slanter (version 0.2-0)

slanted_orders: Compute rows and columns orders which move high values close to the diagonal.

Description

For a matrix expressing the cross-similarity between two (possibly different) sets of entities, this produces better results than clustering (e.g. as done by pheatmap). This is because clustering does not care about the order of each two sub-partitions. That is, clustering is as happy with ((2, 1), (4, 3)) as it is with the more sensible ((1, 2), (3, 4)). As a result, visualizations of similarities using naive clustering can be misleading.

Usage

slanted_orders(
  data,
  order_rows = TRUE,
  order_cols = TRUE,
  squared_order = TRUE,
  same_order = FALSE,
  discount_outliers = TRUE,
  max_spin_count = 10
)

Arguments

data

A rectangular matrix containing non-negative values.

order_rows

Whether to reorder the rows.

order_cols

Whether to reorder the columns.

squared_order

Whether to reorder to minimize the l2 norm (otherwise minimizes the l1 norm).

same_order

Whether to apply the same order to both rows and columns.

discount_outliers

Whether to do a final order phase discounting outlier values far from the diagonal.

max_spin_count

How many times to retry improving the solution before giving up.

Value

A list with two keys, rows and cols, which contain the order.

Examples

# NOT RUN {
slanter::slanted_orders(cor(t(mtcars)))
# }