Interdaily stability (IS)

Source: R/metric_interdaily_stability.R
interdaily_stability.Rd

This function calculates the variability of 24h light exposure patterns across multiple days. Calculated as the ratio of the variance of the average daily pattern to the total variance across all days. Calculated with mean hourly light levels. Ranges between 0 (Gaussian noise) and 1 (Perfect Stability).

Usage

interdaily_stability(
  Light.vector,
  Datetime.vector,
  na.rm = FALSE,
  as.df = FALSE
)

Arguments

Light.vector

Numeric vector containing the light data.

Datetime.vector

Vector containing the time data. Must be POSIXct.

na.rm

Logical. Should missing values be removed? Defaults to FALSE.

as.df

Logical. Should the output be returned as a data frame? If TRUE, a data frame with a single column named interdaily_stability will be returned. Defaults to FALSE.

Value

Numeric value or dataframe with column 'IS'.

Details

Note that this metric will always be 1 if the data contains only one 24 h day.

References

Van Someren, E. J. W., Swaab, D. F., Colenda, C. C., Cohen, W., McCall, W. V., & Rosenquist, P. B. (1999). Bright Light Therapy: Improved Sensitivity to Its Effects on Rest-Activity Rhythms in Alzheimer Patients by Application of Nonparametric Methods. Chronobiology International, 16(4), 505–518. doi:10.3109/07420529908998724

Hartmeyer, S.L., Andersen, M. (2023). Towards a framework for light-dosimetry studies: Quantification metrics. Lighting Research & Technology. doi:10.1177/14771535231170500

See also

Other metrics: bright_dark_period(), centroidLE(), disparity_index(), duration_above_threshold(), exponential_moving_average(), frequency_crossing_threshold(), intradaily_variability(), midpointCE(), nvRC(), nvRD(), nvRD_cumulative_response(), period_above_threshold(), pulses_above_threshold(), threshold_for_duration(), timing_above_threshold()

Examples


set.seed(1)
N <- 24 * 7
# Calculate metric for seven 24 h days with two measurements per hour
dataset1 <-
  tibble::tibble(
    Id = rep("A", N * 2),
    Datetime = lubridate::as_datetime(0) + c(lubridate::minutes(seq(0, N * 60 - 30, 30))),
    MEDI = sample(1:1000, N * 2)
  )
dataset1 %>%
  dplyr::summarise(
    "Interdaily stability" = interdaily_stability(MEDI, Datetime)
  )
#> # A tibble: 1 × 1
#>   `Interdaily stability`
#>                    <dbl>
#> 1                  0.153