MEAsmooth: Moving average smoothing for motion energy data

In kleinbub/rMEA: Synchrony in Motion Energy Analysis (MEA) Time-Series

Moving average smoothing for motion energy data

Description

This function applies a moving average filter, based on SAS "proc expand" procedure. The moving average is applied independently on each subject's motion energy data. NA values are set to 0.

Usage

MEAsmooth(mea, moving.average.win = 0.5)

Arguments

mea	an object of class MEA or a list of MEA objects (see function readMEA)
moving.average.win	numeric. The size of the filter window, in seconds or fractions of seconds.

Value

The filtered object(s)

Examples

## read the first 4 minutes of the normal sample
##   (intake interviews of patients that carried on therapy)
path_normal <- system.file("extdata/normal", package = "rMEA")
mea_raw <- readMEA(path_normal, sampRate = 25, s1Col = 1, s2Col = 2,
                     s1Name = "Patient", s2Name = "Therapist",
                     idOrder = c("id","session"), idSep="_", skip=1, nrow = 6000)

## filter with moving average
mea_filter = MEAsmooth(mea_raw)

## assign groups names
mea_raw <- setGroup(mea_raw, "raw")
mea_filter <- setGroup(mea_filter, "filtered")

## Compute ccf
mea_raw <- MEAccf(mea_raw, lagSec = 5, winSec = 60, incSec = 30, r2Z = TRUE, ABS = FALSE)
mea_filter <- MEAccf(mea_filter, lagSec = 5, winSec = 60, incSec = 30, r2Z = TRUE, ABS = FALSE)

## Compare the effect of filtering on ccf
MEAlagplot(mea_filter, contrast = mea_raw)

kleinbub/rMEA documentation built on Dec. 15, 2024, 8:33 p.m.