Distance conditional response probability — distance

Response probability as a function of a distance measure, conditional on item availability.

Usage

distance_crp(
  data,
  index_key,
  distances,
  edges,
  centers = NULL,
  count_unique = FALSE,
  item_query = NULL,
  test_key = NULL,
  test = NULL
)

Arguments

data: Merged study and recall data.
index_key: Name of column containing the index of each item in the distances matrix.
distances: Items x items matrix of pairwise distances.
edges: Edges of bins to apply to the distances.
centers: Centers to label each bin. If not specified, the center point between edges will be used.
count_unique: If TRUE, possible transitions to a given distance bin will only count once for a given transition.
item_query: Query string to select items to include in the pool of possible recalls to be examined.
test_key: Name of column with labels to use when testing transitions for inclusion.
test: Function that takes in previous and current item values and returns TRUE for transitions that should be included.

Value

Results with subject, bin, prob, actual, and possible

columns. The prob column indicates conditional response probability. The actual column indicates the count of transitions actually made at a given distance bin. The possible column indicates the number of transitions that could have been made, given item availability (previously recalled items are excluded).

Examples

# Load data and item-item distances
raw <- sample_data("Morton2013")
data <- merge_free_recall(raw)
d <- sample_distances("Morton2013")

# Prepare bin definitions
percentiles <- pracma::linspace(.01, .99, 10)
edges <- quantile(pracma::squareform(d$distances), percentiles)

# Calculate distance CRP
data$item_index <- pool_index(data$item, d$items)
crp <- distance_crp(data, "item_index", d$distances, edges)