LightLogR (version 0.3.8)

nvRD: 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")

Value

A numeric vector containing the nvRD 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::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.

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(), 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)
  )