Compute external metrics for spatial data — getSpatialExternalMetrics • poem

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

Usage

getSpatialExternalMetrics(
  true,
  pred,
  location,
  k = 6,
  alpha = 0.5,
  level = "class",
  metrics = c("SpatialWH", "SpatialAWH", "SpatialWC", "SpatialAWC"),
  fuzzy_true = TRUE,
  fuzzy_pred = FALSE,
  ...
)

Arguments

true: A vector containing the labels of the true classes. Must be a vector of characters, integers, numerics, or a factor, but not a list.
pred: A vector containing the labels of the predicted clusters. Must be a vector of characters, integers, numerics, or a factor, but not a list.
location: A matrix or data.frame of coordinates
k: The number of neighbors used when calculating the fuzzy class memberships for fuzzy metrics, or when calculating the weighted accuracy.
alpha: The parameter to control to what extend the spot itself contribute to the class composition calculation. "equal" means it is weighted the same as other neighbors. A numeric value between 0 and 1 means the weight of the frequency contribution for the spot itself, and the frequency contribution for its knn is then 1-alpha. By default 0.5.
level: The level to calculate the metrics. Options include "element", "class" and "dataset".
metrics: The metrics to compute. See details.
fuzzy_true: Logical; whether to compute fuzzy class memberships for true.
fuzzy_pred: Logical; whether to compute fuzzy class memberships for pred.
...: Optional params for getFuzzyPartitionElementMetrics() or findSpatialKNN().

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: "SpatialSPC", "SpatialNPC".
If level = "class", the allowed metrics are: "SpatialWH","SpatialAWH", "SpatialWC","SpatialAWC".
If level = "dataset", the allowed metrics are: "SpatialRI","SpatialARI","SpatialWH","SpatialAWH", "SpatialWC","SpatialAWC","SpatialAccuracy".

Examples

data(sp_toys)
data <- sp_toys
getSpatialExternalMetrics(data$label, data$p1, data[,c("x", "y")], k=6, level="class")
#> Comparing between a fuzzy truth and a hard prediction...
#> Standard error of the mean NDC across permutations:0.00228
#>   SpatialWH SpatialAWH SpatialWC SpatialAWC class cluster
#> 1        NA         NA 0.8078698   0.589422     1      NA
#> 2        NA         NA 1.0000000   1.000000     2      NA
#> 3 1.0000000  1.0000000        NA         NA    NA       1
#> 4 0.8323893  0.6494766        NA         NA    NA       2