Compute internal metrics for spatial data — getSpatialInternalMetrics • poem

Computes a selection of internal clustering evaluation metrics for spatial data.

Usage

getSpatialInternalMetrics(
  labels,
  location,
  k = 6,
  level = "class",
  metrics = c("CHAOS", "PAS", "ELSA"),
  ...
)

Arguments

labels: A vector containing the labels to be evaluated.
location: A numerical matrix containing the location information, with rows as samples and columns as location dimensions.
k: The size of the spatial neighborhood to look at for each spot. This is used for calculating PAS and ELSA scores.
level: The level to calculate the metrics. Options include "element", "class" and "dataset".
metrics: The metrics to compute. See details.
...: Optional params for PAS().

Value

A data.frame of metrics.

Details

The allowed values for metrics depend on the value of level:

If level = "element", the allowed metrics are: "PAS", "ELSA".
If level = "class", the allowed metrics are: "CHAOS", "PAS", "ELSA".
If level = "dataset", the allowed metrics are:
- "PAS": Proportion of abnormal spots (PAS score)
- "ELSA": Entropy-based Local indicator of Spatial Association (ELSA score)
- "CHAOS": Spatial Chaos Score.
- "MPC": Modified partition coefficient
- "PC": Partition coefficient
- "PE": Partition entropy

Examples

data(sp_toys)
data <- sp_toys
getSpatialInternalMetrics(data$label, data[,c("x", "y")], k=6, level="class")
#> the specified variable is considered as categorical...
#>   class         PAS    ELSA.Ea   ELSA.Ec       ELSA     CHAOS
#> 1     1 0.008333333 0.07106927 0.1940084 0.06040663 0.2305866
#> 2     2 0.008333333 0.06918805 0.1944579 0.05840334 0.2305866