EnvResampleTest: A function to test environmental inequality indices by...
In SegEnvIneq: Environmental Inequality Indices Based on Segregation Measures

View source: R/SegEnvIneq.R

EnvResampleTest

R Documentation

A function to test environmental inequality indices by resampling

Description

Resampling tests for environmental inequality indexes.

Usage

EnvResampleTest(x, a = NULL, fun, simtype = "MonteCarlo",
nsim = NULL, sampleunit = "unit", proba = NULL, setseed = FALSE,
perc = c(.05, .95), outl = FALSE, outmeth = "bp", sdtimes = 2, IQRrange = 1.5,
spatobj = NULL, folder = NULL, shape = NULL,
spatobj1 = NULL, folder1 = NULL, shape1 = NULL,
spatobj2 = NULL, folder2 = NULL, shape2 = NULL,
distmin = NULL, dist = NULL, K = NULL, kdist = NULL,
vers = "standard", w = NULL, b = NULL, ar = NULL, per = NULL,
queen = TRUE, ptype = "int", f = "exp", beta = 1)

Arguments

`x`	- a vector of the population/group distribution across spatial units for EDfunc or a matrix with the groups distributions across spatial units for ECfunc
`a`	- a vector of the environmental variable spatial distribution for EDfunc
`fun`	- a character vector with the function to be tested, fun = "EDfunc" or fun = "ECfunc"
`simtype`	- a character vector with the type of simulation. If simtype = 'MonteCarlo' (by default), the function produces a randomization test using Monte Carlo simulations. If simtype = 'Jack', the function generates jackknife replications
`nsim`	- the number of simulations (equal to the number of observations for jackknife)
`sampleunit`	- for jackknife replicant, the resampling is made only on spatial units. For Monte Carlo simulations, the user can choose between "unit" for spatial units resampling and "ind" for population resampling.
`proba`	- for Monte Carlo simulations on population, proba is a vector with location probabilities. If proba = NULL, the vector is equiprobable. If outliers are determined with jackknife technique, proba indicates the probability (confidence interval) for scoring tests.
`setseed`	- if TRUE (by default), specify zero seed for repetead simulation
`perc`	- percentille values for jackknife simulations
`outl`	- logical parameter for jackknife simulations, if TRUE the function provides the outliers obtained by jackknife iterations
`outmeth`	- a character vector designing the outliers detection method: outmeth = 'bp' (by default) for boxplot method; outmeth = 'sd' for standard deviation method; outmeth = 'z' for normal scores method; outmeth = 't' for t Student scores method; outmeth = 'chisq' for chi-squared scores method; outmeth = 'mad' for median absolute deviation method. The estimations based on scoring methods are obtained using outliers package
`sdtimes`	- multiplication factor of the standard deviation used for outliers detection with jackknife simulations (2 by default)
`IQRrange`	- determines the boxplot thresholds (1.5 by default) as multiplication of IQR (Inter Quartile Range)
`spatobj`	- a spatial object (SpatialPolygonsDataFrame) for EDfunc geographic functions
`folder`	- a character vector with the folder (directory) containing the shapefile for EDfunc
`shape`	- a character vector with the name of the shapefile for EDfunc as alternative to spatobj
`spatobj1`	- polygons spatial objects for population distribution to compute distances matrix for ECfunc
`folder1`	- a character vector with the folder (directory) containing the shape1 for ECfunc
`shape1`	- a character vector with the name of the shapefile for ECfunc as alternative to spatobj1
`spatobj2`	- points spatial objects for (dis-)amenity location to compute distances matrix for ECfunc
`folder2`	- a character vector with the folder (directory) containing the shape2 for ECfunc
`shape2`	- a character vector with the name of the shapefile for ECfunc as alternative to spatobj2
`distmin`	- an optional vector for ECfunc with the minimal distance between each spatial unit and all the environmental localisations
`dist`	- an optional matrix for ECfunc with the distance between all spatial units and environmental localisations.
`K`	- the order of contiguity matrix if "contig" version of EDfunc is chosen (K = 1 by default)
`kdist`	- if provided, the version of the index constrained to the nearest neighbors within a distance of kdist
`vers`	- the EDfunc version: "standard" (by default) for aspatial environmental dissimilarity index (Duncan); "contig" for adjusted index with a contiguity spatial interactions matrix (Morrill); "bound" for adjusted index with a boundaries spatial interactions matrix (Wong); "shape" for adjusted index with a boundaries and shape spatial interactions matrix (Wong); "user" for adjusted index with any user spatial interactions matrix
`w`	- an optional spatial weights matrix for EDfunc.
`b`	- an optional shared border matrix for EDfunc.
`ar`	- an optional vector of spatial units area for EDfunc.
`per`	- an optional vector of spatial units perimeter for EDfunc.
`queen`	- logical parameter for EDfunc defining criteria used for contiguity matrix computation, TRUE for queen (by default), FALSE for rook
`ptype`	- a string variable for EDfunc giving two options for perimeter calculation for Wong's indices: "int" to use only interior borders of spatial units and "all" to use entire borders, including to the exterior of the area
`f`	- spatial decay function of contiguity matrix for EDfunc when K > 1, with f = "exp" (by default) for exponential function of contiguity "distance"
`beta`	- spatial decay intensity parameter for EDfunc (equal to 1 by default), used only when the version with contiguity is chosen and K > 1

Value

A list with: - index's name - simulation type - statistics summary of the simulations - simulated index distribution - simulated population distribution - matrix with outliers (jackknife) - list with outliers values (jackknife)

References

Schaeffer Y. and Tivadar M. (2019) Measuring Environmental Inequalities: Insights from the Residential Segregation Literature. Ecological Economics, 164, 106329

Tivadar M. (2019) OasisR: An R Package to Bring Some Order to the World of Segregation Measurement. Journal of Statistical Software, 89 (7), pp. 1-39

Examples

data(segdata, package = "OasisR")
# segdata - theoretical distributions on a 10x10 grid map
# We consider A1 - population distribution and A2 - amenity distribution
testoutput <- EnvResampleTest (x = segdata@data$A1, a = segdata@data$A2, spatobj = segdata,
fun = "EDfunc", vers = "contig", queen = FALSE)
testoutput$Summary
hist(testoutput$IndexDist)

SegEnvIneq documentation built on Aug. 30, 2023, 9:06 a.m.