LightLogR (version 0.3.8)

centroidLE: Centroid of light exposure

Description

This function calculates the centroid of light exposure as the mean of the time vector weighted in proportion to the corresponding binned light intensity.

Usage

centroidLE(
  Light.vector,
  Time.vector,
  bin.size = NULL,
  na.rm = FALSE,
  as.df = FALSE
)

Value

Single column data frame or vector.

Arguments

Light.vector

Numeric vector containing the light data.

Time.vector

Vector containing the time data. Can be POSIXct, hms, duration, or difftime.

bin.size

Value specifying size of bins to average the light data over. Must be either a duration or a duration string, e.g., "1 day" or "10 sec". If nothing is provided, no binning will be performed.

na.rm

Logical. Should missing values be removed for the calculation? 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 centroidLE will be returned. Defaults to FALSE.

References

Phillips, A. J. K., Clerx, W. M., O’Brien, C. S., Sano, A., Barger, L. K., Picard, R. W., Lockley, S. W., Klerman, E. B., & Czeisler, C. A. (2017). Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing. Scientific Reports, 7(1), 3216. tools:::Rd_expr_doi("10.1038/s41598-017-03171-4")

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

See Also

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

Examples

# Dataset with POSIXct time vector
dataset1 <-
  tibble::tibble(
    Id = rep("A", 24),
    Datetime = lubridate::as_datetime(0) + lubridate::hours(0:23),
    MEDI = c(rep(1, 6), rep(250, 13), rep(1, 5))
  )
dataset1 %>%
  dplyr::reframe(
    "Centroid of light exposure" = centroidLE(MEDI, Datetime, "2 hours")
  )

# Dataset with hms time vector
dataset2 <-
  tibble::tibble(
    Id = rep("A", 24),
    Time = hms::as_hms(lubridate::as_datetime(0) + lubridate::hours(0:23)),
    MEDI = c(rep(1, 6), rep(250, 13), rep(1, 5))
  )
dataset2 %>%
  dplyr::reframe(
    "Centroid of light exposure" = centroidLE(MEDI, Time, "2 hours")
  )

# Dataset with duration time vector
dataset3 <-
  tibble::tibble(
    Id = rep("A", 24),
    Hour = lubridate::duration(0:23, "hours"),
    MEDI = c(rep(1, 6), rep(250, 13), rep(1, 5))
  )
dataset3 %>%
  dplyr::reframe(
    "Centroid of light exposure" = centroidLE(MEDI, Hour, "2 hours")
  )