Calculate geographic distance and mahalanobis distance to estimate outlier probability of a data point

Calculate geographic distance and mahalanobis distance to estimate outlier probability of a data point

Usage

distance_calc(data, latitude, longitude, env_layers, itr = 15, k = 3)

Arguments

data

data table with spatial and environmental variables

latitude

nested input from ec_flag_outlier

longitude

nested input from ec_flag_outlier

env_layers

header names of env variables. env_layers <- c("Temperature", "pH")

itr

iteration to run the clustering 100 or 1000 times

k

number of cluster to choose in each iteration

Value

A list of results that shows result of calculated distance for each iteration

Examples

data <- data.frame(
  scientificName = "Mexacanthina lugubris",
  decimalLongitude = c(-117, -117.8, -116.9),
  decimalLatitude = c(32.9, 33.5, 31.9),
  temperature_mean = c(12, 13, 14),
  temperature_min = c(9, 6, 10),
  temperature_max = c(14, 16, 18)
)

env_layers <- c("temperature_mean", "temperature_min", " temperature_max")

result_list <- distance_calc(data,
  latitude = "decimalLatitude",
  longitude = "decimalLongitude",
  env_layers,
  itr = 100,
  k = 3
)