nvRD {LightLogR}

Non-visual direct response

Description

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

Usage

nvRD(MEDI.vector, Illuminance.vector, Time.vector, epoch = "dominant.epoch")

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::hms(), lubridate::duration(), difftime().

epoch

The epoch at which the data was sampled. Can be either a lubridate::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 lubridate::duration() string, e.g., "1 day" or "10 sec".

Details

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

Value

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

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. 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(), nvRC(), nvRD_cumulative_response(), period_above_threshold(), pulses_above_threshold(), threshold_for_duration(), timing_above_threshold()

Examples

# Dataset 1 with 24h measurement
dataset1 <-
  tibble::tibble(
    Id = rep("A", 60 * 24),
    Datetime = lubridate::as_datetime(0) + lubridate::minutes(0:(60*24-1)),
    Illuminance = c(rep(0, 60*8), rep(sample(1:1000, 16, replace = TRUE), each = 60)),
    MEDI = Illuminance * rep(sample(0.5:1.5, 24, replace = TRUE), each = 60)
  ) 
# Dataset 2 with 48h measurement
dataset2 <-
  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)
  )
# Combined datasets
dataset.combined <- rbind(dataset1, dataset2)

# Calculate nvRD per ID
dataset.combined.nvRD <- dataset.combined %>% 
  dplyr::group_by(Id) %>% 
  dplyr::mutate(
    nvRD = nvRD(MEDI, Illuminance, Datetime)
  )