covariate | R Documentation |
Creates a covariate term that is added directly to the fMRI model without being convolved with a hemodynamic response function (HRF). This is useful for including nuisance variables, continuous covariates, or any other regressors that should not undergo HRF convolution.
covariate(..., data, id = NULL, prefix = NULL)
... |
A variable argument set of covariate names. |
data |
A data.frame containing the variables. |
id |
An optional identifier for the covariate term. |
prefix |
An optional prefix to add to the covariate names. |
In fMRI analysis, some predictors should not be convolved with the HRF because they represent:
Continuous physiological measurements (e.g., heart rate, respiration)
Motion parameters from head movement correction
Scanner drift or other technical artifacts
Behavioral measures that directly correlate with BOLD signal
Global signal or other nuisance variables
The covariate term can be combined with standard HRF-convolved event terms in the same model. For example:
model <- event_model(signal ~ hrf(stimulus) + covariate(motion_x, motion_y), data = data)
A list containing information about the covariate term with class 'covariatespec' that can be used within an event_model.
event_model()
for creating complete fMRI models
hrf()
for creating HRF-convolved event terms
# Add motion parameters as covariates
motion_data <- data.frame(
x = rnorm(100), # x translation
y = rnorm(100) # y translation
)
cv <- covariate(x, y, data = motion_data, prefix = "motion")
# Combine with event model
sframe <- sampling_frame(blocklens = c(100), TR = 2)
event_data <- data.frame(
stimulus = factor(rep(c("A", "B"), 50)),
onset = seq(0, 198, by = 4)
)
# Full model with both HRF-convolved events and non-convolved covariates
model <- event_model(
signal ~ hrf(stimulus) + covariate(x, y),
data = event_data,
sampling_frame = sframe
)
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