Package index • spdep

All functions

oldcol: Columbus OH spatial analysis data set - old numbering

EBImoran.mc(): Permutation test for empirical Bayes index

EBest(): Global Empirical Bayes estimator

EBlocal(): Local Empirical Bayes estimator

LOSH(): Local spatial heteroscedasticity

LOSH.cs(): Chi-square based test for local spatial heteroscedasticity

LOSH.mc(): Bootstrapping-based test for local spatial heteroscedasticity

SD.RStests() have_factor_preds_mf() warn_factor_preds(): Rao's score and adjusted Rao's score tests of linear hypotheses for spatial Durbin and spatial Durbin error models

aggregate(<nb>): Aggregate a spatial neighbours object

airdist(): Measure distance from plot

autocov_dist(): Distance-weighted autocovariate

bhicv: Data set with 4 life condition indices of Belo Horizonte region

card(): Cardinalities for neighbours lists

cell2nb() vi2mrc(): Generate neighbours list for grid cells

choynowski(): Choynowski probability map values

columbus: Columbus OH spatial analysis data set

n.comp.nb(): Depth First Search on Neighbor Lists

diffnb(): Differences between neighbours lists

dnearneigh(): Neighbourhood contiguity by distance

droplinks() addlinks1(): Drop and add links in a neighbours list

edit(<nb>): Interactive editing of neighbours lists

eire: Eire data sets

geary(): Compute Geary's C

geary.mc(): Permutation test for Geary's C statistic

geary.test(): Geary's C test for spatial autocorrelation

globalG.test(): Global G test for spatial autocorrelation

gabrielneigh() relativeneigh() soi.graph() graph2nb() plot(<Gabriel>) plot(<relative>): Graph based spatial weights

grid2nb(): Construct neighbours for a GridTopology

hotspot(): Cluster Classifications for Local Indicators of Spatial Association and Local Indicators for Categorical Data

include.self() remove.self(): Include self in neighbours list

joincount.mc(): Permutation test for same colour join count statistics

joincount.multi() print(<jcmulti>): BB, BW and Jtot join count statistic for k-coloured factors

joincount.test() print(<jclist>): BB join count statistic for k-coloured factors

knearneigh(): K nearest neighbours for spatial weights

knn2nb(): Neighbours list from knn object

lag(<listw>): Spatial lag of a numeric vector

lee(): Compute Lee's statistic

lee.mc(): Permutation test for Lee's L statistic

lee.test(): Lee's L test for spatial autocorrelation

licd_multi(): Local Indicators for Categorical Data

listw2sn() sn2listw(): Spatial neighbour sparse representation

lm.RStests() lm.LMtests() print(<RStestlist>) summary(<RStestlist>) print(<RStestlist.summary>): Rao's score (a.k.a Lagrange Multiplier) diagnostics for spatial dependence in linear models

lm.morantest(): Moran's I test for residual spatial autocorrelation

lm.morantest.exact() print(<moranex>): Exact global Moran's I test

lm.morantest.sad() print(<moransad>) summary(<moransad>) print(<summary.moransad>): Saddlepoint approximation of global Moran's I test

localC() localC_perm(): Compute Local Geary statistic

localG() localG_perm(): G and Gstar local spatial statistics

localGS(): A local hotspot statistic for analysing multiscale datasets

local_joincount_bv(): Calculate the local bivariate join count

local_joincount_uni(): Calculate the local univariate join count

localmoran() localmoran_perm(): Local Moran's I statistic

localmoran.exact() localmoran.exact.alt() print(<localmoranex>) as.data.frame(<localmoranex>): Exact local Moran's Ii tests

localmoran.sad() print(<localmoransad>) summary(<localmoransad>) print(<summary.localmoransad>) listw2star(): Saddlepoint approximation of local Moran's Ii tests

localmoran_bv(): Compute the Local Bivariate Moran's I Statistic

mat2listw(): Convert a square spatial weights matrix to a weights list object

moran(): Compute Moran's I

moran.mc(): Permutation test for Moran's I statistic

moran.plot(): Moran scatterplot

moran.test(): Moran's I test for spatial autocorrelation

moran_bv(): Compute the Global Bivariate Moran's I

mstree(): Find the minimal spanning tree

nb2INLA(): Output spatial neighbours for INLA

nb2WB() listw2WB(): Output spatial weights for WinBUGS

nb2blocknb(): Block up neighbour list for location-less observations

nb2lines() listw2lines() df2sn(): Use vector files for import and export of weights

nb2listw() listw2U(): Spatial weights for neighbours lists

nb2listwdist(): Distance-based spatial weights for neighbours lists

nb2mat() listw2mat(): Spatial weights matrices for neighbours lists

nbcost() nbcosts(): Compute cost of edges

nbdists(): Spatial link distance measures

nblag() nblag_cumul(): Higher order neighbours lists

intersect.nb() union.nb() setdiff.nb() complement.nb(): Set operations on neighborhood objects

p.adjustSP(): Adjust local association measures' p-values

plot(<mst>): Plot the Minimum Spanning Tree

plot(<nb>) plot(<listw>): Plot a neighbours list

plot(<skater>): Plot the object of skater class

poly2nb(): Construct neighbours list from polygon list

probmap(): Probability mapping for rates

prunecost(): Compute cost of prune each edge

prunemst(): Prune a Minimun Spanning Tree

read.gal() read.geoda(): Read a GAL lattice file into a neighbours list

read.gwt2nb() write.sn2gwt() read.dat2listw() write.sn2dat() read.swmdbf2listw() read_swm_dbf() write.swmdbf() write_swm_dbf() write.sn2DBF(): Read and write spatial neighbour files

Rotation(): Rotate a set of point by a certain angle

set.mcOption() get.mcOption() set.coresOption() get.coresOption() set.ClusterOption() get.ClusterOption(): Options for parallel support

set.spChkOption() get.spChkOption() chkIDs() spNamedVec() set.VerboseOption() get.VerboseOption() set.ZeroPolicyOption() get.ZeroPolicyOption() set.SubgraphOption() get.SubgraphOption() set.SubgraphCeiling() get.SubgraphCeiling() set.NoNeighbourOption() get.NoNeighbourOption() set.listw_is_CsparseMatrix_Option() get.listw_is_CsparseMatrix_Option(): Control checking of spatial object IDs

skater(): Spatial 'K'luster Analysis by Tree Edge Removal

sp.correlogram() plot(<spcor>) print(<spcor>): Spatial correlogram

sp.mantel.mc() plot(<mc.sim>): Mantel-Hubert spatial general cross product statistic

spatialdelta() summary(<spatialdelta>) print(<summary.spatialdelta>) linearised_diffusive_weights() metropolis_hastings_weights() iterative_proportional_fitting_weights() graph_distance_weights() as.matrix(<adjusted_spatial_weights>) plot_spatialcoords() plot_moran() plot_spatialscree() factorial_coordinates() plot_factorialcoords() plot_factorialscree() localdelta() cornish_fisher(): Weighted Multivariate Spatial Autocorrelation Measures

aple.plot() localAple() aple.mc() aple() lextrB() lextrW() lextrS() griffith_sone() subgraph_eigenw() mom_calc() mom_calc_int2() stsls() impacts(<stsls>) GMerrorsar() summary(<gmsar>) gstsls() impacts(<gmsar>) Hausman.test(<gmsar>) lagmess() ME() SpatialFiltering() LR.sarlm() logLik(<sarlm>) LR1.sarlm() Wald1.sarlm() Hausman.test(<sarlm>) as.spam.listw() as_dgRMatrix_listw() as_dsTMatrix_listw() as_dsCMatrix_I() as_dsCMatrix_IrW() Jacobian_W() powerWeights() plot(<lagImpact>) print(<lagImpact>) summary(<lagImpact>) HPDinterval(<lagImpact>) intImpacts() can.be.simmed() eigenw() similar.listw() do_ldet() jacobianSetup() cheb_setup() mcdet_setup() eigen_setup() eigen_pre_setup() spam_setup() spam_update_setup() Matrix_setup() Matrix_J_setup() LU_setup() LU_prepermutate_setup() moments_setup() SE_classic_setup() SE_whichMin_setup() SE_interp_setup() MCMCsamp() spautolm() summary(<spautolm>) spBreg_sac() impacts(<MCMC_sar_g>) impacts(<MCMC_sem_g>) impacts(<MCMC_sac_g>) spBreg_err() spBreg_lag() predict(<SLX>) lmSLX() impacts(<SLX>) create_WX() anova(<sarlm>) bptest.sarlm() errorsarlm() impacts(<sarlm>) lagsarlm() predict(<sarlm>) print(<sarlm.pred>) as.data.frame(<sarlm.pred>) residuals(<sarlm>) deviance(<sarlm>) coef(<sarlm>) vcov(<sarlm>) fitted(<sarlm>) sacsarlm() summary(<sarlm>) print(<sarlm>) print(<summary.sarlm>) trW(): Defunct Functions in Package spdep

spdep(): Return package version number

spweights.constants() Szero(): Provides constants for spatial weights matrices

ssw(): Compute the sum of dissimilarity

subset(<listw>): Subset a spatial weights list

subset(<nb>): Subset a neighbours list

summary(<nb>) print(<nb>) summary(<listw>) print(<listw>): Print and summary function for neighbours and weights lists

is.symmetric.nb() sym.attr.nb() make.sym.nb() old.make.sym.nb() is.symmetric.glist(): Test a neighbours list for symmetry

tolerance.nb(): Function to construct edges based on a tolerance angle and a maximum distance

tri2nb(): Neighbours list from tri object

write.nb.gal(): Write a neighbours list as a GAL lattice file