Construct a Covariate Term

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.

Usage

covariate(..., data, id = NULL, prefix = NULL, subset = NULL)

Arguments

...

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.

subset

Optional expression used to subset the covariate data.

Value

A list containing information about the covariate term with class 'covariatespec' that can be used within an event_model.

Details

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(onset ~ hrf(stimulus) + covariate(motion_x, motion_y, data = cov_data),
                    data = events, block = ~ 1, sampling_frame = sframe)

See also

• event_model() for creating complete fMRI models

• hrf() for creating HRF-convolved event terms

Examples

# 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)
# 50 events, strictly increasing onsets per block
event_data <- data.frame(
  stimulus = factor(rep(c("A", "B"), 25)),
  onset = seq(0, by = 4, length.out = 50)
)

# Full model with both HRF-convolved events and non-convolved covariates
model <- event_model(
  onset ~ hrf(stimulus) + covariate(x, y, data = motion_data, id = "motion"),
  data = event_data,
  block = ~ 1,
  sampling_frame = sframe
)