MEAlagplot: Plots the average cross-correlation at different lags

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

Plots the average cross-correlation at different lags

Description

Provides a graphical representation of the comparison between two lists of MEA objects. The X-axis represents the lag values over which cross-correlation was calculated (in seconds), the Y-axis represents the averaged strength of the cross-correlation. Typically, the is useful for a visual inspection of the strength of synchrony from real dyads in relation to synchrony expected by coincidence (pseudosynchrony).

Usage

MEAlagplot(
  mea,
  contrast = F,
  by.group = T,
  sub.line = 0.5,
  mea.alpha = 0.8,
  contrast.alpha = 0.5,
  ...
)

Arguments

mea	a list of MEA objects (see function MEAlist).
contrast	either FALSE or a list of MEA objects to be used as a contrast
by.group	logical. Should the different groups of mea be plotted separately?
sub.line	on which margin line should the 'social presence' subtitle be printed, starting at 0 counting outwards.
mea.alpha	numeric from 0 to 1. The value of opacity of individual lines for the main MEA data. If set to zero, drawing is suppressed to improve performance.
contrast.alpha	numeric from 0 to 1. The value of opacity of individual lines for contrast data. If set to zero, drawing is suppressed to improve performance.
...	further arguments and par parameters passed to plot

Details

A typical application of MEAlagplot is to represent the difference between real dyads and random dyads obtained through a shuffle procedure. It may also be used to see the difference among various filtering procedures or different regions of interest (e.g. head-synchrony versus body-synchrony, female vs. male dyads, etc).

Percentages indicate the relative amount of synchrony where the values are higher than the contrast sample.

Examples

## This example is excluded from test as it takes more than 10s to run
## 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_normal <- readMEA(path_normal, sampRate = 25, s1Col = 1, s2Col = 2,
                     s1Name = "Patient", s2Name = "Therapist",
                     idOrder = c("id","session"), idSep="_", skip=1, nrow = 6000)
mea_normal <- setGroup(mea_normal, "normal")

## read the dropout sample (intake interviews of patients that dropped out)
path_dropout <- system.file("extdata/dropout", package = "rMEA")
mea_dropout <- readMEA(path_dropout, sampRate = 25, s1Col = 1, s2Col = 2,
                     s1Name = "Patient", s2Name = "Therapist",
                     idOrder = c("id","session"), idSep="_", skip=1, nrow = 6000)
mea_dropout <- setGroup(mea_dropout, "dropout")

## Combine into a single object
mea_all = c(mea_normal, mea_dropout)

## Create a shuffled sample
mea_rand = shuffle(mea_all, 20)

## Compute ccf
mea_all = MEAccf(mea_all, lagSec = 5, winSec = 60, incSec = 30, r2Z = TRUE, ABS = TRUE)
mea_rand = MEAccf(mea_rand, lagSec = 5, winSec = 60, incSec = 30, r2Z = TRUE, ABS = TRUE)

## Visualize the effects:

MEAlagplot(mea_all, contrast = mea_rand, by.group = TRUE)
MEAlagplot(mea_all, contrast = mea_rand, by.group = FALSE, col=c(2,4))

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