MEAdistplot {rMEA} | R Documentation |
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
mea |
a well formatted list of |
contrast |
either FALSE or a list of |
by |
Either "none", "group", "id", or "session". Defines the grouping to be used. |
by.group |
deprecated argument. Use by="group" instead. |
sub.line |
on which margin line should the Effect Size subtitle be printed, starting at 0 counting outwards. |
... |
further graphical parameters passed to |
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)
[Package rMEA version 1.2.2 Index]