LightLogR (version 0.3.8)

exponential_moving_average: Exponential moving average filter (EMA)

Description

This function smoothes the data using an exponential moving average filter with a specified decay half-life.

Usage

exponential_moving_average(
  Light.vector,
  Time.vector,
  decay = "90 min",
  epoch = "dominant.epoch"
)

Value

A numeric vector containing the smoothed light data. The output has the same length as Light.vector.

Arguments

Light.vector

Numeric vector containing the light data. Missing values are replaced by 0.

Time.vector

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

decay

The decay half-life controlling the exponential smoothing. Can be either a duration or a string. If it is a string, it needs to be a valid duration string, e.g., "1 day" or "10 sec". The default is set to "90 mins" for a biologically relevant estimate (see the reference paper).

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".

Details

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

References

Price, L. L. A. (2014). On the Role of Exponential Smoothing in Circadian Dosimetry. Photochemistry and Photobiology, 90(5), 1184-1192. tools:::Rd_expr_doi("10.1111/php.12282")

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(), centroidLE(), disparity_index(), duration_above_threshold(), 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

sample.data.environment.EMA = sample.data.environment %>%
  dplyr::filter(Id == "Participant") %>%
  filter_Datetime(length = lubridate::days(2)) %>%
  dplyr::mutate(MEDI.EMA = exponential_moving_average(MEDI, Datetime))

# Plot to compare results
sample.data.environment.EMA %>%
  ggplot2::ggplot(ggplot2::aes(x = Datetime)) +
  ggplot2::geom_line(ggplot2::aes(y = MEDI), colour = "black") +
  ggplot2::geom_line(ggplot2::aes(y = MEDI.EMA), colour = "red")