LightLogR (version 0.3.8)

nvRC: Non-visual circadian response

Description

This function calculates the non-visual circadian response (nvRC). It takes into account the assumed response dynamics of the non-visual system and the circadian rhythm and processes the light exposure signal to quantify the effective circadian-weighted input to the non-visual system (see Details).

Usage

nvRC(
  MEDI.vector,
  Illuminance.vector,
  Time.vector,
  epoch = "dominant.epoch",
  sleep.onset = NULL
)

Value

A numeric vector containing the nvRC data. The output has the same length as Time.vector.

Arguments

MEDI.vector

Numeric vector containing the melanopic EDI data.

Illuminance.vector

Numeric vector containing the Illuminance data.

Time.vector

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

epoch

The epoch at which the data was sampled. Can be either a duration or a string. If it is a string, it needs to be either "dominant.epoch" (the default) for a guess based on the data, or a valid duration string, e.g., "1 day" or "10 sec".

sleep.onset

The time of habitual sleep onset. Can be HMS, numeric, or NULL. If NULL (the default), then the data is assumed to start at habitual sleep onset. If Time.vector is HMS or POSIXct, sleep.onset must be HMS. Likewise, if Time.vector is numeric, sleep.onset must be numeric.

Details

The timeseries is assumed to be regular. Missing values in the light data will be replaced by 0.

References

Amundadottir, M.L. (2016). Light-driven model for identifying indicators of non-visual health potential in the built environment [Doctoral dissertation, EPFL]. EPFL infoscience. tools:::Rd_expr_doi("10.5075/epfl-thesis-7146")

See Also

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

Examples

dataset1 <-
  tibble::tibble(
    Id = rep("B", 60 * 48),
    Datetime = lubridate::as_datetime(0) + lubridate::minutes(0:(60*48-1)),
    Illuminance = c(rep(0, 60*8), rep(sample(1:1000, 16, replace = TRUE), each = 60),
                    rep(0, 60*8), rep(sample(1:1000, 16, replace = TRUE), each = 60)),
    MEDI = Illuminance * rep(sample(0.5:1.5, 48, replace = TRUE), each = 60)
  )
# Time.vector as POSIXct
dataset1.nvRC <- dataset1 %>%
  dplyr::mutate(
    nvRC = nvRC(MEDI, Illuminance, Datetime, sleep.onset = hms::as_hms("22:00:00"))
  )

# Time.vector as difftime
dataset2 <- dataset1 %>% 
  dplyr::mutate(Datetime = Datetime - lubridate::as_datetime(lubridate::dhours(22)))
dataset2.nvRC <- dataset2 %>%
  dplyr::mutate(
    nvRC = nvRC(MEDI, Illuminance, Datetime, sleep.onset = lubridate::dhours(0))
  )