Extract longnames

Superseded by conditions(x, style = "canonical").

Usage

# S3 method for class 'covariate_term'
longnames(x, ...)

# S3 method for class 'covariate_convolved_term'
longnames(x, ...)

longnames(x, ...)

# S3 method for class 'event_model'
longnames(x, drop.empty = TRUE, expand_basis = FALSE, ...)

# S3 method for class 'convolved_term'
longnames(x, ...)

# S3 method for class 'event_seq'
longnames(x, ...)

# S3 method for class 'feature_term'
longnames(x, ...)

Arguments

x

The object.

...

Additional arguments.

drop.empty

Logical; leave out cells with no events (default TRUE).

expand_basis

Logical; add a basis suffix for multi-basis HRFs (default FALSE).

Value

Character vector of long (fully qualified) names.

Details

Long names are the canonical condition names: each factor level is written variable.level, and the factors of an interaction are joined with _ (for example condition.A_attn.x). With expand_basis = TRUE a _bNN suffix is added for each HRF basis function.

They are not the design-matrix column names. A term's columns are paste0(term_tag, "_", longnames(term, expand_basis = TRUE)), so the column for level A of hrf(condition) is condition_condition.A, and longnames(term) returns condition.A. Use columns() (or colnames(design_matrix(x))) for the column names, and condition_map() for a table that lines the two up.

With drop.empty = TRUE (the default) a cell of an interaction that has no events is left out, as it is from the design matrix, so the names line up one-to-one with the term's columns. drop.empty = FALSE lists the full grid of factor levels, which is what conditions() always returns.

Methods exist for event_term, event_model (all terms in order), convolved_term, feature_term, covariate terms, and bare events from event_factor() and friends (event_seq), which are named as the single-variable event_term built from them would be.

Examples

# Create a simple event term with one condition factor
term <- event_term(
  list(condition = factor(c("A", "B", "A"))),
  onsets = c(0, 10, 20),
  blockids = c(1, 1, 1)
)
longnames(term)  # Returns: "condition.A" "condition.B"
#> [1] "condition.A" "condition.B"

# Create event term with multiple factors
term2 <- event_term(
  list(
    category = factor(c("face", "scene", "face")),
    attention = factor(c("attend", "attend", "ignore"))
  ),
  onsets = c(0, 10, 20),
  blockids = c(1, 1, 1)
)
longnames(term2)
#> [1] "category.face_attention.attend"  "category.scene_attention.attend"
#> [3] "category.face_attention.ignore" 
# Returns: "category.face_attention.attend"
#          "category.scene_attention.attend"
#          "category.face_attention.ignore"

# Long names versus design-matrix column names
sf <- fmrihrf::sampling_frame(blocklens = 30, TR = 2)
des <- data.frame(onset = c(0, 10, 20), run = 1,
                  condition = factor(c("A", "B", "A")))
em <- event_model(onset ~ hrf(condition), data = des, block = ~run,
                  sampling_frame = sf)
longnames(em)  # "condition.A" "condition.B"
#> [1] "condition.A" "condition.B"
columns(em)    # "condition_condition.A" "condition_condition.B"
#> [1] "condition_condition.A" "condition_condition.B"