Detection of potential artefacts in CCF results.

Description

High synchronization values for extended time durations may be the effect of artefacts in the MEA data. For instance subject 2 movement may have been captured by subject 1's camera (or ROI) as well, or some environment characteristic (e.g. light) is changing for both cameras (or ROIs). This function identifies those moments allowing to inspect the original videos with temporal precision. **Please note that is impossible to discriminate real high synchronization phenomena from artefacts without inspecting the original videos.**

Usage

CCFartefacts(mea, threshold, delta = 1, duration = attributes(mea)$ccf$inc)

Arguments

Details

The function only considers lag_zero correlations as MEA artefacts are expected to be non-lagged phenomena.

Value

a data.frame object with all potential artefact epochs

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_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 <- MEAccf(mea_normal, lagSec = 5, winSec = 30, incSec = 10, ABS = FALSE)

## find potential artefacts with various granularity of reporting
print(CCFartefacts(mea_normal, threshold = 0.2, delta=1))
print(CCFartefacts(mea_normal, threshold = 0.2, delta=5))
print(CCFartefacts(mea_normal, threshold = 0.2, delta=5, duration=60))

MEA class constructor

Description

The preferred way to create an object of class MEA is through the function readMEA.

Usage

MEA(
  dataframe,
  sampRate,
  filter = "raw",
  id,
  session,
  group,
  s1Name,
  s2Name,
  uid = paste(group, id, session, sep = "_")
)

is.MEA(x)

Arguments

Details

It is advised to not create the MEA object manually but to always use the function readMEA instead.

Value

A list containing three objects:

MEA: the dataframe containing the motion energy data

ccf: the matrix of lagged cross-correlations between s1 and s2 (if MEAccf was run)

ccfRes: some useful row marginals

is.MEA returns TRUE if and only if its argument is of class MEA

Moving-windows lagged cross-correlation routine for MEA objects

Description

This function analyzes a bivariate MEA signal represented by two time-series (subject 1 "s1", subject 2 "s2") resulting from a dyadic interaction. MEAccf performs windowed cross-correlations with specified increments. The cross-correlation analysis is repeated for each lag step, with discrete increments of 1 sample in both directions.

Usage

MEAccf(mea, lagSec, winSec, incSec, r2Z = T, ABS = T)

Arguments

Details

The choice of lagSec depends on the type of synchronization expected from the specific interaction. In the literature, lags of ±5 seconds have been reported by multiple authors. Function MEAlagplot can be used for visual inspection of the appropriateness of the chosen lag.

The choice of winSec represents the temporal resolution of the analysis. The combination of incSec and winSec settings has a big impact on the results. These parameters should be chosen carefully, guided by theoretical and empirical considerations.

If r2Z is TRUE, values of Fisher's Z are constrained to an upper bound of 10.

Using absolute values (ABS) treats positive and negative cross-correlations as equal. The underlying assumption is that both simultaneous movement (positive correlation) and when one subject accelerates and the other decelerates (negative correlation), are both signs of interrelatedness and should thus contribute equally to overall synchrony.

Value

The function returns a copy of the mea object in which the ccf and ccfRes objects are populated. mea$ccf includes the complete lagged cross-correlation table for each window and each lag of S1 and S2 MEA signals. mea$ccfRes contains various aggregate values, typically used in research:

• lag_zero: a numeric vector containing for each window, the non-lagged cross-correlation value.

• all_lags: a numeric vector containing for each window, the average across all lags.

• s1_lead/s2_lead: a numeric vector containing for each window, the average of positive/negative lags, summing up the strength of S1/S2 in "leading" the synchronization.

• s1_lead_0/s2_lead_0: the same as s1_lead/s2_lead, but including lag_zero values in the average.

• bestLag: for each window, the lag value (in seconds) that has the highest correlation value.

• grandAver: a single numeric value of the grand-average of the whole cross-correlation table.

• winTimes: a data frame containing the start and end times of each window in the format hh:mm:ss

Examples

## read a single file
path_normal <- system.file("extdata/normal/200_01.txt", package = "rMEA")
mea_normal <- readMEA(path_normal, sampRate = 25, s1Col = 1, s2Col = 2,
                     s1Name = "Patient", s2Name = "Therapist", skip=1,
                     idOrder = c("id","session"), idSep="_")

## perform ccf analysis
mea_ccf = MEAccf(mea_normal, lagSec = 5, winSec = 60, incSec = 30, r2Z = TRUE, ABS = TRUE)
summary(mea_ccf)

##extract ccf values
res <- getCCF(mea_ccf, type="grandAver")
print(res)

#visualize the analysis results for the first file
MEAheatmap(mea_ccf[[1]])

Distribution of cross-correlations

Description

Plots the distribution of the average cross-correlations in a list of MEA objects.

Usage

MEAdistplot(
  mea,
  contrast = FALSE,
  by = c("none", "group", "id", "session"),
  by.group = FALSE,
  sub.line = 0.5,
  ...
)

Arguments

Details

If contrast is defined, then a normalized difference (Cohen's d) between the means of each group and the contrast is provided. Otherwise, if the mea object has 3 or less groups, Cohen's d will be calculated on the group differences.

Examples

## This example is excluded from test as it may take 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:

MEAdistplot(mea_all, contrast = mea_rand, by.group = TRUE)

Plot a heatmap of dyadic cross-correlations

Description

Graphical representation of the lagged cross-correlations in time. Provides an intuitive description of synchronization dynamics.

Usage

MEAheatmap(
  mea,
  legendSteps = 10,
  rescale = FALSE,
  colors = c("#F5FBFF", "#86E89E", "#FFF83F", "#E8A022", "#FF3700"),
  bias = 1,
  mirror = TRUE
)

Arguments

Details

The cross-correlation values are rescaled to be in a range from 0 to 1 before plotting.

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, ...)

Arguments

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)

Well formatted list of MEA objects

Description

This constructor function checks if all the supplied MEA objects share the same sampling rate, pre-processing, and metadata, and returns an object with additional attributes summarizing the contained MEA objects.

Usage

MEAlist(listOfMea)

is.MEAlist(x)

Arguments

Value

an object of class MEAlist

is.MEAlist returns TRUE if and only if its argument is of class MEAlist

Apply a function to a single or a list of MEA objects

Description

MEApply is a wrapper to do.call that allows to apply a function on the motion energy data of one or multiple MEA objects. Complex constructions are possible, see details.

Usage

MEAmap(mea, FUN, label = "", ...)

Arguments

Details

FUN will be applied on the motion energy time-series of MEA objects, which is stored as a data frame with 2 columns, respectively for s1 and s2.

Value

an object of the same class of the provided 'mea' object, with the transformed motion energy data

Replace outliers with given values

Description

Sometimes motion energy analysis generates excessively high peaks resulting from video artifacts or other anomalies in the video source.

Usage

MEAoutlier(
  mea,
  threshold = function(x) {     stats::sd(x) * 10 },
  direction = c("greater", "less"),
  replace = NA
)

Arguments

Value

The same mea object with all extreme values substituted.

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)

## Remove extreme values, higher than 10 times the standard deviation
mea_clean = MEAoutlier(mea_raw, threshold=function(x){sd(x)*10}, direction = "greater")

Substitute values from MEA data

Description

This function allows to substitute MEA data from a list of time epochs. This is useful to mark and remove artefacts, or to substitute extreme values.

Usage

MEAreplace(mea, epochs, replacement, filterLabel = "replaced")

Arguments

Details

the artefacts data.frame must contain a "start" and "end" columns, with the boundaries of the epochs that must be marked as artefacts. The start and end values can be either integers (denoting seconds), or string values in the format hh:mm:ss, or mm:ss. Furthermore the data.frame must contain a uid column containing strings in the format "group_id_session", OR three columns group, id, session presenting the information separately. Data identifiers must match those of the mea object.

The data.frame can be either hand crafted, for instance by importing a csv file (see read.table), or generated through the packages own artefact detection tools such as CCFartefacts

Value

returns the same MEA or MEAlist object, with all artefactual data substituted.

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_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 <- MEAccf(mea_normal, lagSec = 5, winSec = 30, incSec = 10, ABS = FALSE)

## find potential artefacts
artefacts = CCFartefacts(mea_normal, threshold = 0.2, delta=1)

##replace values
mea_replaced <- MEAreplace(mea_normal, epochs = artefacts,
                           replace = NA, filterLabel = "artefacts deletion")

#visualize results on first case
plot(mea_normal$all_200_1)
plot(mea_replaced$all_200_1)

Scaling (and centering) of motion energy time-series

Description

Scaling (and centering) of motion energy time-series

Usage

MEAscale(mea, scale = "sd", ..., center = F, removeNA = T)

Arguments

Details

If scale is a function, it is found by a call to match.fun and typically is either a function or a symbol (e.g., a backquoted name) or a character string specifying a function to be searched for from the environment of the call to apply. Note that the chosen function must return a single numeric value.

center is directly passed to scale. If center is TRUE then centering is done by subtracting the means (omitting NAs) from the motion energy time-series. If center is a numeric vector, the first value will be subtracted from s1 and the second from s2. Note: the s1 and s2 signals are scaled independently.

Value

returns the same MEA or MEAlist object, with all motion energy data rescaled

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)

## rescale by factor 0.7
mea_scaled = MEAscale(mea_raw, scale = 0.7)

## rescale with standard deviation
mea_scaled = MEAscale(mea_raw, scale = "sd", removeNA = TRUE)

## assign groups names
mea_raw <- setGroup(mea_raw, "raw")
mea_scaled <- setGroup(mea_scaled, "scaled")

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

## Compare the effect of scaling on ccf
MEAlagplot(mea_scaled, contrast = mea_raw)

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

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)

Extract the names of the ccf analysis summaries in a MEA objects

Description

Extract the names of the ccf analysis summaries in a MEA objects

Usage

ccfResNames(mea)

Arguments

Value

a vector containing the labels of the ccfRes indexes

Cohen's d A simple function to calculate Cohen's d effect size

Description

Cohen's d A simple function to calculate Cohen's d effect size

Usage

cohens_d(x, y)

Arguments

Examples

# Generates two samples with means distance of 1 sd
x = rnorm(1000, mean = 10, sd = 1.5)
y = rnorm(1000, mean = 11.5, sd = 1.5)
# cohen's d should approximate to 1
cohens_d(x,y)

Transform color

Description

Transform color

Usage

colTrans(col, luminosity = NA, alpha = NA)

Arguments

Value

a color string

Plots the initial, middle and ending part of a MEA object

Description

This is typically useful to check if the motion energy time-series are good. The middle section is chosen randomly among possible middle sections.

Usage

diagnosticPlot(mea, width = 60, ...)

Arguments

Details

Motion energy time-series should always be visually inspected for possible artifacts. Periodic peaks or drops in time-series are indicators of e.g. key-frames or duplicated video-frames. For further information regarding the program MEA, please refer to the documentation available at http://www.psync.ch.

Examples

## read a single file
path_normal <- system.file("extdata/normal/200_01.txt", package = "rMEA")
mea_normal <- readMEA(path_normal, sampRate = 25, s1Col = 1, s2Col = 2,
                     s1Name = "Patient", s2Name = "Therapist", skip=1,
                     idOrder = c("id","session"), idSep="_")
## Visual inspection of the data
diagnosticPlot(mea_normal[[1]])

Extract ccf values from MEA objects

Description

Extract ccf values from MEA objects

Usage

getCCF(mea, type)

Arguments

Value

If type="matrix", the ccf matrix with discrete lag-seconds is returned. If type="fullMatrix", the whole ccf matrix with all lags is returned. Otherwise a vector containing the ccf time-series for the selected lag, or aggregated values is returned. If mea is a list, the return value is a list of the individual ccf of each MEA object.

Get MEA attributes

Description

Get MEA attributes

Usage

id(mea)

group(mea)

session(mea)

sampRate(mea)

s1Name(mea)

s2Name(mea)

uid(mea)

Arguments

Details

if a well formatted list of MEA objects is provided, the function returns a vector of results for id, session, group and uid. sampRate, s1Name, and s2Name return always a single value, as they are not allowed to be mixed.

Value

A string or a vector of strings containing the metadata.

Extract the lag names of a ccf analysis in MEA objects

Description

Extract the lag names of a ccf analysis in MEA objects

Usage

lagNames(mea)

Arguments

Value

a vector containing the labels of the lag values

Adds lines of a MEA object to a Plot

Description

Adds lines of a MEA object to a Plot

Usage

## S3 method for class 'MEA'
lines(x, from = 0, to = NULL, duration = NULL, ccf = F, rescale = F, ...)

Arguments

Details

Note: if more of than 10s of trailing zeroes are found at the end of both s1 and s2 signals they are truncated.

Examples

## read a single file
path_normal <- system.file("extdata/normal/200_01.txt", package = "rMEA")
mea_normal <- readMEA(path_normal, sampRate = 25, s1Col = 1, s2Col = 2,
                     s1Name = "Patient", s2Name = "Therapist", skip=1,
                     idOrder = c("id","session"), idSep="_")
mea_normal <- MEAccf(mea_normal, lagSec = 5, winSec = 30, incSec = 10, ABS = FALSE)
mea_smoothed <- MEAsmooth(mea_normal)
## Visual inspection of the data
plot(mea_normal[[1]], from = 240, duration=20)
lines(mea_smoothed[[1]], from = 240, duration=20, lty=3, col=c(1,2))

Plots an object of class MEA

Description

Plots an object of class MEA

Usage

## S3 method for class 'MEA'
plot(x, from = 0, to = NULL, duration = NULL, ccf = F, rescale = F, ...)

Arguments

Details

Note: if more of than 10s of trailing zeroes are found at the end of both s1 and s2 signals they are truncated.

Examples

## read a single file
path_normal <- system.file("extdata/normal/200_01.txt", package = "rMEA")
mea_normal <- readMEA(path_normal, sampRate = 25, s1Col = 1, s2Col = 2,
                     s1Name = "Patient", s2Name = "Therapist", skip=1,
                     idOrder = c("id","session"), idSep="_")
mea_normal <- MEAccf(mea_normal, lagSec = 5, winSec = 30, incSec = 10, ABS = FALSE)
## Visual inspection of the data
plot(mea_normal[[1]], from = 60, to = "2:00")
plot(mea_normal[[1]], from = 0, duration = "5:00")

#' ## Visualize CCF inspection of the data
plot(mea_normal[[1]], from = 0, duration = "2:00", ccf = "lag_zero", rescale=TRUE)

Import MEA raw data

Description

readMEA reads the output of MEA software.

Usage

readMEA(
  path,
  s1Col,
  s2Col,
  sampRate,
  namefilt = NA,
  s1Name = "s1",
  s2Name = "s2",
  idOrder = c("id", "session", "group"),
  idSep = "_",
  removeShortFiles = NULL,
  ...
)

Arguments

Details

For instance if s1Col = c(1,3) and s2Col = c(2,4), the returned values will be the sum of column 1 and 3 for the first participant and columns 2 and 4 for the second one.

Value

an object of class MEAlist

Examples

## read the first 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", skip=1,
                     idOrder = c("id","session"), idSep="_")
mea_normal <- setGroup(mea_normal, "normal")

## read the second 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", skip=1,
                     idOrder = c("id","session"), idSep="_")
mea_dropout <- setGroup(mea_dropout, "dropout")

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

summary(mea_all)

Sets the group of MEA objects

Description

Sets the group of MEA objects

Usage

setGroup(mea, group)

Arguments

Value

an object of the same type of 'mea', with the group attributes set to group.

Examples

## read a sample
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)
mea_normal <- setGroup(mea_normal, "normal")

Shuffle MEA data (between subjects)

Description

This function recombines the s1 and s2 motion energy time-series between all MEA objects in the supplied list. It is typically used to compare genuine synchrony of real data with pseudosynchrony of shuffled (recombined) data.

Usage

shuffle(mea, size = "max", keepRoles = FALSE)

Arguments

Details

The shuffling process first creates all possible combinations between s1 and s2 of all MEA objects in the supplied list, then removes the original pairings, and finally extracts the desired numbers of dyads without replacement.

Note: all the ccf data, if present, are discarded from the shuffled objects and have to be calculated again using MEAccf

Value

an object of class MEAlist containing randomly combined dyads.

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_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")

## Create a shuffled sample
mea_rand = shuffle(mea_normal, 50)

summary(mea_rand)

Shuffle MEA data (within subjects)

Description

This function generates fakes dyads to be used for pseudosynchrony calculations following the Ramseyer & Tschacher (2010) within-subject segment shuffling approach. Between subjects shuffling shuffle is probably more conservative, and suggested for most cases. This function is provided for replicability of older studies, and can be useful to quickly assess pseudosynchrony in single sessions, or very small samples.

Usage

shuffle_segments(mea, n_each, segSec)

Arguments

Details

For each MEA object, the shuffling procedure first divides s1 and s2 MEA data in segments of size segSec, then shuffles them within subject (so that the new segments of s1, are the old segments of s1 in a new order). This is repeated for n_each times, before getting to the next MEA object

Note: all the ccf data, if present, are discarded from the shuffled objects and have to be calculated again using MEAccf

Value

an object of class MEAlist containing n_each * length(mea) random dyads.

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_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")

## Create a shuffled sample
mea_rand = shuffle_segments(mea_normal, n_each=10, segSec=30)

summary(mea_rand)

Transform time values between different formats

Description

This function allows to

Usage

timeMaster(
  baseTime,
  add = 0,
  out = c("auto", "hour", "min", "sec"),
  baseSep = "[\\.,:,\\,',-,\"]"
)

Arguments

Examples

## Adding seconds to minutes
timeMaster(30, add="10:00", out = "min")

## Adding strings to integer seconds and returning a numeric value
timeMaster(30, add="10:00")

## Automatic detection of format
timeMaster("01:30:55",add="10:00",out = "auto")

Exports analyzed MEA data to .txt files

Description

Exports analyzed MEA data to .txt files

Usage

writeMEA(mea, save.directory, what = c("mea", "ccf"), ...)

Arguments

Details

'mea' exports the (filtered) MEA data. 'ccf' instead exports the cross-correlation matrix together with all summarizing statistics calculated by MEAccf.

Examples

## This example is excluded from test as it takes more than 10s to run
## define a regex filter for the filenames to be read
to_read = c("20[123]_*")

## read the first 4 minutes of the files
path_normal <- system.file("extdata/normal", package = "rMEA")
mea_normal <- readMEA(path_normal, namefilt = to_read, sampRate = 25, s1Col = 1, s2Col = 2,
                     s1Name = "Patient", s2Name = "Therapist",
                     idOrder = c("id","session"), idSep = "_",  skip = 1, nrow = 6000)

## perform ccf analysis
mea_ccf = MEAccf(mea_normal, lagSec = 5, winSec = 60, incSec = 30, r2Z = TRUE, ABS = TRUE)

## export data and analysis
save_path = tempdir()
writeMEA(mea_ccf, save.directory = save_path, what="ccf")