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Spatial Analysis by Distance IndicEs (SADIE).

Source: R/sadie.R
sadie.Rd

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.

Usage

sadie(data, ...)

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

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

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

# S3 method for 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).

Value

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.

References

Perry JN. 1995. Spatial analysis by distance indices. Journal of Animal Ecology 64, 303–314. 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. 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. doi:10.1111/j.2041-210X.2011.00165.x

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)
#> Computation of Perry's indices:
my_res
#> Spatial Analysis by Distance IndicEs (sadie)
#> 
#> Call:
#> sadie.count(data = my_count)
#> 
#> Ia: 1.2569 (Pa = 0.06)
#> 
summary(my_res)
#> 
#> Call:
#> sadie.count(data = my_count)
#> 
#> First 6 rows of clustering indices:
#>     x y i cost_flows      idx_P idx_LMX prob
#> 1   0 0 0 -148.09142 -2.2522651      NA   NA
#> 2  30 0 0  -91.27589 -1.5275799      NA   NA
#> 3  60 0 3 -108.16654 -2.1907077      NA   NA
#> 4  90 0 7  -40.99681 -0.6336085      NA   NA
#> 5 120 0 9   30.00000  0.7628231      NA   NA
#> 6 150 0 1  -42.31033 -0.7946351      NA   NA
#> 
#> Summary indices:
#>                       overall    inflow  outflow
#> Perry's index        1.309858 -1.458153 1.119927
#> Li-Madden-Xu's index       NA        NA       NA
#> 
#> Main outputs:
#> Ia: 1.2569 (Pa = 0.06)
#> 
#> 'Total cost': 1061.153
#> Number of permutations: 100
#> 
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)
#> Computation of Perry's indices:
#> Computation of Li-Madden-Xu's indices:
my_res
#> Spatial Analysis by Distance IndicEs (sadie)
#> 
#> Call:
#> sadie.count(data = my_count, index = "all", nperm = 20, threads = 2)
#> 
#> Ia: 1.3517 (Pa = 0.05)
#> 
summary(my_res)
#> 
#> Call:
#> sadie.count(data = my_count, index = "all", nperm = 20, threads = 2)
#> 
#> First 6 rows of clustering indices:
#>     x y i cost_flows      idx_P    idx_LMX       prob
#> 1   0 0 0 -148.09142 -2.0856425 -3.0083225 0.04761905
#> 2  30 0 0  -91.27589 -1.5994304 -1.5545829 0.23809524
#> 3  60 0 3 -108.16654 -1.9837172 -1.9703605 0.14285714
#> 4  90 0 7  -40.99681 -0.7734811 -0.7590137 0.66666667
#> 5 120 0 9   30.00000  0.8051948  0.8750000 0.09523810
#> 6 150 0 1  -42.31033 -0.8651327 -0.8268594 0.57142857
#> 
#> Summary indices:
#>                       overall    inflow  outflow
#> Perry's index        1.385343 -1.558203 1.149058
#> Li-Madden-Xu's index 1.317835 -1.431426 1.205459
#> 
#> Main outputs:
#> Ia: 1.3517 (Pa = 0.05)
#> 
#> 'Total cost': 1061.153
#> Number of permutations: 20
#> 
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)
#> Computation of Perry's indices:
#> Spatial Analysis by Distance IndicEs (sadie)
#> 
#> Call:
#> sadie.data.frame(data = my_df)
#> 
#> Ia: 1.2569 (Pa = 0.06)
#>