sadie function

Spatial Analysis by Distance IndicEs (SADIE).

sadie performs the SADIE procedure. It computes different indices and probabilities based on the distance to regularity for the observed spatial pattern and a specified number of random permutations of this pattern. Both kind of clustering indices described by Perry et al. (1999) and Li et al. (2012) can be computed.

sadie(data, ...)

## S3 method for class 'data.frame'
sadie(
  data,
  index = c("Perry", "Li-Madden-Xu", "all"),
  nperm = 100,
  seed = NULL,
  threads = 1,
  ...,
  method = "shortsimplex",
  verbose = TRUE
)

## S3 method for class 'matrix'
sadie(
  data,
  index = c("Perry", "Li-Madden-Xu", "all"),
  nperm = 100,
  seed = NULL,
  threads = 1,
  ...,
  method = "shortsimplex",
  verbose = TRUE
)

## S3 method for class 'count'
sadie(
  data,
  index = c("Perry", "Li-Madden-Xu", "all"),
  nperm = 100,
  seed = NULL,
  threads = 1,
  ...,
  method = "shortsimplex",
  verbose = TRUE
)

## S3 method for class 'incidence'
sadie(
  data,
  index = c("Perry", "Li-Madden-Xu", "all"),
  nperm = 100,
  seed = NULL,
  threads = 1,
  ...,
  method = "shortsimplex",
  verbose = TRUE
)

Arguments

• data: A data frame or a matrix with only three columns: the two first ones must be the x and y coordinates of the sampling units, and the last one, the corresponding disease intensity observations. It can also be a count or an incidence object.
• ...: Additional arguments to be passed to other methods.
• index: The index to be calculated: "Perry", "Li-Madden-Xu" or "all". By default, only Perry's index is computed for each sampling unit.
• nperm: Number of random permutations to assess probabilities.
• seed: Fixed seed to be used for randomizations (only useful for checking purposes). Not fixed by default (= NULL).
• threads: Number of threads to perform the computations.
• method: Method for the transportation algorithm.
• verbose: Explain what is being done (TRUE by default).

Returns

A sadie object.

Details

By convention in the SADIE procedure, clustering indices for a donor unit (outflow) and a receiver unit (inflow) are positive and negative in sign, respectively.

Examples

set.seed(123)
# Create an intensity object:
my_count <- count(aphids, mapping(x = xm, y = ym))
# Only compute Perry's indices:
my_res <- sadie(my_count)
my_res
summary(my_res)
plot(my_res)
plot(my_res, isoclines = TRUE)

set.seed(123)
# Compute both Perry's and Li-Madden-Xu's indices (using multithreading):
my_res <- sadie(my_count, index = "all", threads = 2, nperm = 20)
my_res
summary(my_res)
plot(my_res) # Identical to: plot(my_res, index = "Perry")
plot(my_res, index = "Li-Madden-Xu")

set.seed(123)
# Using usual data frames instead of intensity objects:
my_df <- aphids[, c("xm", "ym", "i")]
sadie(my_df)

References

Perry JN. 1995. Spatial analysis by distance indices. Journal of Animal Ecology 64, 303–314. tools:::Rd_expr_doi("10.2307/5892")

Perry JN, Winder L, Holland JM, Alston RD. 1999. Red–blue plots for detecting clusters in count data. Ecology Letters 2, 106–113. tools:::Rd_expr_doi("10.1046/j.1461-0248.1999.22057.x")

Li B, Madden LV, Xu X. 2012. Spatial analysis by distance indices: an alternative local clustering index for studying spatial patterns. Methods in Ecology and Evolution 3, 368–377. tools:::Rd_expr_doi("10.1111/j.2041-210X.2011.00165.x")