Control the parameters used to classify hotspots

This function allows specification of parameters that affect the output from hotspot_classify.

Usage

hotspot_classify_params(
  hotspot_prop = 0.1,
  persistent_prop = 0.8,
  recent_prop = 0.2,
  critical_p = 0.05,
  nb_dist = NULL,
  include_self = TRUE,
  p_adjust_method = NULL
)

Arguments

hotspot_prop

A single numeric value specifying the minimum proportion of periods for which a cell must contain significant clusters of points before the cell can be classified as a hot or cold spot of any type.

persistent_prop

A single numeric value specifying the minimum proportion of periods for which a cell must contain significant clusters of points before the cell can be classified as a persistent hot or cold spot.

recent_prop

A single numeric value specifying the proportion of periods that should be treated as being recent in the classification of emerging and former hotspots.

critical_p

A threshold p-value below which values should be treated as being statistically significant.

nb_dist

The distance around a cell that contains the neighbours of that cell, which are used in calculating the statistic. If this argument is NULL (the default), nb_dist is set as cell_size * sqrt(2) so that only the cells immediately adjacent to each cell are treated as being its neighbours.

include_self

Should points in a given cell be counted as well as counts in neighbouring cells when calculating the values of G_i^* (if include_self = TRUE, the default) or G_i^* (if include_self = FALSE) values? You are unlikely to want to change the default value.

p_adjust_method

The method to be used to adjust p-values for multiple comparisons. NULL (the default) uses the default method used by p.adjust, but any of the character values in stats::p.adjust.methods may be specified.

Value

A list that can be used as the input to the params argument to hotspot_classify.