plot_fmri {EMC2} | R Documentation |
Plot fMRI peri-stimulus time courses
Description
This function plots average BOLD response around specified events for a single ROI
by using FIR based event estimation, all event_types in events are taken into account in the FIR.
Posterior predictives can be overlaid via the post_predict
argument.
Usage
plot_fmri(
timeseries,
post_predict = NULL,
events,
event_type,
high_pass = TRUE,
high_pass_model = "cosine",
posterior_args = list(),
legend_pos = "topleft",
layout = NA,
n_cores = 1,
...
)
Arguments
timeseries |
A data frame with columns 'subjects', 'run', 'time', and one ROI measurement column. |
post_predict |
Optional posterior predictive samples data frame (not shown in examples). |
events |
A data frame with columns 'subjects', 'run', 'onset', 'duration', 'event_type', and 'modulation'. |
event_type |
Character string specifying which |
high_pass |
Logical indicating whether to apply high-pass filtering.
Alternatively, specifying 'add' adds the regressors to the design matrix in the FIR.
The choice here should be the same as the choice for |
high_pass_model |
Character indicating which type of high-pass filtering to apply ('cosine', 'poly') |
posterior_args |
Named list of graphical parameters for posterior predictive lines. |
legend_pos |
Position of the legend. Default: "topleft". |
layout |
Panel layout matrix for multiple modulation groups. NULL leaves current layout |
n_cores |
Number of cores to calculate FIR across subjects with. |
... |
Additional graphical parameters passed to plotting functions (e.g., col, lwd, lty). |
Value
NULL. Produces plots as a side-effect.
Examples
ts <- data.frame(
subjects = rep(1, 100),
run = rep(1, 100),
time = seq(0, 99),
ROI = rnorm(100)
)
events <- data.frame(
subjects = rep(1, 5),
run = rep(1, 5),
onset = c(10, 30, 50, 70, 90),
event_type = rep("A", 5),
modulation = rep(1, 5),
duration = rep(0.5, 5)
)
plot_fmri(ts, events = events, event_type = "A")