From 48b978b6dd7774e754abc04529bfd9d041284587 Mon Sep 17 00:00:00 2001 From: Roger Bivand Date: Tue, 6 Feb 2024 13:34:13 +0100 Subject: [PATCH] update docs --- docs/articles/CO69.html | 28 +- docs/articles/nb.html | 106 +++--- docs/articles/nb_sf.html | 56 ++-- docs/articles/sids.html | 38 +-- .../figure-html/unnamed-chunk-3-1.png | Bin 116683 -> 116768 bytes docs/news/index.html | 5 +- docs/pkgdown.yml | 2 +- docs/reference/LOSH.mc-1.png | Bin 37519 -> 37118 bytes docs/reference/LOSH.mc.html | 6 +- docs/reference/SD.RStests.html | 304 ++++++++++++++++++ docs/reference/bhicv.html | 2 +- docs/reference/compon.html | 15 +- docs/reference/dnearneigh.html | 6 +- docs/reference/edit.nb.html | 5 +- docs/reference/geary.mc.html | 95 +++++- docs/reference/geary.test.html | 58 +++- docs/reference/globalG.test.html | 55 +++- docs/reference/index.html | 8 +- docs/reference/joincount.multi.html | 69 +--- docs/reference/knearneigh.html | 4 +- docs/reference/lm.RStests.html | 277 ++++++++++++++++ docs/reference/lm.morantest.html | 12 +- docs/reference/localC.html | 77 +---- docs/reference/moran.mc.html | 66 ++++ docs/reference/moran.test.html | 24 +- docs/reference/mstree.html | 2 +- docs/reference/nb2blocknb.html | 69 +--- docs/reference/nb2lines.html | 8 +- docs/reference/nb2listwdist.html | 62 +--- docs/reference/nblag.html | 6 +- docs/reference/poly2nb.html | 2 +- docs/reference/skater.html | 14 +- docs/sitemap.xml | 6 + 33 files changed, 1028 insertions(+), 459 deletions(-) create mode 100644 docs/reference/SD.RStests.html create mode 100644 docs/reference/lm.RStests.html diff --git a/docs/articles/CO69.html b/docs/articles/CO69.html index 83020959..79a6a10e 100644 --- a/docs/articles/CO69.html +++ b/docs/articles/CO69.html @@ -379,8 +379,8 @@

Spatial weights 
 library(terra)
-v_eire_ge1 <-vect(eire_ge1)
-SG <- rasterize(v_eire_ge1, rast(nrows=70, ncols=50, extent=ext(v_eire_ge1)), field="county")
+v_eire_ge1 <-vect(eire_ge1)
+SG <- rasterize(v_eire_ge1, rast(nrows=70, ncols=50, extent=ext(v_eire_ge1)), field="county")
 library(rgrass)
 grass_home <- "/home/rsb/topics/grass/g820/grass82"
 initGRASS(grass_home, home=tempdir(), SG=SG, override=TRUE)
@@ -398,7 +398,7 @@ 
Spatial weightsinv_dlist <- lapply(dlist, function(x) 1/(x/1.609344))
 combo_km <- lapply(1:length(inv_dlist), function(i) inv_dlist[[i]]*prop_borders[[i]])
 combo_km_lw <- nb2listw(grass_borders$neighbours, glist=combo_km, style="B")
-summary(combo_km_lw)
+summary(combo_km_lw) 
## Characteristics of weights list object:
 ## Neighbour list object:
 ## Number of regions: 25 
@@ -434,7 +434,7 @@ 
Spatial weightsred_lw_unstand$neighbours[[Kerry]] <- red_lw_unstand$neighbours[[Kerry]][-Clare_in_Kerry]
 red_lw_unstand$weights[[Clare]] <- red_lw_unstand$weights[[Clare]][-Kerry_in_Clare]
 red_lw_unstand$weights[[Kerry]] <- red_lw_unstand$weights[[Kerry]][-Clare_in_Kerry]
-summary(red_lw_unstand)
+summary(red_lw_unstand) 
## Characteristics of weights list object:
 ## Neighbour list object:
 ## Number of regions: 25 
@@ -565,7 +565,7 @@ 
Measures of spatial autocorrelation
 Prop_stdR  <- lapply(vars, function(x) moran.test(eire_ge1[[x]], listw=lw_std, randomisation=TRUE))
 })
##    user  system elapsed 
-##   0.116   0.000   0.116
+## 0.118 0.000 0.118 
 res <- sapply(c("MoranN", "MoranR", "GearyN", "GearyR", "Prop_unstdN", "Prop_unstdR", "Prop_stdN", "Prop_stdR"), function(x) sapply(get(x), "[[", "statistic"))
 rownames(res) <- vars
@@ -608,7 +608,7 @@ 
Measures of spatial autocorrelation
 I <- sapply(Prop_stdN, function(x) x$estimate[1])[raw_data]
 EI <- sapply(Prop_stdN, function(x) x$estimate[2])[raw_data]
 res <- (I - EI)/wrong_N_sqVI
-names(res) <- vars[raw_data]
+names(res) <- vars[raw_data]
 print(formatC(res, format="f", digits=4), quote=FALSE)
##    pagval2_10   pagval10_50     pagval50p     cowspacre  ocattlepacre     pigspacre 
 ##        3.8836        1.4957        4.8276        4.6744        1.9003        3.1550 
@@ -621,7 +621,7 @@ 
Measures of spatial autocorrelation
 calculated above:

 
 res <- lapply(c("MoranR", "GearyR", "Prop_unstdR", "Prop_stdR"), function(x) sapply(get(x), function(y) c(y$estimate[1], sqrt(y$estimate[3]))))
-res <- t(do.call("rbind", res))
+res <- t(do.call("rbind", res))
 colnames(res) <- c("I", "sigma_I", "C", "sigma_C", "unstd_r", "sigma_r", "std_r", "sigma_r")
 rownames(res) <- vars
 print(formatC(res, format="f", digits=4), quote=FALSE)

@@ -652,7 +652,7 @@ 
Measures of spatial autocorrelation
 of the measure shown in the original Table 5:

 
 oMoranf <- function(x, nb) {
-  z <- scale(x, scale=FALSE)
+  z <- scale(x, scale=FALSE)
   n <- length(z)
   glist <- lapply(1:n, function(i) {ii <- nb[[i]]; ifelse(ii > i, 1, 0)})
   lw <- nb2listw(nb, glist=glist, style="B")
@@ -710,7 +710,7 @@ 
Simulating measures of s
 }
 f_bpara <- function(x, nsim, listw) {
   boot(x, statistic=MoranI.boot, R=nsim, sim="parametric", ran.gen=Nsim,
-    mle=list(mean=mean(x), sd=sd(x)), listw=listw, n=length(x),
+    mle=list(mean=mean(x), sd=sd(x)), listw=listw, n=length(x),
     S0=Szero(listw))
 }
 nsim <- 4999
@@ -737,8 +737,8 @@ 
Simulating measures of s
 Prop_stdRb  <- lapply(vars, function(x) f_bperm(x=eire_ge1[[x]], nsim=nsim, listw=lw_std))
 })

 
-res <- lapply(c("MoranNb", "MoranRb", "Prop_unstdNb", "Prop_unstdRb", "Prop_stdNb", "Prop_stdRb"), function(x) sapply(get(x), function(y) (y$t0 - mean(y$t))/sd(y$t)))
-res <- t(do.call("rbind", res))
+res <- lapply(c("MoranNb", "MoranRb", "Prop_unstdNb", "Prop_unstdRb", "Prop_stdNb", "Prop_stdRb"), function(x) sapply(get(x), function(y) (y$t0 - mean(y$t))/sd(y$t)))
+res <- t(do.call("rbind", res))
 colnames(res) <- c("MoranNb", "MoranRb", "Prop_unstdNb", "Prop_unstdRb", "Prop_stdNb", "Prop_stdRb")
 rownames(res) <- vars

 
We collate the results to compare with the analytical standard
@@ -786,7 +786,7 @@ 
Simulating measures of s
 Prop_stdSad  <- lapply(lm_objs, function(x) lm.morantest.sad(x, listw=lw_std))
 })
##    user  system elapsed 
-##   0.064   0.000   0.065
+## 0.066 0.000 0.067 
 res <- sapply(c("MoranSad", "Prop_unstdSad", "Prop_stdSad"), function(x) sapply(get(x), "[[", "statistic"))
 rownames(res) <- vars
@@ -839,7 +839,7 @@

Simulating measures of s Prop_stdEx <- lapply(lm_objs, function(x) lm.morantest.exact(x, listw=lw_std)) }) 
##    user  system elapsed 
-##   0.081   0.000   0.082
+## 0.084 0.000 0.084 
 res <- sapply(c("MoranEx", "Prop_unstdEx", "Prop_stdEx"), function(x) sapply(get(x), "[[", "statistic"))
 rownames(res) <- vars
@@ -881,7 +881,7 @@

Simulating measures of s function of the eigenvalues of the spatial weights matrix. APLE requires the use of row standardised weights.

-vars_scaled <- lapply(vars, function(x) scale(eire_ge1[[x]], scale=FALSE))
+vars_scaled <- lapply(vars, function(x) scale(eire_ge1[[x]], scale=FALSE))
 nb_W <- nb2listw(lw_unstand$neighbours, style="W")
 pre <- spatialreg:::preAple(0, listw=nb_W)
 MoranAPLE <- sapply(vars_scaled, function(x) spatialreg:::inAple(x, pre))
diff --git a/docs/articles/nb.html b/docs/articles/nb.html
index f4fc7ea1..791ddd08 100644
--- a/docs/articles/nb.html
+++ b/docs/articles/nb.html
@@ -133,7 +133,7 @@ 
Introductionlibrary(spdep)
## Loading required package: spData
## Loading required package: sf
-
## Linking to GEOS 3.12.1, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUE
+
## Linking to GEOS 3.12.1, GDAL 3.8.3, PROJ 9.3.1; sf_use_s2() is TRUE
 NY8 <- as(sf::st_read(system.file("shapes/NY8_utm18.shp", package="spData")), "Spatial")
## Reading layer `NY8_utm18' from data source 
@@ -152,8 +152,8 @@ 
Introduction
 Syracuse <- NY8[NY8$AREANAME == "Syracuse city",]
-Sy0_nb <- subset(NY_nb, NY8$AREANAME == "Syracuse city")
-summary(Sy0_nb)

+Sy0_nb <- subset(NY_nb, NY8$AREANAME == "Syracuse city")
+summary(Sy0_nb)
## Neighbour list object:
 ## Number of regions: 63 
 ## Number of nonzero links: 346 
@@ -185,7 +185,7 @@ 
Creating Contiguity Neighbours## [1] "sp"
 Sy1_nb <- poly2nb(Syracuse)
-isTRUE(all.equal(Sy0_nb, Sy1_nb, check.attributes=FALSE))
+isTRUE(all.equal(Sy0_nb, Sy1_nb, check.attributes=FALSE)) 
## [1] TRUE

As we can see, creating the contiguity neighbours from the Syracuse object reproduces the neighbours from (Waller and Gotway 2004). Careful examination of @@ -204,21 +204,21 @@

Creating Contiguity Neighbours
 Sy2_nb <- poly2nb(Syracuse, queen=FALSE)
-isTRUE(all.equal(Sy0_nb, Sy2_nb, check.attributes=FALSE))
+isTRUE(all.equal(Sy0_nb, Sy2_nb, check.attributes=FALSE)) 
## [1] FALSE
 oopar <- par(mfrow=c(1,2), mar=c(3,3,1,1)+0.1)
-plot(Syracuse, border="grey60")
-plot(Sy0_nb, coordinates(Syracuse), add=TRUE, pch=19, cex=0.6)
-text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="a)", cex=0.8)
-plot(Syracuse, border="grey60")
-plot(Sy0_nb, coordinates(Syracuse), add=TRUE, pch=19, cex=0.6)
-plot(diffnb(Sy0_nb, Sy2_nb, verbose=FALSE), coordinates(Syracuse),
+plot(Syracuse, border="grey60")
+plot(Sy0_nb, coordinates(Syracuse), add=TRUE, pch=19, cex=0.6)
+text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="a)", cex=0.8)
+plot(Syracuse, border="grey60")
+plot(Sy0_nb, coordinates(Syracuse), add=TRUE, pch=19, cex=0.6)
+plot(diffnb(Sy0_nb, Sy2_nb, verbose=FALSE), coordinates(Syracuse),
   add=TRUE, pch=".", cex=0.6, lwd=2)
## Warning in diffnb(Sy0_nb, Sy2_nb, verbose = FALSE): region.id differ; using ids
 ## of first list
-text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="b)", cex=0.8)
+text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="b)", cex=0.8) 

 par(oopar)
@@ -247,7 +247,7 @@

Creating Contiguity NeighbourswriteVECT6(Syracuse, "SY0") contig <- vect2neigh("SY0") Sy3_nb <- sn2listw(contig)$neighbours -isTRUE(all.equal(Sy3_nb, Sy2_nb, check.attributes=FALSE)) +isTRUE(all.equal(Sy3_nb, Sy2_nb, check.attributes=FALSE))

Similar approaches may also be used to read ArcGIS coverage data by tallying the left neighbour and right neighbour arc indices with the polygons in the data set, using either RArcInfo or @@ -302,20 +302,20 @@

Creating Graph-Based NeighboursSy7_nb <- graph2nb(relativeneigh(coords), row.names=IDs) 
 oopar <- par(mfrow=c(2,2), mar=c(1,1,1,1)+0.1)
-plot(Syracuse, border="grey60")
-plot(Sy4_nb, coords, add=TRUE, pch=".")
-text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="a)", cex=0.8)
-plot(Syracuse, border="grey60")
+plot(Syracuse, border="grey60")
+plot(Sy4_nb, coords, add=TRUE, pch=".")
+text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="a)", cex=0.8)
+plot(Syracuse, border="grey60")
 if (!is.null(Sy5_nb)) {
-  plot(Sy5_nb, coords, add=TRUE, pch=".")
-  text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="b)", cex=0.8)
+  plot(Sy5_nb, coords, add=TRUE, pch=".")
+  text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="b)", cex=0.8)
 }
-plot(Syracuse, border="grey60")
-plot(Sy6_nb, coords, add=TRUE, pch=".")
-text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="c)", cex=0.8)
-plot(Syracuse, border="grey60")
-plot(Sy7_nb, coords, add=TRUE, pch=".")
-text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="d)", cex=0.8)
+plot(Syracuse, border="grey60") +plot(Sy6_nb, coords, add=TRUE, pch=".") +text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="c)", cex=0.8) +plot(Syracuse, border="grey60") +plot(Sy7_nb, coords, add=TRUE, pch=".") +text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="d)", cex=0.8) 

 par(oopar)
@@ -380,15 +380,15 @@

Distance-Based Neighbours## 15 1 1 
 oopar <- par(mfrow=c(1,3), mar=c(1,1,1,1)+0.1)
-plot(Syracuse, border="grey60")
-plot(Sy8_nb, coords, add=TRUE, pch=".")
-text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="a)", cex=0.8)
-plot(Syracuse, border="grey60")
-plot(Sy9_nb, coords, add=TRUE, pch=".")
-text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="b)", cex=0.8)
-plot(Syracuse, border="grey60")
-plot(Sy10_nb, coords, add=TRUE, pch=".")
-text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="c)", cex=0.8)
+plot(Syracuse, border="grey60") +plot(Sy8_nb, coords, add=TRUE, pch=".") +text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="a)", cex=0.8) +plot(Syracuse, border="grey60") +plot(Sy9_nb, coords, add=TRUE, pch=".") +text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="b)", cex=0.8) +plot(Syracuse, border="grey60") +plot(Sy10_nb, coords, add=TRUE, pch=".") +text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="c)", cex=0.8) 

 par(oopar)
@@ -408,7 +408,7 @@

Distance-Based Neighbours 
 dsts <- unlist(nbdists(Sy8_nb, coords))
-summary(dsts)
+summary(dsts) 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 ##   395.7   587.3   700.1   760.4   906.1  1544.6
@@ -429,15 +429,15 @@ 
Distance-Based Neighbours##  4  1  1
 oopar <- par(mfrow=c(1,3), mar=c(1,1,1,1)+0.1)
-plot(Syracuse, border="grey60")
-plot(Sy11_nb, coords, add=TRUE, pch=".")
-text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="a)", cex=0.8)
-plot(Syracuse, border="grey60")
-plot(Sy12_nb, coords, add=TRUE, pch=".")
-text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="b)", cex=0.8)
-plot(Syracuse, border="grey60")
-plot(Sy13_nb, coords, add=TRUE, pch=".")
-text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="c)", cex=0.8)
+plot(Syracuse, border="grey60") +plot(Sy11_nb, coords, add=TRUE, pch=".") +text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="a)", cex=0.8) +plot(Syracuse, border="grey60") +plot(Sy12_nb, coords, add=TRUE, pch=".") +text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="b)", cex=0.8) +plot(Syracuse, border="grey60") +plot(Sy13_nb, coords, add=TRUE, pch=".") +text(bbox(Syracuse)[1,1], bbox(Syracuse)[2,2], labels="c)", cex=0.8)

 par(oopar)
@@ -456,15 +456,15 @@

Distance-Based Neighboursres <- sapply(nb_l, function(x) table(card(x))) mx <- max(card(Sy13_nb)) res1 <- matrix(0, ncol=(mx+1), nrow=3) -rownames(res1) <- names(res) +rownames(res1) <- names(res) colnames(res1) <- as.character(0:mx) -res1[1, names(res$d1)] <- res$d1 -res1[2, names(res$d2)] <- res$d2 -res1[3, names(res$d3)] <- res$d3 +res1[1, names(res$d1)] <- res$d1 +res1[2, names(res$d2)] <- res$d2 +res1[3, names(res$d3)] <- res$d3 library(RColorBrewer) pal <- grey.colors(3, 0.95, 0.55, 2.2) # RSB quietening greys -barplot(res1, col=pal, beside=TRUE, legend.text=FALSE, xlab="numbers of neighbours", ylab="tracts") +barplot(res1, col=pal, beside=TRUE, legend.text=FALSE, xlab="numbers of neighbours", ylab="tracts") legend("topright", legend=format(dS, digits=1), fill=pal, bty="n", cex=0.8, title="max. distance")

Distance-based neighbours: frequencies of numbers of neighbours by @@ -476,7 +476,7 @@

Distance-Based Neighbours +summary(dsts0) 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 ##    82.7  1505.0  3378.7  5865.8  8954.3 38438.1

If the areal entities are approximately regularly spaced, using @@ -506,14 +506,14 @@

Higher-Order Neighbours 
 Sy0_nb_lags <- nblag(Sy0_nb, maxlag=9)
-names(Sy0_nb_lags) <- c("first", "second", "third", "fourth", "fifth", "sixth", "seventh", "eighth", "ninth")
+names(Sy0_nb_lags) <- c("first", "second", "third", "fourth", "fifth", "sixth", "seventh", "eighth", "ninth")
 res <- sapply(Sy0_nb_lags, function(x) table(card(x)))
 mx <- max(unlist(sapply(Sy0_nb_lags, function(x) card(x))))
 nn <- length(Sy0_nb_lags)
 res1 <- matrix(0, ncol=(mx+1), nrow=nn)
-rownames(res1) <- names(res)
+rownames(res1) <- names(res)
 colnames(res1) <- as.character(0:mx)
-for (i in 1:nn) res1[i, names(res[[i]])] <- res[[i]]
+for (i in 1:nn) res1[i, names(res[[i]])] <- res[[i]]
 res1
##          0 1 2 3 4  5  6 7 8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
 ## first    0 1 1 5 9 14 17 9 6  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
diff --git a/docs/articles/nb_sf.html b/docs/articles/nb_sf.html
index 865e1ecd..736105d0 100644
--- a/docs/articles/nb_sf.html
+++ b/docs/articles/nb_sf.html
@@ -170,8 +170,8 @@ 
Data setsf_bna$AREAKEY <- gsub("\\.", "", sf_bna$Primary.ID)
 data(NY_data, package="spData")
 key <- as.character(nydata$AREAKEY)
-sf_bna1 <- sf_bna[match(key, sf_bna$AREAKEY), c("AREAKEY")]
-sf_bna2 <- merge(sf_bna1, nydata, by="AREAKEY")
+sf_bna1 <- sf_bna[match(key, sf_bna$AREAKEY), c("AREAKEY")]
+sf_bna2 <- merge(sf_bna1, nydata, by="AREAKEY")
 sf_bna2_utm18 <- st_transform(sf_bna2, "+proj=utm +zone=18 +datum=NAD27")
 st_write(sf_bna2_utm18, "NY8_bna_utm18.gpkg")
@@ -229,7 +229,7 @@

Comparison of sp and sf approaches} if (dothis) sf_extSoftVersion() 
##           GEOS           GDAL         proj.4 GDAL_with_GEOS     USE_PROJ_H 
-##       "3.12.1"        "3.8.2"        "9.3.1"         "true"         "true" 
+##       "3.12.1"        "3.8.3"        "9.3.1"         "true"         "true" 
 ##           PROJ 
 ##        "9.3.1"

Let us read the GPKG file with valid geometries in to ‘sf’ and ‘sp’ @@ -258,7 +258,7 @@

Contiguity neighbours for pol eps <- sqrt(.Machine$double.eps) system.time(for(i in 1:reps) NY8_sf_1_nb <- poly2nb(NY8_sf, queen=TRUE, snap=eps))/reps 
##    user  system elapsed 
-##  0.0934  0.0048  0.0995
+## 0.0989 0.0056 0.1050

Using spatial indices to check intersection of polygons is much faster than the legacy method in poly2nb. From spdep 1.1-7, use is made of GEOS through sf to find candidate @@ -306,7 +306,7 @@

Contiguity neighbours from partly invalid set:

 try(NY8_sf_old_1_nb <- poly2nb(NY8_sf_old), silent = TRUE)
-all.equal(NY8_sf_old_1_nb, NY8_sf_1_nb, check.attributes=FALSE)
+all.equal(NY8_sf_old_1_nb, NY8_sf_1_nb, check.attributes=FALSE) 
## [1] "Component 57: Numeric: lengths (4, 5) differ" 
 ## [2] "Component 58: Numeric: lengths (5, 6) differ" 
 ## [3] "Component 66: Numeric: lengths (7, 11) differ"
@@ -347,7 +347,7 @@ 
Contiguity neighbours from
 geometries in the same ways as before imposing validity:

 
 try(NY8_sf_old_1_nb_val <- poly2nb(NY8_sf_old_val), silent = TRUE)
-all.equal(NY8_sf_old_1_nb_val, NY8_sf_1_nb, check.attributes=FALSE)

+all.equal(NY8_sf_old_1_nb_val, NY8_sf_1_nb, check.attributes=FALSE)
## [1] "Component 57: Numeric: lengths (4, 5) differ" 
 ## [2] "Component 58: Numeric: lengths (5, 6) differ" 
 ## [3] "Component 66: Numeric: lengths (7, 11) differ"
@@ -356,7 +356,7 @@ 
Contiguity neighbours from
 
The neighbour sets are the same for the old boundaries with or
 without imposing validity:

 
-all.equal(NY8_sf_old_1_nb_val, NY8_sf_old_1_nb, check.attributes=FALSE)

+all.equal(NY8_sf_old_1_nb_val, NY8_sf_old_1_nb, check.attributes=FALSE)
## [1] TRUE
@@ -383,7 +383,7 @@

Finding points for polygon objects 
 if (unname(sf_extSoftVersion()["GEOS"] >= "3.9.0")) 
-    NY8_cic_sf <- st_cast(st_inscribed_circle(st_geometry(NY8_sf), nQuadSegs=0), "POINT")[(1:(2*nrow(NY8_sf)) %% 2) != 0]
+ NY8_cic_sf <- st_cast(st_inscribed_circle(st_geometry(NY8_sf), nQuadSegs=0), "POINT")[(1:(2*nrow(NY8_sf)) %% 2) != 0]

We need to check whether coordinates are planar or not:

 st_is_longlat(NY8_ct_sf)
@@ -419,12 +419,12 @@

K-nearest neighbours
 system.time(for (i in 1:reps) NY88_nb_sf <- knn2nb(knearneigh(NY8_ct_sf, k=1)))/reps
##    user  system elapsed 
-##  0.0182  0.0009  0.0193
+## 0.0181 0.0008 0.0191

Legacy code may be used omitting the kd-tree:

 system.time(for (i in 1:reps) NY89_nb_sf <- knn2nb(knearneigh(NY8_ct_sf, k=1, use_kd_tree=FALSE)))/reps
##    user  system elapsed 
-##  0.0174  0.0011  0.0186
+## 0.0180 0.0013 0.0195

Distance neighbours @@ -433,7 +433,7 @@

Distance neighbours
 dsts <- unlist(nbdists(NY88_nb_sf, NY8_ct_sf))
-summary(dsts)

+summary(dsts) 
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 ##    82.85   912.85  1801.11  3441.04  4461.26 17033.11
@@ -443,7 +443,7 @@ 
Distance neighbours
 system.time(for (i in 1:reps) NY810_nb <- dnearneigh(NY8_ct_sf, d1=0, d2=0.75*max_1nn))/reps
##    user  system elapsed 
-##  0.0310  0.0007  0.0319
+## 0.0324 0.0007 0.0334

By default, the function uses dbscan::frNN() to build a kd-tree in 2D or 3D which is then used to find distance neighbours. For small n, the argument use_kd_tree=FALSE may speed up @@ -453,7 +453,7 @@

Distance neighbours
 system.time(for (i in 1:reps) NY811_nb <- dnearneigh(NY8_ct_sf, d1=0, d2=0.75*max_1nn, use_kd_tree=FALSE))/reps
##    user  system elapsed 
-##  0.0166  0.0009  0.0175
+## 0.0176 0.0008 0.0185
@@ -483,7 +483,7 @@

K-nearest neighbourssf_use_s2(TRUE) system.time(for (i in 1:reps) pts_ll1_nb <- knn2nb(knearneigh(pts_ll, k=6)))/reps

##    user  system elapsed 
-##  0.0245  0.0000  0.0246
+## 0.0251 0.0000 0.0252

For this smaller data set, the legacy approach without spatial indexing is adequate, but slows down as the number of observations increases:

@@ -493,12 +493,12 @@

K-nearest neighbours
 system.time(for (i in 1:reps) pts_ll2_nb <- knn2nb(knearneigh(pts_ll, k=6)))/reps
##    user  system elapsed 
-##  0.0186  0.0001  0.0188
+## 0.0202 0.0000 0.0203

The WGS84 ellipsoid Great Circle distances differ a very little from the s2 spherical distances, yielding output that here diverges for two tract centroids:

-all.equal(pts_ll1_nb, pts_ll2_nb, check.attributes=FALSE)
+all.equal(pts_ll1_nb, pts_ll2_nb, check.attributes=FALSE) 
## [1] "Component 52: Mean relative difference: 1.466667"  
 ## [2] "Component 124: Mean relative difference: 0.0251046"
@@ -546,7 +546,7 @@ 
Distance neighbours=0.75*max_1nn_ll))/(reps/5)
 }
##    user  system elapsed 
-##  0.0430  0.0000  0.0435
+## 0.0445 0.0000 0.0450

Alternatively, spherical distances can be used with dwithin=FALSE and s2::s2_closest_edges(); although running in similar time, s2::s2_closest_edges() @@ -555,9 +555,9 @@

Distance neighbours
 system.time(for (i in 1:(reps/5)) pts_ll5_nb <- dnearneigh(pts_ll, d1=0, d2=0.75*max_1nn_ll, dwithin=FALSE))/(reps/5)
##    user  system elapsed 
-##  0.0265  0.0005  0.0270
+## 0.029 0.000 0.029 
-if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3_nb, pts_ll5_nb, check.attributes=FALSE)
+if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3_nb, pts_ll5_nb, check.attributes=FALSE) 
## [1] TRUE

Using s2::s2_closest_edges() respects d1 > 0 without requiring a second pass in R, so is @@ -568,7 +568,7 @@

Distance neighbours=0.75*max_1nn_ll, dwithin=FALSE))/(reps/5) } 
##    user  system elapsed 
-##   0.027   0.000   0.027
+## 0.0275 0.0000 0.0280

Using s2::s2_dwithin_matrix() requires a second pass, one for the lower bound, another for the upper bound, and a set difference operation to find neighbours in the distance band:

@@ -578,20 +578,20 @@

Distance neighbours=0.75*max_1nn_ll))/(reps/5) } 
##    user  system elapsed 
-##  0.0720  0.0005  0.0730
+## 0.0740 0.0000 0.0745 
-if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3a_nb, pts_ll5a_nb, check.attributes=FALSE)
+if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3a_nb, pts_ll5a_nb, check.attributes=FALSE) 
## [1] TRUE

Setting use_s2=FALSE falls back to the legacy version, which uses symmetry to reduce time:

 system.time(for (i in 1:reps) pts_ll6_nb <- dnearneigh(pts_ll, d1=0, d2=0.75*max_1nn_ll, use_s2=FALSE))/reps
##    user  system elapsed 
-##  0.0094  0.0000  0.0095
+## 0.0101 0.0000 0.0102

Minor differences may occur between the legacy ellipsoid and s2 spherical approaches:

-all.equal(pts_ll5_nb, pts_ll6_nb, check.attributes=FALSE)
+all.equal(pts_ll5_nb, pts_ll6_nb, check.attributes=FALSE) 
##  [1] "Component 20: Numeric: lengths (6, 5) differ"     
 ##  [2] "Component 28: Numeric: lengths (7, 6) differ"     
 ##  [3] "Component 112: Numeric: lengths (109, 108) differ"
@@ -617,9 +617,9 @@ 
Distance neighbours
 system.time(for (i in 1:reps) pts_ll6a_nb <- dnearneigh(pts_ll, d1=5, d2=0.75*max_1nn_ll, use_s2=FALSE))/reps

 
##    user  system elapsed 
-##  0.0096  0.0000  0.0096

+##  0.0099  0.0001  0.0100
-if (packageVersion("s2") > "1.0.7") all.equal(pts_ll5a_nb, pts_ll6a_nb, check.attributes=FALSE)
+if (packageVersion("s2") > "1.0.7") all.equal(pts_ll5a_nb, pts_ll6a_nb, check.attributes=FALSE) 
##  [1] "Component 20: Numeric: lengths (6, 5) differ"       
 ##  [2] "Component 28: Numeric: lengths (7, 6) differ"       
 ##  [3] "Component 112: Numeric: lengths (62, 61) differ"    
@@ -668,9 +668,9 @@ 
Contiguity neighbours for spherical polygon support
 system.time(for (i in 1:reps) NY8_sf_1_nb_ll <- poly2nb(NY8_sf_ll, queen=TRUE, snap=eps))/reps

 
##    user  system elapsed 
-##  0.1571  0.0022  0.1613

+##  0.1574  0.0024  0.1604
-all.equal(NY8_sf_1_nb, NY8_sf_1_nb_ll, check.attributes=FALSE)
+all.equal(NY8_sf_1_nb, NY8_sf_1_nb_ll, check.attributes=FALSE) 
## [1] TRUE
diff --git a/docs/articles/sids.html b/docs/articles/sids.html index 837ad497..39dafff8 100644 --- a/docs/articles/sids.html +++ b/docs/articles/sids.html @@ -125,7 +125,7 @@

Getting the data into R

We will be using the spdep and spreg packages, here version: spdep, version 1.3-2, -2024-01-02, the sf package and the +2024-02-06, the sf package and the tmap package. The data from the sources referred to above is documented in the help page for the nc.sids data set in spData. The actual data, included in a shapefile @@ -151,21 +151,21 @@

Getting the data into R 
 sf_use_s2(FALSE)
-plot(st_geometry(nc), axes=TRUE)
-text(st_coordinates(st_centroid(st_geometry(nc), of_largest_polygon=TRUE)), label=nc$FIPSNO, cex=0.5)
+plot(st_geometry(nc), axes=TRUE) +text(st_coordinates(st_centroid(st_geometry(nc), of_largest_polygon=TRUE)), label=nc$FIPSNO, cex=0.5)

We can examine the names of the columns of the data frame to see what it contains — in fact some of the same columns that we will be examining below, and some others which will be useful in cleaning the data set.

-names(nc)
+names(nc) 
##  [1] "CNTY_ID"   "AREA"      "PERIMETER" "CNTY_"     "NAME"      "FIPS"     
 ##  [7] "FIPSNO"    "CRESS_ID"  "BIR74"     "SID74"     "NWBIR74"   "BIR79"    
 ## [13] "SID79"     "NWBIR79"   "east"      "north"     "x"         "y"        
 ## [19] "lon"       "lat"       "L_id"      "M_id"      "geometry"
-summary(nc)
+summary(nc) 
##     CNTY_ID          AREA          PERIMETER         CNTY_     
 ##  Min.   :1825   Min.   :0.0420   Min.   :0.999   Min.   :1825  
 ##  1st Qu.:1902   1st Qu.:0.0910   1st Qu.:1.324   1st Qu.:1902  
@@ -324,7 +324,7 @@ 
Getting the data into R
 
-all.equal(as.data.frame(nc_sf)[,1:14], as.data.frame(sids_sf)[,1:14])

+all.equal(as.data.frame(nc_sf)[,1:14], as.data.frame(sids_sf)[,1:14])
##  [1] "Names: 12 string mismatches"                                  
 ##  [2] "Component 4: Modes: numeric, character"                       
 ##  [3] "Component 4: target is numeric, current is character"         
@@ -342,7 +342,7 @@ 
Getting the data into R## [15] "Component 14: Attributes: < target is NULL, current is list >"
 ## [16] "Component 14: target is numeric, current is sfc_MULTIPOLYGON"
-all.equal(as.data.frame(nc_sf)[,1:14], as.data.frame(sids2_sf)[,1:14])
+all.equal(as.data.frame(nc_sf)[,1:14], as.data.frame(sids2_sf)[,1:14]) 
##  [1] "Names: 12 string mismatches"                         
 ##  [2] "Component 4: Modes: numeric, character"              
 ##  [3] "Component 4: target is numeric, current is character"
@@ -360,7 +360,7 @@ 
Getting the data into RThe spData data set has some columns reordered and a
 surprise:

 
-all.equal(as.data.frame(nc_sf)[,1:14], as.data.frame(nc)[,c(2,3,4,1,5:14)])

+all.equal(as.data.frame(nc_sf)[,1:14], as.data.frame(nc)[,c(2,3,4,1,5:14)])
## [1] "Component \"NWBIR74\": Mean relative difference: 0.04891304"

so a difference in NWBIR74:

@@ -399,8 +399,8 @@ 
Getting the data into R## 37055 37095
 ## 3 disjoint connected subgraphs
-plot(st_geometry(nc), border="grey")
-plot(ncCC89, st_centroid(st_geometry(nc), of_largest_polygon), add=TRUE, col="blue")
+plot(st_geometry(nc), border="grey") +plot(ncCC89, st_centroid(st_geometry(nc), of_largest_polygon), add=TRUE, col="blue")

Printing the neighbour object shows that it is a neighbour list object, with a very sparse structure — if displayed as a matrix, only @@ -411,16 +411,16 @@

Getting the data into Rregion.id of 37001 can be retreived by matching against the indices. More information can be obtained by using -summary() on an nb +summary() on an nb object. Finally, we associate a vector of names with the neighbour list, through the row.names argument. The names should be unique, as with data frame row names.

 r.id <- attr(ncCC89, "region.id")
-ncCC89[[match("37001", r.id)]]
+ncCC89[[match("37001", r.id)]] 
## [1] 11 26 29 30 48
-r.id[ncCC89[[match("37001", r.id)]]]
+r.id[ncCC89[[match("37001", r.id)]]] 
## [1] 37033 37081 37135 37063 37037

The neighbour list object records neighbours by their order in relation to the list itself, so the neighbours list for the county with @@ -510,7 +510,7 @@

Probability mapping
-hist(nc$pmap, main="")
+hist(nc$pmap, main="")

One ad-hoc way to assess the impact of the possible failure of our assumption that the counts follow the Poisson distribution is to @@ -609,7 +609,7 @@

Exploration and modelling of the and Read (1989):

 nc$ft.SID74 <- sqrt(1000)*(sqrt(nc$SID74/nc$BIR74) + sqrt((nc$SID74+1)/nc$BIR74))
-stem(round(nc$ft.SID74, 1), scale=2)
+stem(round(nc$ft.SID74, 1), scale=2) 
## 
 ##   The decimal point is at the |
 ## 
@@ -652,9 +652,9 @@ 
Median polish smoothingmFT <- sqrt(1000)*(sqrt(mSID74/mBIR74) + sqrt((mSID74+1)/mBIR74))
 # mFT1 <- t(matrix(mFT, 4, 4, byrow=TRUE))
 # wrong assignment of 12 elements to a 4x4 matrix detected by CRAN test 2021-05-22
-rc <- do.call("rbind", lapply(strsplit(names(mFT), ":"), as.integer))
+rc <- do.call("rbind", lapply(strsplit(names(mFT), ":"), as.integer))
 mFT1 <- matrix(as.numeric(NA), 4, 4)
-for (i in 1:nrow(rc)) mFT1[rc[i,1], rc[i,2]] <- mFT[i]
+for (i in 1:nrow(rc)) mFT1[rc[i,1], rc[i,2]] <- mFT[i]
 med <- medpolish(mFT1, na.rm=TRUE, trace.iter=FALSE)
 med
## 
@@ -681,8 +681,8 @@ 
Median polish smoothing
 
-mL_id <- model.matrix(~ as.factor(nc$L_id) -1)
-mM_id <- model.matrix(~ as.factor(nc$M_id) -1)
+mL_id <- model.matrix(~ as.factor(nc$L_id) -1)
+mM_id <- model.matrix(~ as.factor(nc$M_id) -1)
 nc$pred <- c(med$overall + mL_id %*% med$row + mM_id %*% med$col)
 nc$mp_resid <- nc$ft.SID74 - nc$pred

 
diff --git a/docs/articles/sids_files/figure-html/unnamed-chunk-3-1.png b/docs/articles/sids_files/figure-html/unnamed-chunk-3-1.png
index a1e842960b7a86396deb6b4ffd4fe05df2c0ada2..c0569c25afb63e0a30f2b44d492743f757556a59 100644
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zPtD#oKK)G++tL~LxO(1CcIx?hVPQxrWdA-=1pPz^^#A|Changelog 

 
     

 
Version 1.3-2 (development)

-
  • add SDM.RStests implementation of Rao’s score tests for spatial Durbin models (Koley and Bera, 2024)
    • 
+
    • change lm.LMtests to lm.RStests and re-name Lagrange multiplier to Rao’s score; add GNM_ prefix to test names if the input object inherits from SlX created by spatialreg::lmSLX (Koley, forthcoming)
      • 
+
    • add SD.RStests implementation of Rao’s score tests for spatial Durbin models (Koley and Bera, 2024) and for SDEM models (Koley, forthcoming)
      • 
+
    • #143 row.names pass-through in poly2nb corrected, harmonised row.names pass-through also in nbdists and dnearneigh
      • 
+
    • #139 add na.action argument to geary.test, geary.mc and globalG.test
      • 
 
    • add style to sn2listw use in tri2nb
      • 
 

     

diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 16762241..ed010dfe 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -6,5 +6,5 @@ articles:
   nb: nb.html
   nb_sf: nb_sf.html
   sids: sids.html
-last_built: 2024-01-04T12:20Z
+last_built: 2024-02-06T12:30Z
 
diff --git a/docs/reference/LOSH.mc-1.png b/docs/reference/LOSH.mc-1.png
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summary(resLOSH_mc) #> Hi x_bar_i ei Pr() #> Min. :0.03438 Min. :13.85 Min. : 0.0298 Min. :0.009901 -#> 1st Qu.:0.23838 1st Qu.:24.71 1st Qu.: 7.0114 1st Qu.:0.099010 -#> Median :0.66689 Median :35.90 Median : 52.1094 Median :0.722772 -#> Mean :1.06592 Mean :34.88 Mean : 151.9232 Mean :0.555466 +#> 1st Qu.:0.23838 1st Qu.:24.71 1st Qu.: 7.0114 1st Qu.:0.079208 +#> Median :0.66689 Median :35.90 Median : 52.1094 Median :0.653465 +#> Mean :1.06592 Mean :34.88 Mean : 151.9232 Mean :0.552233 #> 3rd Qu.:1.59680 3rd Qu.:45.39 3rd Qu.: 105.0551 3rd Qu.:0.900990 #> Max. :4.68765 Max. :54.91 Max. :2455.2201 Max. :0.990099 resLOSH_cs <- LOSH.cs(columbus$CRIME, nb2listw(col.gal.nb)) diff --git a/docs/reference/SD.RStests.html b/docs/reference/SD.RStests.html new file mode 100644 index 00000000..efb6fbce --- /dev/null +++ b/docs/reference/SD.RStests.html @@ -0,0 +1,304 @@ + +Rao's score and adjusted Rao's score tests of linear hypotheses for spatial Durbin and spatial Durbin error models — SD.RStests • spdep + + +
+
+ + + +
+
+
+

Rao's score and adjusted Rao's score tests of linear hypotheses for spatial Durbin and spatial Durbin error models

+ + +
+ +
+

Rao's score and adjusted Rao's score tests of linear hypotheses applied to a fitted linear model to examine whether either the spatially lagged dependent variable lag or the spatially lagged independent variable(s) WX should be included in the model, or both (SDM). Adjusted tests are provided for lag and WX adapting to the presence of the other, and a joint test for both. The joint test is equal to the unadjusted of one plus the adjusted of the other. In addition, draft tests are added from Koley (2024, section 6) for spatial Durbin error models to examine whether either the spatially lagged error err or the spatially lagged independent variable(s) WX should be included in the model, or both (SDEM); because of orthogonality, no adjusted tests are required.

+
+ +
+ 
SD.RStests(model, listw, zero.policy = attr(listw, "zero.policy"), test = "SDM",
+ Durbin = TRUE)
+
+ +
+

Arguments

+
model
+

an object of class lm returned by lm

+ +
listw
+

a listw object created for example by nb2listw, +expected to be row-standardised (W-style)

+ +
zero.policy
+

default attr(listw, "zero.policy") as set when listw was created, if attribute not set, use global option value; if TRUE assign zero to the lagged value of zones without +neighbours, if FALSE assign NA

+ +
test
+

test=“SDM” computes the SDM tests, a character vector of tests requested chosen from SDM_RSlag, SDM_adjRSlag, SDM_RSWX, SDM_adjRSWX, SDM_Joint, test=“SDEM” computes the SDEM tests, a character vector of tests requested chosen from SDEM_RSerr, SDEM_RSWX, SDEM_Joint; test=“all” computes all the tests

+ +
Durbin
+

default TRUE for Durbin models including WX; if TRUE, full spatial Durbin model; if a formula object, the subset of explanatory variables to lag

+ +
+
+

Value

+ + +

A list of class LMtestlist of htest objects, each with:

+
statistic
+

the value of the Lagrange Multiplier test.

+ +
parameter
+

number of degrees of freedom

+ +
p.value
+

the p-value of the test.

+ +
method
+

a character string giving the method used.

+ +
data.name
+

a character string giving the name(s) of the data.

+ +
+
+

References

+

Malabika Koley and Anil K. Bera (2024) To use, or not to use the spatial Durbin model? – that is the question, Spatial Economic Analysis, 19:1, 30-56, doi:10.1080/17421772.2023.2256810 +; Malabika Koley (2024) Specification Testing under General Nesting Spatial Model (Appendix C), https://sites.google.com/view/malabikakoley/research.

+
+
+

Author

+

Roger Bivand Roger.Bivand@nhh.no, Malabika Koley and Anil K. Bera

+
+
+

Note

+

The results in the example below agree with those in Table 3, p. 22 in Koley and Bera (2024).

+
+
+

See also

+

lm, lm.RStests

+
+ +
+

Examples

+ 
columbus <- sf::st_read(system.file("shapes/columbus.shp", package="spData")[1])
+#> Reading layer `columbus' from data source 
+#>   `/home/rsb/lib/r_libs/spData/shapes/columbus.shp' using driver `ESRI Shapefile'
+#> Simple feature collection with 49 features and 20 fields
+#> Geometry type: POLYGON
+#> Dimension:     XY
+#> Bounding box:  xmin: 5.874907 ymin: 10.78863 xmax: 11.28742 ymax: 14.74245
+#> CRS:           NA
+col.gal.nb <- read.gal(system.file("weights/columbus.gal", package="spData")[1])
+col.listw <- nb2listw(col.gal.nb, style="W")
+lm_obj <- lm(CRIME ~ INC + HOVAL, data=columbus)
+summary(lm.RStests(lm_obj, col.listw, test="all"))
+#> 	Rao's score (a.k.a Lagrange multiplier) diagnostics for spatial
+#> 	dependence
+#> data:  
+#> model: lm(formula = CRIME ~ INC + HOVAL, data = columbus)
+#> test weights: col.listw
+#>  
+#>          statistic parameter  p.value   
+#> RSerr     4.611126         1 0.031765 * 
+#> RSlag     7.855675         1 0.005066 **
+#> adjRSerr  0.033514         1 0.854744   
+#> adjRSlag  3.278064         1 0.070212 . 
+#> SARMA     7.889190         2 0.019359 * 
+#> ---
+#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+res <- SD.RStests(lm_obj, col.listw, test="SDM")
+summary(res)
+#> 	Rao's score test spatial Durbin diagnostics
+#> data:  
+#> model: lm(formula = CRIME ~ INC + HOVAL, data = columbus)
+#> weights: col.listw
+#>  
+#>              statistic parameter  p.value   
+#> SDM_RSlag       7.8557         1 0.005066 **
+#> SDM_adjRSlag    4.6111         1 0.031765 * 
+#> SDM_RSWX        6.1376         2 0.046477 * 
+#> SDM_adjRSWX     2.8931         2 0.235386   
+#> SDM_Joint      10.7487         3 0.013165 * 
+#> ---
+#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+all.equal(unname(res$SDM_Joint$statistic),
+ unname(res$SDM_RSlag$statistic + res$SDM_adjRSWX$statistic))
+#> [1] TRUE
+all.equal(unname(res$SDM_Joint$statistic),
+ unname(res$SDM_adjRSlag$statistic + res$SDM_RSWX$statistic))
+#> [1] TRUE
+res <- SD.RStests(lm_obj, col.listw, test="SDEM")
+summary(res)
+#> 	Rao's score test spatial Durbin diagnostics
+#> data:  
+#> model: lm(formula = CRIME ~ INC + HOVAL, data = columbus)
+#> weights: col.listw
+#>  
+#>            statistic parameter p.value  
+#> SDEM_RSerr    4.6111         1 0.03177 *
+#> SDEM_RSWX     6.1376         2 0.04648 *
+#> SDEM_Joint   10.7487         3 0.01317 *
+#> ---
+#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+all.equal(unname(res$SDEM_Joint$statistic),
+ unname(res$SDEM_RSerr$statistic + res$SDEM_RSWX$statistic))
+#> [1] TRUE
+summary(SD.RStests(lm_obj, nb2listw(col.gal.nb, style="C"), test="all"))
+#> Warning: Spatial weights matrix not row standardized
+#> 	Rao's score test spatial Durbin diagnostics
+#> data:  
+#> model: lm(formula = CRIME ~ INC + HOVAL, data = columbus)
+#> weights: nb2listw(col.gal.nb, style = "C")
+#>  
+#>              statistic parameter  p.value   
+#> SDM_RSlag      10.6095         1 0.001125 **
+#> SDM_adjRSlag    4.8428         1 0.027762 * 
+#> SDM_RSWX        9.2305         3 0.026378 * 
+#> SDM_adjRSWX     3.4637         3 0.325498   
+#> SDM_Joint      14.0733         4 0.007065 **
+#> SDEM_RSerr      4.8428         1 0.027762 * 
+#> SDEM_RSWX       9.2305         3 0.026378 * 
+#> SDEM_Joint     14.0733         4 0.007065 **
+#> ---
+#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+summary(SD.RStests(lm_obj, col.listw, test="all", Durbin= ~ INC))
+#> 	Rao's score test spatial Durbin diagnostics
+#> data:  
+#> model: lm(formula = CRIME ~ INC + HOVAL, data = columbus)
+#> weights: col.listw
+#> Durbin: ~ INC
+#>  
+#>              statistic parameter  p.value   
+#> SDM_RSlag      7.85568         1 0.005066 **
+#> SDM_adjRSlag   3.16076         1 0.075428 . 
+#> SDM_RSWX       4.92399         1 0.026486 * 
+#> SDM_adjRSWX    0.22908         1 0.632210   
+#> SDM_Joint      8.08475         2 0.017556 * 
+#> SDEM_RSerr     4.61113         1 0.031765 * 
+#> SDEM_RSWX      4.92399         1 0.026486 * 
+#> SDEM_Joint     9.53512         2 0.008501 **
+#> ---
+#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+lm_obj0 <- lm(I(scale(CRIME)) ~ 0 + I(scale(INC)) + I(scale(HOVAL)),
+ data=columbus)
+summary(SD.RStests(lm_obj0, col.listw, test="all"))
+#> 	Rao's score test spatial Durbin diagnostics
+#> data:  
+#> model: lm(formula = I(scale(CRIME)) ~ 0 + I(scale(INC)) +
+#> I(scale(HOVAL)), data = columbus)
+#> weights: col.listw
+#>  
+#>              statistic parameter  p.value   
+#> SDM_RSlag       7.8250         1 0.005153 **
+#> SDM_adjRSlag    4.6111         1 0.031765 * 
+#> SDM_RSWX        6.0609         2 0.048295 * 
+#> SDM_adjRSWX     2.8470         2 0.240873   
+#> SDM_Joint      10.6720         3 0.013638 * 
+#> SDEM_RSerr      4.6111         1 0.031765 * 
+#> SDEM_RSWX       6.0609         2 0.048295 * 
+#> SDEM_Joint     10.6720         3 0.013638 * 
+#> ---
+#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+columbusNA <- columbus
+columbusNA$HOVAL[15] <- NA
+lm_objNA <- lm(CRIME ~ INC + HOVAL, data=columbusNA)
+summary(SD.RStests(lm_objNA, col.listw, test="all"))
+#> 	Rao's score test spatial Durbin diagnostics
+#> data:  
+#> model: lm(formula = CRIME ~ INC + HOVAL, data = columbusNA)
+#> weights: col.listw
+#>  
+#>              statistic parameter  p.value   
+#> SDM_RSlag       7.6010         1 0.005834 **
+#> SDM_adjRSlag    4.4716         1 0.034463 * 
+#> SDM_RSWX        6.0812         2 0.047807 * 
+#> SDM_adjRSWX     2.9518         2 0.228576   
+#> SDM_Joint      10.5527         3 0.014407 * 
+#> SDEM_RSerr      4.4716         1 0.034463 * 
+#> SDEM_RSWX       6.0812         2 0.047807 * 
+#> SDEM_Joint     10.5527         3 0.014407 * 
+#> ---
+#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+
+
+ +
+ + +
+

Developed by Roger Bivand.

+
+ +
+

Site built with pkgdown 2.0.7.

+
+ +
+ + + + + + + + diff --git a/docs/reference/bhicv.html b/docs/reference/bhicv.html index f8ecaca4..cc5502bd 100644 --- a/docs/reference/bhicv.html +++ b/docs/reference/bhicv.html @@ -105,7 +105,7 @@

Examples

bh <- st_read(system.file("etc/shapes/bhicv.shp",
       package="spdep")[1])
 #> Reading layer `bhicv' from data source 
-#>   `/tmp/Rtmp0wv3u5/temp_libpath4cfe661e8f5da/spdep/etc/shapes/bhicv.shp' 
+#>   `/tmp/Rtmpza6q35/temp_libpath88034516afce7/spdep/etc/shapes/bhicv.shp' 
 #>   using driver `ESRI Shapefile'
 #> Simple feature collection with 98 features and 8 fields
 #> Geometry type: POLYGON
diff --git a/docs/reference/compon.html b/docs/reference/compon.html
index 631f059c..acbd528c 100644
--- a/docs/reference/compon.html
+++ b/docs/reference/compon.html
@@ -138,10 +138,14 @@ 
Examples

 #>     set.mcOption
 if (run) {
 B <- as(nb2listw(col2, style="B", zero.policy=TRUE), "CsparseMatrix")
-g1 <- graph.adjacency(B, mode="undirected")
-c1 <- clusters(g1)
+g1 <- graph.adjacency(B, mode="undirected")
+c1 <- clusters(g1)
 print(c1$no == res$nc)
 }
+#> Warning: `graph.adjacency()` was deprecated in igraph 2.0.0.
+#> ℹ Please use `graph_from_adjacency_matrix()` instead.
+#> Warning: `clusters()` was deprecated in igraph 2.0.0.
+#> ℹ Please use `components()` instead.
 #> [1] TRUE
 if (run) {
 print(all.equal(c1$membership, res$comp.id))
@@ -153,8 +157,8 @@ 
Examples

 #> [1] TRUE
 if (run) {
 W <- as(nb2listw(col2, style="W", zero.policy=TRUE), "CsparseMatrix")
-g1W <- graph.adjacency(W, mode="directed", weighted="W")
-c1W <- clusters(g1W)
+g1W <- graph.adjacency(W, mode="directed", weighted="W")
+c1W <- clusters(g1W)
 print(all.equal(c1W$membership, res$comp.id, check.attributes=FALSE))
 }
 #> [1] TRUE
@@ -162,14 +166,13 @@ 
Examples

 ow <- options("warn")$warn
 options("warn"=2L)
 # Matrix 1.4-2 vulnerability work-around
-B1 <- try(get.adjacency(g1), silent=TRUE)
+B1 <- try(get.adjacency(g1), silent=TRUE)
 if (!inherits(B1, "try-error")) {
 #B1 <- get.adjacency(g1)
 print(all.equal(B, B1))
 }
 options("warn"=ow)
 }
-#> [1] TRUE
diff --git a/docs/reference/dnearneigh.html b/docs/reference/dnearneigh.html index a89a4150..21ab546d 100644 --- a/docs/reference/dnearneigh.html +++ b/docs/reference/dnearneigh.html @@ -239,13 +239,13 @@

Examples

gck1b <- knn2nb(knearneigh(xy1, k=1)) system.time(o <- nbdists(gck1b, xy1)) #> user system elapsed -#> 0.006 0.000 0.006 +#> 0.006 0.000 0.007 (all.linked <- max(unlist(o))) #> [1] 522.4464 # use s2 brute-force dwithin_matrix approach for s2 <= 1.0.7 system.time(gc.nb.dwithin <- dnearneigh(xy1, 0, all.linked, use_s2=TRUE, dwithin=TRUE)) #> user system elapsed -#> 0.009 0.001 0.009 +#> 0.01 0.00 0.01 summary(gc.nb, xy1, scale=0.5) #> Neighbour list object: #> Number of regions: 48 @@ -271,7 +271,7 @@

Examples

system.time(gc.nb.dwithin <- dnearneigh(xy1, 0, all.linked, use_s2=TRUE, dwithin=TRUE)) } #> user system elapsed -#> 0.009 0.000 0.009 +#> 0.01 0.00 0.01 if (packageVersion("s2") > "1.0.7") { summary(gc.nb.dwithin, xy1, scale=0.5) } diff --git a/docs/reference/edit.nb.html b/docs/reference/edit.nb.html index d31b013d..0e2d5b40 100644 --- a/docs/reference/edit.nb.html +++ b/docs/reference/edit.nb.html @@ -121,12 +121,11 @@

See also

Examples

- 
# \dontrun{
+    
if (FALSE) {
 columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
 class(columbus)
-#> [1] "sf"         "data.frame"
 if (FALSE) nnb1 <- edit.nb(col.gal.nb, polys=as(columbus, "Spatial"))
-# }
+}
diff --git a/docs/reference/geary.mc.html b/docs/reference/geary.mc.html index f41c26cd..2a32b386 100644 --- a/docs/reference/geary.mc.html +++ b/docs/reference/geary.mc.html @@ -73,7 +73,7 @@

Permutation test for Geary's C statistic

geary.mc(x, listw, nsim, zero.policy=attr(listw, "zero.policy"), alternative="greater",
- spChk=NULL, adjust.n=TRUE, return_boot=FALSE)
+ spChk=NULL, adjust.n=TRUE, return_boot=FALSE, na.action=na.fail)
@@ -102,6 +102,8 @@

Arguments

return_boot

return an object of class boot from the equivalent permutation bootstrap rather than an object of class htest

+
na.action
+

a function (default na.fail), can also be na.omit or na.exclude - in these cases the weights list will be subsetted to remove NAs in the data. It may be necessary to set zero.policy to TRUE because this subsetting may create no-neighbour observations. Note that only weights lists created without using the glist argument to nb2listw may be subsetted. na.pass is not permitted because it is meaningless in a permutation test.

Value

@@ -146,6 +148,7 @@

See also

Examples

data(oldcol)
+set.seed(1)
 sim1 <- geary.mc(COL.OLD$CRIME, nb2listw(COL.nb, style="W"),
  nsim=99, alternative="less")
 sim1
@@ -153,19 +156,19 @@ 
Examples

 #> 	Monte-Carlo simulation of Geary C
 #> 
 #> data:  COL.OLD$CRIME 
-#> weights: nb2listw(COL.nb, style = "W") 
+#> weights: nb2listw(COL.nb, style = "W")  
 #> number of simulations + 1: 100 
 #> 
 #> statistic = 0.52987, observed rank = 1, p-value = 0.99
 #> alternative hypothesis: less
 #> 
 mean(sim1$res)
-#> [1] 0.9905898
+#> [1] 1.002705
 var(sim1$res)
-#> [1] 0.01235372
+#> [1] 0.01043616
 summary(sim1$res)
 #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>  0.5299  0.9225  1.0000  0.9906  1.0710  1.2144 
+#>  0.5299  0.9362  1.0141  1.0027  1.0673  1.2459 
 colold.lags <- nblag(COL.nb, 3)
 sim2 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[2]],
  style="W"), nsim=99)
@@ -174,15 +177,15 @@ 
Examples

 #> 	Monte-Carlo simulation of Geary C
 #> 
 #> data:  COL.OLD$CRIME 
-#> weights: nb2listw(colold.lags[[2]], style = "W") 
+#> weights: nb2listw(colold.lags[[2]], style = "W")  
 #> number of simulations + 1: 100 
 #> 
-#> statistic = 0.81129, observed rank = 2, p-value = 0.02
+#> statistic = 0.81129, observed rank = 1, p-value = 0.01
 #> alternative hypothesis: greater
 #> 
 summary(sim2$res)
 #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>  0.8090  0.9579  1.0123  1.0075  1.0575  1.1845 
+#>  0.8113  0.9500  1.0173  1.0147  1.0731  1.1962 
 sim3 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[3]],
  style="W"), nsim=99)
 sim3
@@ -190,15 +193,85 @@ 
Examples

 #> 	Monte-Carlo simulation of Geary C
 #> 
 #> data:  COL.OLD$CRIME 
-#> weights: nb2listw(colold.lags[[3]], style = "W") 
+#> weights: nb2listw(colold.lags[[3]], style = "W")  
 #> number of simulations + 1: 100 
 #> 
-#> statistic = 1.1303, observed rank = 93, p-value = 0.93
+#> statistic = 1.1303, observed rank = 89, p-value = 0.89
 #> alternative hypothesis: greater
 #> 
 summary(sim3$res)
 #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>  0.8015  0.9369  1.0017  0.9932  1.0467  1.1948 
+#>  0.7903  0.9609  0.9989  1.0148  1.0656  1.2726 
+crime <- COL.OLD$CRIME
+is.na(crime) <- sample(1:length(crime), 10)
+try(geary.mc(crime, nb2listw(COL.nb, style="W"), nsim=99,
+ na.action=na.fail))
+#> Error in na.fail.default(x) : missing values in object
+geary.mc(crime, nb2listw(COL.nb, style="W"), nsim=99, zero.policy=TRUE,
+ na.action=na.omit)
+#> Warning: subsetting caused increase in subgraph count
+#> 
+#> 	Monte-Carlo simulation of Geary C
+#> 
+#> data:  crime 
+#> weights: nb2listw(COL.nb, style = "W") 
+#> omitted: 14, 16, 17, 21, 24, 31, 32, 42, 44, 47 
+#> number of simulations + 1: 100 
+#> 
+#> statistic = 0.49368, observed rank = 1, p-value = 0.01
+#> alternative hypothesis: greater
+#> 
+geary.mc(crime, nb2listw(COL.nb, style="W"), nsim=99, zero.policy=TRUE,
+ return_boot=TRUE, na.action=na.omit)
+#> Warning: subsetting caused increase in subgraph count
+#> NA observations omitted: 14, 16, 17, 21, 24, 31, 32, 42, 44, 47
+#> 
+#> DATA PERMUTATION
+#> 
+#> 
+#> Call:
+#> boot(data = x, statistic = geary_boot, R = nsim, sim = "permutation", 
+#>     listw = listw, n = n, n1 = wc$n1, S0 = wc$S0, zero.policy = zero.policy, 
+#>     parallel = parallel, ncpus = ncpus, cl = cl)
+#> 
+#> 
+#> Bootstrap Statistics :
+#>      original    bias    std. error
+#> t1* 0.4936767 0.4891213   0.1517218
+geary.mc(crime, nb2listw(COL.nb, style="W"), nsim=99, zero.policy=TRUE,
+ na.action=na.exclude)
+#> Warning: subsetting caused increase in subgraph count
+#> 
+#> 	Monte-Carlo simulation of Geary C
+#> 
+#> data:  crime 
+#> weights: nb2listw(COL.nb, style = "W") 
+#> omitted: 14, 16, 17, 21, 24, 31, 32, 42, 44, 47 
+#> number of simulations + 1: 100 
+#> 
+#> statistic = 0.49368, observed rank = 1, p-value = 0.01
+#> alternative hypothesis: greater
+#> 
+geary.mc(crime, nb2listw(COL.nb, style="W"), nsim=99, zero.policy=TRUE,
+ return_boot=TRUE, na.action=na.exclude)
+#> Warning: subsetting caused increase in subgraph count
+#> NA observations omitted: 14, 16, 17, 21, 24, 31, 32, 42, 44, 47
+#> 
+#> DATA PERMUTATION
+#> 
+#> 
+#> Call:
+#> boot(data = x, statistic = geary_boot, R = nsim, sim = "permutation", 
+#>     listw = listw, n = n, n1 = wc$n1, S0 = wc$S0, zero.policy = zero.policy, 
+#>     parallel = parallel, ncpus = ncpus, cl = cl)
+#> 
+#> 
+#> Bootstrap Statistics :
+#>      original    bias    std. error
+#> t1* 0.4936767 0.4570379   0.1307276
+try(geary.mc(crime, nb2listw(COL.nb, style="W"), nsim=99, na.action=na.pass))
+#> Error in geary.mc(crime, nb2listw(COL.nb, style = "W"), nsim = 99, na.action = na.pass) : 
+#>   na.pass not permitted
diff --git a/docs/reference/geary.test.html b/docs/reference/geary.test.html index 049c5dcf..54c9a5ba 100644 --- a/docs/reference/geary.test.html +++ b/docs/reference/geary.test.html @@ -73,7 +73,7 @@

Geary's C test for spatial autocorrelation

geary.test(x, listw, randomisation=TRUE, zero.policy=attr(listw, "zero.policy"),
-    alternative="greater", spChk=NULL, adjust.n=TRUE)
+ alternative="greater", spChk=NULL, adjust.n=TRUE, na.action=na.fail)
@@ -99,6 +99,9 @@

Arguments

adjust.n

default TRUE, if FALSE the number of observations is not adjusted for no-neighbour observations, if TRUE, the number of observations is adjusted

+
na.action
+

a function (default na.fail), can also be na.omit or na.exclude - in these cases the weights list will be subsetted to remove NAs in the data. It may be necessary to set zero.policy to TRUE because this subsetting may create no-neighbour observations. Note that only weights lists created without using the glist argument to nb2listw may be subsetted. na.pass is not permitted.

+

Value

@@ -151,7 +154,7 @@

Examples

#> Geary C test under randomisation #> #> data: COL.OLD$CRIME -#> weights: nb2listw(COL.nb, style = "W") +#> weights: nb2listw(COL.nb, style = "W") #> #> Geary C statistic standard deviate = 4.7605, p-value = 9.655e-07 #> alternative hypothesis: Expectation greater than statistic @@ -165,7 +168,7 @@

Examples

#> Geary C test under normality #> #> data: COL.OLD$CRIME -#> weights: nb2listw(COL.nb, style = "W") +#> weights: nb2listw(COL.nb, style = "W") #> #> Geary C statistic standard deviate = 4.6388, p-value = 1.752e-06 #> alternative hypothesis: Expectation greater than statistic @@ -180,7 +183,7 @@

Examples

#> Geary C test under randomisation #> #> data: COL.OLD$CRIME -#> weights: nb2listw(colold.lags[[2]], style = "W") +#> weights: nb2listw(colold.lags[[2]], style = "W") #> #> Geary C statistic standard deviate = 2.2896, p-value = 0.01102 #> alternative hypothesis: Expectation greater than statistic @@ -194,7 +197,7 @@

Examples

#> Geary C test under randomisation #> #> data: COL.OLD$CRIME -#> weights: nb2listw(colold.lags[[3]], style = "W") +#> weights: nb2listw(colold.lags[[3]], style = "W") #> #> Geary C statistic standard deviate = -1.5667, p-value = 0.9414 #> alternative hypothesis: Expectation greater than statistic @@ -213,7 +216,7 @@

Examples

#> Geary C test under randomisation #> #> data: COL.OLD$CRIME -#> weights: nb2listw(COL.k4.nb, style = "W") +#> weights: nb2listw(COL.k4.nb, style = "W") #> #> Geary C statistic standard deviate = 6.4415, p-value = 5.916e-11 #> alternative hypothesis: Expectation greater than statistic @@ -227,7 +230,7 @@

Examples

#> Geary C test under normality #> #> data: COL.OLD$CRIME -#> weights: nb2listw(COL.k4.nb, style = "W") +#> weights: nb2listw(COL.k4.nb, style = "W") #> #> Geary C statistic standard deviate = 6.2873, p-value = 1.615e-10 #> alternative hypothesis: Expectation greater than statistic @@ -243,7 +246,7 @@

Examples

#> Geary C test under randomisation #> #> data: COL.OLD$CRIME -#> weights: listw2U(nb2listw(COL.k4.nb, style = "W")) +#> weights: listw2U(nb2listw(COL.k4.nb, style = "W")) #> #> Geary C statistic standard deviate = 6.4415, p-value = 5.916e-11 #> alternative hypothesis: Expectation greater than statistic @@ -257,7 +260,7 @@

Examples

#> Geary C test under normality #> #> data: COL.OLD$CRIME -#> weights: listw2U(nb2listw(COL.k4.nb, style = "W")) +#> weights: listw2U(nb2listw(COL.k4.nb, style = "W")) #> #> Geary C statistic standard deviate = 6.2873, p-value = 1.615e-10 #> alternative hypothesis: Expectation greater than statistic @@ -265,6 +268,43 @@

Examples

#> Geary C statistic Expectation Variance #> 0.399254423 1.000000000 0.009129529 #> +crime <- COL.OLD$CRIME +is.na(crime) <- sample(1:length(crime), 10) +try(geary.test(crime, nb2listw(COL.nb, style="W"), na.action=na.fail)) +#> Error in na.fail.default(x) : missing values in object +geary.test(crime, nb2listw(COL.nb, style="W"), zero.policy=TRUE, + na.action=na.omit) +#> +#> Geary C test under randomisation +#> +#> data: crime +#> weights: nb2listw(COL.nb, style = "W") +#> omitted: 3, 4, 10, 20, 21, 23, 27, 29, 31, 38 +#> +#> Geary C statistic standard deviate = 4.2726, p-value = 9.661e-06 +#> alternative hypothesis: Expectation greater than statistic +#> sample estimates: +#> Geary C statistic Expectation Variance +#> 0.45199742 1.00000000 0.01645071 +#> +geary.test(crime, nb2listw(COL.nb, style="W"), zero.policy=TRUE, + na.action=na.exclude) +#> +#> Geary C test under randomisation +#> +#> data: crime +#> weights: nb2listw(COL.nb, style = "W") +#> omitted: 3, 4, 10, 20, 21, 23, 27, 29, 31, 38 +#> +#> Geary C statistic standard deviate = 4.2726, p-value = 9.661e-06 +#> alternative hypothesis: Expectation greater than statistic +#> sample estimates: +#> Geary C statistic Expectation Variance +#> 0.45199742 1.00000000 0.01645071 +#> +try(geary.test(crime, nb2listw(COL.nb, style="W"), na.action=na.pass)) +#> Error in geary.test(crime, nb2listw(COL.nb, style = "W"), na.action = na.pass) : +#> na.pass not permitted
diff --git a/docs/reference/globalG.test.html b/docs/reference/globalG.test.html index d2cca182..d391e8a6 100644 --- a/docs/reference/globalG.test.html +++ b/docs/reference/globalG.test.html @@ -73,7 +73,8 @@

Global G test for spatial autocorrelation

globalG.test(x, listw, zero.policy=attr(listw, "zero.policy"), alternative="greater",
- spChk=NULL, adjust.n=TRUE, B1correct=TRUE, adjust.x=TRUE, Arc_all_x=FALSE)
+ spChk=NULL, adjust.n=TRUE, B1correct=TRUE, adjust.x=TRUE, Arc_all_x=FALSE, + na.action=na.fail)
@@ -105,6 +106,9 @@

Arguments

Arc_all_x

default FALSE, if Arc_all_x=TRUE and adjust.x=TRUE, use the full x vector in part of the denominator term for G

+
na.action
+

a function (default na.fail), can also be na.omit or na.exclude - in these cases the weights list will be subsetted to remove NAs in the data. It may be necessary to set zero.policy to TRUE because this subsetting may create no-neighbour observations. Note that only weights lists created without using the glist argument to nb2listw may be subsetted. na.pass is not permitted.

+

Value

@@ -189,6 +193,55 @@

Examples

#> 80 0.24606972 -0.18791364 0.8509443 #> 90 0.30073463 0.11457610 0.9087811 #> 100 0.34879996 0.31591356 0.7520681 +data(oldcol) +crime <- COL.OLD$CRIME +is.na(crime) <- sample(1:length(crime), 10) +res <- try(globalG.test(crime, nb2listw(COL.nb, style="B"), + na.action=na.fail)) +#> Error in na.fail.default(x) : missing values in object +res +#> [1] "Error in na.fail.default(x) : missing values in object\n" +#> attr(,"class") +#> [1] "try-error" +#> attr(,"condition") +#> <simpleError in na.fail.default(x): missing values in object> +globalG.test(crime, nb2listw(COL.nb, style="B"), zero.policy=TRUE, + na.action=na.omit) +#> Warning: subsetting caused increase in subgraph count +#> +#> Getis-Ord global G statistic +#> +#> data: crime +#> weights: nb2listw(COL.nb, style = "B") +#> omitted: 2, 3, 5, 6, 7, 10, 13, 30, 39, 49 +#> n reduced by no-neighbour observations +#> +#> standard deviate = 3.8084, p-value = 6.993e-05 +#> alternative hypothesis: greater +#> sample estimates: +#> Global G statistic Expectation Variance +#> 1.441233e-01 1.123755e-01 6.949287e-05 +#> +globalG.test(crime, nb2listw(COL.nb, style="B"), zero.policy=TRUE, + na.action=na.exclude) +#> Warning: subsetting caused increase in subgraph count +#> +#> Getis-Ord global G statistic +#> +#> data: crime +#> weights: nb2listw(COL.nb, style = "B") +#> omitted: 2, 3, 5, 6, 7, 10, 13, 30, 39, 49 +#> n reduced by no-neighbour observations +#> +#> standard deviate = 3.8084, p-value = 6.993e-05 +#> alternative hypothesis: greater +#> sample estimates: +#> Global G statistic Expectation Variance +#> 1.441233e-01 1.123755e-01 6.949287e-05 +#> +try(globalG.test(crime, nb2listw(COL.nb, style="B"), na.action=na.pass)) +#> Error in globalG.test(crime, nb2listw(COL.nb, style = "B"), na.action = na.pass) : +#> na.pass not permitted
diff --git a/docs/reference/index.html b/docs/reference/index.html index a4c45098..003e8a1c 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -98,9 +98,9 @@

All functions

Bootstrapping-based test for local spatial heteroscedasticity

-

SDM.RStests()

+

SD.RStests()

-

Rao's score and adjusted Rao's score tests of linear hypotheses for spatial Durbin models

+

Rao's score and adjusted Rao's score tests of linear hypotheses for spatial Durbin and spatial Durbin error models

aggregate(<nb>)

@@ -230,9 +230,9 @@

All functions

Spatial neighbour sparse representation

-

lm.LMtests() print(<LMtestlist>) summary(<LMtestlist>) print(<LMtestlist.summary>)

+

lm.RStests() lm.LMtests() print(<RStestlist>) summary(<RStestlist>) print(<RStestlist.summary>)

-

Lagrange Multiplier diagnostics for spatial dependence in linear models

+

Rao's score (a.k.a Lagrange Multiplier) diagnostics for spatial dependence in linear models

lm.morantest()

diff --git a/docs/reference/joincount.multi.html b/docs/reference/joincount.multi.html index 2816e9dc..d5d04d09 100644 --- a/docs/reference/joincount.multi.html +++ b/docs/reference/joincount.multi.html @@ -161,95 +161,30 @@

Examples

#> high:high 52.000 28.138 18.342 5.5716 #> high:low 28.000 61.170 25.882 -6.5200 #> Jtot 28.000 61.170 33.190 -5.7577 -# \dontrun{ +if (FALSE) { data(oldcol) HICRIME <- cut(COL.OLD$CRIME, breaks=c(0,35,80), labels=c("low","high")) names(HICRIME) <- rownames(COL.OLD) joincount.multi(HICRIME, nb2listw(COL.nb, style="B")) -#> Joincount Expected Variance z-value -#> low:low 34.000 29.592 18.895 1.0141 -#> high:high 54.000 27.224 17.888 6.3307 -#> high:low 28.000 59.184 26.233 -6.0884 -#> Jtot 28.000 59.184 26.233 -6.0884 data(hopkins, package="spData") image(1:32, 1:32, hopkins[5:36,36:5], breaks=c(-0.5, 3.5, 20), col=c("white", "black")) box() - hopkins.rook.nb <- cell2nb(32, 32, type="rook") unlist(spweights.constants(nb2listw(hopkins.rook.nb, style="B"))) -#> n n1 n2 n3 nn S0 S1 S2 -#> 1024 1023 1022 1021 1048576 3968 7936 61984 hopkins.queen.nb <- cell2nb(32, 32, type="queen") hopkins.bishop.nb <- diffnb(hopkins.rook.nb, hopkins.queen.nb, verbose=FALSE) hopkins4 <- hopkins[5:36,36:5] hopkins4[which(hopkins4 > 3, arr.ind=TRUE)] <- 4 hopkins4.f <- factor(hopkins4) table(hopkins4.f) -#> hopkins4.f -#> 0 1 2 3 4 -#> 657 215 98 30 24 joincount.multi(hopkins4.f, nb2listw(hopkins.rook.nb, style="B")) -#> Joincount Expected Variance z-value -#> 0:0 864.00000 816.27273 116.05233 4.4304 -#> 1:1 94.00000 87.14015 55.25216 0.9229 -#> 2:2 18.00000 18.00379 14.81562 -0.0010 -#> 3:3 2.00000 1.64773 1.55539 0.2825 -#> 4:4 5.00000 1.04545 0.99845 3.9576 -#> 1:0 503.00000 535.05682 227.76750 -2.1241 -#> 2:0 213.00000 243.88636 97.21769 -3.1325 -#> 2:1 99.00000 79.81061 59.01930 2.4978 -#> 3:0 61.00000 74.65909 28.58592 -2.5547 -#> 3:1 28.00000 24.43182 18.99976 0.8186 -#> 3:2 15.00000 11.13636 9.82411 1.2327 -#> 4:0 40.00000 59.72727 22.78583 -4.1327 -#> 4:1 23.00000 19.54545 15.26564 0.8842 -#> 4:2 14.00000 8.90909 7.90051 1.8112 -#> 4:3 5.00000 2.72727 2.58616 1.4133 -#> Jtot 1001.00000 1059.89015 273.78610 -3.5591 cat("replicates Upton & Fingleton table 3.4 (p. 166)\n") -#> replicates Upton & Fingleton table 3.4 (p. 166) joincount.multi(hopkins4.f, nb2listw(hopkins.bishop.nb, style="B")) -#> Joincount Expected Variance z-value -#> 0:0 823.00000 790.76420 144.44877 2.6821 -#> 1:1 101.00000 84.41702 55.98143 2.2164 -#> 2:2 19.00000 17.44117 14.61542 0.4077 -#> 3:3 3.00000 1.59624 1.51444 1.1407 -#> 4:4 3.00000 1.01278 0.97111 2.0166 -#> 1:0 497.00000 518.33629 234.93545 -1.3920 -#> 2:0 216.00000 236.26491 104.42142 -1.9831 -#> 2:1 81.00000 77.31652 58.70829 0.4807 -#> 3:0 58.00000 72.32599 31.49151 -2.5529 -#> 3:1 21.00000 23.66832 18.85316 -0.6145 -#> 3:2 17.00000 10.78835 9.62487 2.0022 -#> 4:0 48.00000 57.86080 25.15973 -1.9659 -#> 4:1 21.00000 18.93466 15.14473 0.5307 -#> 4:2 10.00000 8.63068 7.73708 0.4923 -#> 4:3 4.00000 2.64205 2.51686 0.8560 -#> Jtot 973.00000 1026.76858 284.51030 -3.1877 cat("replicates Upton & Fingleton table 3.6 (p. 168)\n") -#> replicates Upton & Fingleton table 3.6 (p. 168) joincount.multi(hopkins4.f, nb2listw(hopkins.queen.nb, style="B")) -#> Joincount Expected Variance z-value -#> 0:0 1687.0000 1607.0369 303.8034 4.5877 -#> 1:1 195.0000 171.5572 114.2057 2.1936 -#> 2:2 37.0000 35.4450 29.6821 0.2854 -#> 3:3 5.0000 3.2440 3.0687 1.0024 -#> 4:4 8.0000 2.0582 1.9674 4.2361 -#> 1:0 1000.0000 1053.3931 480.6959 -2.4353 -#> 2:0 429.0000 480.1513 215.0360 -3.4882 -#> 2:1 180.0000 157.1271 119.3987 2.0932 -#> 3:0 119.0000 146.9851 65.1029 -3.4684 -#> 3:1 49.0000 48.1001 38.3268 0.1454 -#> 3:2 32.0000 21.9247 19.5237 2.2802 -#> 4:0 88.0000 117.5881 52.0312 -4.1019 -#> 4:1 44.0000 38.4801 30.7868 0.9948 -#> 4:2 24.0000 17.5398 15.6933 1.6308 -#> 4:3 9.0000 5.3693 5.0994 1.6078 -#> Jtot 1974.0000 2086.6587 582.8326 -4.6665 cat("replicates Upton & Fingleton table 3.7 (p. 169)\n") -#> replicates Upton & Fingleton table 3.7 (p. 169) -# } +} diff --git a/docs/reference/knearneigh.html b/docs/reference/knearneigh.html index 6681e5d4..258cdbbd 100644 --- a/docs/reference/knearneigh.html +++ b/docs/reference/knearneigh.html @@ -191,7 +191,7 @@

Examples

sf_use_s2(TRUE) system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4))) #> user system elapsed -#> 0.009 0.000 0.008 +#> 0.011 0.000 0.012 summary(gck4a.nb, xy1, scale=0.5) #> Neighbour list object: #> Number of regions: 48 @@ -211,7 +211,7 @@

Examples

#> Spherical geometry (s2) switched off system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4))) #> user system elapsed -#> 0.005 0.000 0.005 +#> 0.006 0.000 0.006 summary(gck4a.nb, xy1, scale=0.5) #> Neighbour list object: #> Number of regions: 48 diff --git a/docs/reference/lm.RStests.html b/docs/reference/lm.RStests.html new file mode 100644 index 00000000..1fe4bba9 --- /dev/null +++ b/docs/reference/lm.RStests.html @@ -0,0 +1,277 @@ + +Rao's score (a.k.a Lagrange Multiplier) diagnostics for spatial dependence in linear models — lm.LMtests • spdep + + +
+
+ + + +
+
+
+

Rao's score (a.k.a Lagrange Multiplier) diagnostics for spatial dependence in linear models

+ + +
+ +
+

The function reports the estimates of tests chosen among five statistics for +testing for spatial dependence in linear models. The statistics are +the simple RS test for error dependence (“RSerr”), the simple RS test +for a missing spatially lagged dependent variable (“RSlag”), variants +of these adjusted for the presence of the other (“adjRSerr” +tests for error dependence in the possible presence of a missing lagged +dependent variable, “adjRSlag” the other way round), and a portmanteau test +(“SARMA”, in fact “RSerr” + “adjRSlag”). Note: from spdep 1.3-2, the tests are re-named “RS” - Rao's score tests, rather than “LM” - Lagrange multiplier tests to match the naming of tests from the same family in SDM.RStests.

+
+ +
+ 
lm.RStests(model, listw, zero.policy=attr(listw, "zero.policy"), test="RSerr",
+ spChk=NULL, naSubset=TRUE)
+lm.LMtests(model, listw, zero.policy=attr(listw, "zero.policy"), test="LMerr",
+ spChk=NULL, naSubset=TRUE)
+# S3 method for RStestlist
+print(x, ...)
+# S3 method for RStestlist
+summary(object, p.adjust.method="none", ...)
+# S3 method for RStestlist.summary
+print(x, digits=max(3, getOption("digits") - 2), ...)
+<!-- %tracew(listw) -->
+
+ +
+

Arguments

+
model
+

an object of class lm returned by lm, or optionally a vector of externally calculated residuals (run though na.omit if any NAs present) for use when only "RSerr" is chosen; weights and offsets should not be used in the lm object

+ +
listw
+

a listw object created for example by nb2listw, +expected to be row-standardised (W-style)

+ +
zero.policy
+

default attr(listw, "zero.policy") as set when listw was created, if attribute not set, use global option value; if TRUE assign zero to the lagged value of zones without +neighbours, if FALSE assign NA

+ +
test
+

a character vector of tests requested chosen from RSerr, RSlag, +adjRSerr, adjRSlag, SARMA; test="all" computes all the tests.

+ +
spChk
+

should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use get.spChkOption()

+ +
naSubset
+

default TRUE to subset listw object for omitted observations in model object (this is a change from earlier behaviour, when the model$na.action component was ignored, and the listw object had to be subsetted by hand)

+ +
x, object
+

object to be printed

+ +
p.adjust.method
+

a character string specifying the probability value adjustment (see p.adjust) for multiple tests, default "none"

+ +
digits
+

minimum number of significant digits to be used for most numbers

+ +
...
+

printing arguments to be passed through

+ +
+
+

Details

+

The two types of dependence are for spatial lag \(\rho\) and spatial error \(\lambda\):

+

$$ +\mathbf{y} = \mathbf{X \beta} + \rho \mathbf{W_{(1)} y} + \mathbf{u}, +$$ +$$ +\mathbf{u} = \lambda \mathbf{W_{(2)} u} + \mathbf{e} +$$

+

where \(\mathbf{e}\) is a well-behaved, uncorrelated error +term. Tests for a missing spatially lagged dependent variable test +that \(\rho = 0\), tests for spatial autocorrelation of +the error \(\mathbf{u}\) test whether \(\lambda = 0\). \(\mathbf{W}\) is a spatial weights matrix; for the tests used +here they are identical.

+
+
+

Value

+ + +

A list of class RStestlist of htest objects, each with:

+
statistic
+

the value of the Rao's score (a.k.a Lagrange multiplier) test.

+ +
parameter
+

number of degrees of freedom

+ +
p.value
+

the p-value of the test.

+ +
method
+

a character string giving the method used.

+ +
data.name
+

a character string giving the name(s) of the data.

+ +
+
+

References

+

Anselin, L. 1988 Spatial econometrics: methods and +models. (Dordrecht: Kluwer); Anselin, L., Bera, A. K., Florax, R. and +Yoon, M. J. 1996 Simple diagnostic tests for spatial dependence. Regional +Science and Urban Economics, 26, 77--104 doi:10.1016/0166-0462(95)02111-6 +; +Malabika Koley (2024) Specification Testing under General Nesting Spatial +Model, https://sites.google.com/view/malabikakoley/research.

+
+
+

Author

+

Roger Bivand Roger.Bivand@nhh.no and Andrew Bernat

+
+
+

See also

+

lm, SD.RStests

+
+ +
+

Examples

+ 
data(oldcol)
+oldcrime.lm <- lm(CRIME ~ HOVAL + INC, data = COL.OLD)
+summary(oldcrime.lm)
+#> 
+#> Call:
+#> lm(formula = CRIME ~ HOVAL + INC, data = COL.OLD)
+#> 
+#> Residuals:
+#>     Min      1Q  Median      3Q     Max 
+#> -34.418  -6.388  -1.580   9.052  28.649 
+#> 
+#> Coefficients:
+#>             Estimate Std. Error t value Pr(>|t|)    
+#> (Intercept)  68.6190     4.7355  14.490  < 2e-16 ***
+#> HOVAL        -0.2739     0.1032  -2.654   0.0109 *  
+#> INC          -1.5973     0.3341  -4.780 1.83e-05 ***
+#> ---
+#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+#> 
+#> Residual standard error: 11.43 on 46 degrees of freedom
+#> Multiple R-squared:  0.5524,	Adjusted R-squared:  0.5329 
+#> F-statistic: 28.39 on 2 and 46 DF,  p-value: 9.341e-09
+#> 
+lw <- nb2listw(COL.nb)
+res <- lm.RStests(oldcrime.lm, listw=lw, test="all")
+summary(res)
+#> 	Rao's score (a.k.a Lagrange multiplier) diagnostics for spatial
+#> 	dependence
+#> data:  
+#> model: lm(formula = CRIME ~ HOVAL + INC, data = COL.OLD)
+#> test weights: lw
+#>  
+#>          statistic parameter  p.value   
+#> RSerr     5.723131         1 0.016743 * 
+#> RSlag     9.363684         1 0.002213 **
+#> adjRSerr  0.079495         1 0.777983   
+#> adjRSlag  3.720048         1 0.053763 . 
+#> SARMA     9.443178         2 0.008901 **
+#> ---
+#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+if (require("spatialreg", quietly=TRUE)) {
+  oldcrime.slx <- lm(CRIME ~ HOVAL + INC, data = COL.OLD, listw=lw)
+  summary(lm.RStests(oldcrime.slx, listw=lw, test=c("adjRSerr", "adjRSlag")))
+}
+#> Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
+#>  extra argument ‘listw’ will be disregarded
+#> 	Rao's score (a.k.a Lagrange multiplier) diagnostics for spatial
+#> 	dependence
+#> data:  
+#> model: lm(formula = CRIME ~ HOVAL + INC, data = COL.OLD, listw = lw)
+#> test weights: lw
+#>  
+#>          statistic parameter p.value  
+#> adjRSerr  0.079495         1 0.77798  
+#> adjRSlag  3.720048         1 0.05376 .
+#> ---
+#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+
+
+ +
+ + +
+

Developed by Roger Bivand.

+
+ +
+

Site built with pkgdown 2.0.7.

+
+ +
+ + + + + + + + diff --git a/docs/reference/lm.morantest.html b/docs/reference/lm.morantest.html index 8c5cfbc1..3224a1a3 100644 --- a/docs/reference/lm.morantest.html +++ b/docs/reference/lm.morantest.html @@ -138,7 +138,7 @@

Author

See also

-

lm.LMtests, lm

+

lm.LMtests, lm

@@ -160,15 +160,17 @@

Examples

#> Observed Moran I Expectation Variance #> 0.235638354 -0.033302866 0.008289408 #> -lm.LMtests(oldcrime.lm, nb2listw(COL.nb, style="W")) +lm.LMtests(oldcrime.lm, nb2listw(COL.nb, style="W")) +#> Please update scripts to use lm.RStests in place of lm.LMtests #> -#> Lagrange multiplier diagnostics for spatial dependence +#> Rao's score (a.k.a Lagrange multiplier) diagnostics for spatial +#> dependence #> #> data: #> model: lm(formula = CRIME ~ HOVAL + INC, data = COL.OLD) -#> weights: nb2listw(COL.nb, style = "W") +#> test weights: listw #> -#> LMErr = 5.7231, df = 1, p-value = 0.01674 +#> RSerr = 5.7231, df = 1, p-value = 0.01674 #> lm.morantest(oldcrime.lm, nb2listw(COL.nb, style="S")) #> diff --git a/docs/reference/localC.html b/docs/reference/localC.html index 9c4d40c6..7446ae4d 100644 --- a/docs/reference/localC.html +++ b/docs/reference/localC.html @@ -245,11 +245,9 @@

Examples

} #> [1] 0.985611 # pseudo-p values probably wrongly folded https://github.com/GeoDaCenter/rgeoda/issues/28 -# \dontrun{ +if (FALSE) { tmap_ok <- FALSE if (require(tmap, quietly=TRUE)) tmap_ok <- TRUE -#> Breaking News: tmap 3.x is retiring. Please test v4, e.g. with -#> remotes::install_github('r-tmap/tmap') if (run) { # doi: 10.1111/gean.12164 guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda") @@ -263,25 +261,10 @@

Examples

moran(g$Infants, lw, n=nrow(g), S0=Szero(lw))$I moran(g$Suicids, lw, n=nrow(g), S0=Szero(lw))$I } -#> Reading layer `Guerry' from data source -#> `/home/rsb/lib/r_libs/rgeoda/extdata/Guerry.shp' using driver `ESRI Shapefile' -#> Simple feature collection with 85 features and 29 fields -#> Geometry type: MULTIPOLYGON -#> Dimension: XY -#> Bounding box: xmin: 47680 ymin: 1703258 xmax: 1031401 ymax: 2677441 -#> Projected CRS: NTF (Paris) / Lambert zone II -#> [1] 0.4016812 if (run) { o <- prcomp(st_drop_geometry(g), scale.=TRUE) cor(st_drop_geometry(g), o$x[,1:2])^2 #(Tab. 2) } -#> PC1 PC2 -#> Crm_prs 0.009286863 0.418851254 -#> Crm_prp 0.561825664 0.009377232 -#> Litercy 0.560570048 0.020095011 -#> Donatns 0.024138865 0.586720363 -#> Infants 0.436025658 0.012817054 -#> Suicids 0.548623327 0.152958961 if (run) { g$PC1 <- o$x[, "PC1"] brks <- c(min(g$PC1), natural_breaks(k=6, g["PC1"]), max(g$PC1)) @@ -289,7 +272,6 @@

Examples

tm_borders() # Fig. 1 else pplot(g["PC1"], breaks=brks) } - if (run) { g$PC2 <- -1*o$x[, "PC2"] # eigenvalue sign arbitrary brks <- c(min(g$PC2), natural_breaks(k=6, g["PC2"]), max(g$PC2)) @@ -297,7 +279,6 @@

Examples

tm_borders() # Fig. 2 else plot(g["PC2"], breaks=brks) } - if (run) { w <- queen_weights(g) lm_PC1 <- local_moran(w, g["PC1"], significance_cutoff=0.01, @@ -310,7 +291,6 @@

Examples

colorNA="gray95") + tm_borders() # Fig. 3 else plot(g["lm_PC1"]) } - if (run) { set.seed(1) lm_PC1_spdep <- localmoran_perm(g$PC1, lw, nsim=9999) @@ -322,7 +302,6 @@

Examples

colorNA="gray95") + tm_borders() # rep. Fig. 3 else plot(g["lm_PC1_spdep"]) } - if (run) { lg_PC1 <- local_g(w, g["PC1"], significance_cutoff=0.01, permutations=99999) @@ -340,7 +319,6 @@

Examples

colorNA="gray95") + tm_borders() # Fig. 4 (guess) else plot(g["lg_PC1"]) } - if (run) { lc_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.01, permutations=99999) @@ -352,14 +330,11 @@

Examples

colorNA="gray95") + tm_borders() # Fig. 5 else plot(g["lc_PC1"]) } - if (run) { set.seed(1) system.time(lc_PC1_spdep <- localC_perm(g$PC1, lw, nsim=9999, alternative="two.sided")) } -#> user system elapsed -#> 0.373 0.008 0.381 if (run) { if (require(parallel, quietly=TRUE)) { ncpus <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L @@ -372,7 +347,6 @@

Examples

set.coresOption(cores) } } -#> [1] 1 if (run) { g$lc_PC1_spdep <- attr(lc_PC1_spdep, "cluster") is.na(g$lc_PC1_spdep) <- attr(lc_PC1_spdep, "pseudo-p")[,6] > 0.01 @@ -381,7 +355,6 @@

Examples

colorNA="gray95") + tm_borders() # rep. Fig. 5 else plot(g["lc_PC1_spdep"]) } - if (run) { g$both_PC1 <- interaction(g$lc_PC1, g$lm_PC1) g$both_PC1 <- droplevels(g$both_PC1) @@ -389,7 +362,6 @@

Examples

colorNA="gray95") + tm_borders() # Fig. 6 else plot(g["both_PC1"]) } - if (run) { lc005_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.005, permutations=99999) @@ -401,7 +373,6 @@

Examples

colorNA="gray95") + tm_borders() # Fig. 7 else plot(g["lc005_PC1"]) } - if (run) { g$lc005_PC1_spdep <- attr(lc_PC1_spdep, "cluster") is.na(g$lc005_PC1_spdep) <- attr(lc_PC1_spdep, "pseudo-p")[,6] > 0.005 @@ -410,7 +381,6 @@

Examples

colorNA="gray95") + tm_borders() # rep. Fig. 7 else plot(g["lc005_PC1_spdep"]) } - if (run) { lc001_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.001, permutations=99999) @@ -430,7 +400,6 @@

Examples

else plot(g["lc001_PC1_spdep"]) } } - if (run) { lc_PC2 <- local_geary(w, g["PC2"], significance_cutoff=0.01, permutations=99999) @@ -442,7 +411,6 @@

Examples

colorNA="gray95") + tm_borders() # Fig. 9 else plot(g["lc_PC2"]) } - if (run) { lmc_PC <- local_multigeary(w, g[c("PC1","PC2")], significance_cutoff=0.00247, permutations=99999) @@ -452,25 +420,19 @@

Examples

g$lmc_PC <- droplevels(g$lmc_PC) table(interaction((p.adjust(lisa_pvalues(lmc_PC), "fdr") < 0.01), g$lmc_PC)) } -#> -#> FALSE.Positive TRUE.Positive -#> 0 21 if (run) { if (tmap_ok) tm_shape(g) + tm_fill("lmc_PC", textNA="Insignificant", colorNA="gray95") + tm_borders() # Fig. 10 else plot(g["lmc_PC"]) } - if (run) { set.seed(1) lmc_PC_spdep <- localC_perm(g[c("PC1","PC2")], lw, nsim=9999, alternative="two.sided") all.equal(lisa_values(lmc_PC), c(lmc_PC_spdep)) } -#> [1] TRUE if (run) { cor(attr(lmc_PC_spdep, "pseudo-p")[,6], lisa_pvalues(lmc_PC)) } -#> [1] 0.99052 if (run) { g$lmc_PC_spdep <- attr(lmc_PC_spdep, "cluster") is.na(g$lmc_PC_spdep) <- p.adjust(attr(lmc_PC_spdep, "pseudo-p")[,6], "fdr") > 0.01 @@ -479,7 +441,6 @@

Examples

colorNA="gray95") + tm_borders() # rep. Fig. 10 else plot(g["lmc_PC_spdep"]) } - if (run) { lmc_vars <- local_multigeary(w, st_drop_geometry(g)[, 1:6], significance_cutoff=0.00247, permutations=99999) @@ -490,30 +451,22 @@

Examples

table(interaction((p.adjust(lisa_pvalues(lmc_vars), "fdr") < 0.01), g$lmc_vars)) } -#> -#> FALSE.Positive TRUE.Positive -#> 0 21 if (run) { if (tmap_ok) tm_shape(g) + tm_fill("lmc_vars", textNA="Insignificant", colorNA="gray95") + tm_borders() # Fig. 11 else plot(g["lmc_vars"]) } - if (run) { set.seed(1) system.time(lmc_vars_spdep <- localC_perm(st_drop_geometry(g)[, 1:6], lw, nsim=9999, alternative="two.sided")) } -#> user system elapsed -#> 0.811 0.007 0.821 if (run) { all.equal(lisa_values(lmc_vars), c(lmc_vars_spdep)) } -#> [1] TRUE if (run) { cor(attr(lmc_vars_spdep, "pseudo-p")[,6], lisa_pvalues(lmc_vars)) } -#> [1] 0.9938141 if (run) { if (require(parallel, quietly=TRUE)) { ncpus <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L @@ -526,15 +479,12 @@

Examples

set.coresOption(cores) } } -#> [1] 1 if (run) { all.equal(lisa_values(lmc_vars), c(lmc_vars_spdep1)) } -#> [1] TRUE if (run) { cor(attr(lmc_vars_spdep1, "pseudo-p")[,6], lisa_pvalues(lmc_vars)) } -#> [1] 0.9938141 if (run) { g$lmc_vars_spdep <- attr(lmc_vars_spdep1, "cluster") is.na(g$lmc_vars_spdep) <- p.adjust(attr(lmc_vars_spdep1, "pseudo-p")[,6], "fdr") > 0.01 @@ -543,50 +493,31 @@

Examples

colorNA="gray95") + tm_borders() # rep. Fig. 11 else plot(g["lmc_vars_spdep"]) } - -# } -# \dontrun{ +} +if (FALSE) { library(reticulate) use_python("/usr/bin/python", required = TRUE) gp <- import("geopandas") -#> Error in py_module_import(module, convert = convert): ModuleNotFoundError: No module named 'geopandas' -#> Run `reticulate::py_last_error()` for details. ps <- import("libpysal") -#> Error in py_module_import(module, convert = convert): ModuleNotFoundError: No module named 'libpysal' -#> Run `reticulate::py_last_error()` for details. W <- listw2mat(listw) w <- ps$weights$full2W(W, rownames(W)) -#> Error in eval(expr, envir, enclos): object 'ps' not found w$transform <- "R" -#> Error in envRefSetField(x, what, refObjectClass(x), selfEnv, value): ‘transform’ is not a field in class “Weight” esda <- import("esda") -#> Error in py_module_import(module, convert = convert): ModuleNotFoundError: No module named 'esda' -#> Run `reticulate::py_last_error()` for details. lM <- esda$Moran_Local(x, w) -#> Error in eval(expr, envir, enclos): object 'esda' not found all.equal(unname(localmoran(x, listw, mlvar=FALSE)[,1]), c(lM$Is)) -#> Error in h(simpleError(msg, call)): error in evaluating the argument 'current' in selecting a method for function 'all.equal': object 'lM' not found # confirm x and w the same lC <- esda$Geary_Local(connectivity=w)$fit(scale(x)) -#> Error in eval(expr, envir, enclos): object 'esda' not found # np$std missing ddof=1 n <- length(x) D0 <- spdep:::geary.intern((x - mean(x)) / sqrt(var(x)*(n-1)/n), listw, n=n) # lC components probably wrongly ordered https://github.com/pysal/esda/issues/192 o <- match(round(D0, 6), round(lC$localG, 6)) -#> Error in eval(expr, envir, enclos): object 'lC' not found all.equal(c(lC$localG)[o], D0) -#> Error in h(simpleError(msg, call)): error in evaluating the argument 'target' in selecting a method for function 'all.equal': object 'lC' not found # simulation order not retained lC$p_sim[o] -#> Error in eval(expr, envir, enclos): object 'lC' not found attr(C, "pseudo-p")[,6] -#> [1] 0.408 0.374 0.144 0.056 0.010 0.284 0.032 0.264 0.186 0.004 0.150 0.224 -#> [13] 0.022 0.150 0.438 0.084 0.126 0.184 0.002 0.238 0.126 0.100 0.068 0.372 -#> [25] 0.124 0.234 0.230 0.016 0.318 0.176 0.272 0.164 0.164 0.206 0.402 0.250 -#> [37] 0.410 0.402 0.328 0.294 0.438 0.248 -# } +}
diff --git a/docs/reference/moran.mc.html b/docs/reference/moran.mc.html index 6d9844d8..17940581 100644 --- a/docs/reference/moran.mc.html +++ b/docs/reference/moran.mc.html @@ -184,6 +184,72 @@

Examples

summary(sim3$res[1:nsim]) #> Min. 1st Qu. Median Mean 3rd Qu. Max. #> -0.192986 -0.055468 -0.024154 -0.026327 0.004769 0.129996 +crime <- COL.OLD$CRIME +is.na(crime) <- sample(1:length(crime), 10) +try(moran.mc(crime, nb2listw(COL.nb, style="W"), nsim=99, + na.action=na.fail)) +#> Error in na.fail.default(x) : missing values in object +moran.mc(crime, nb2listw(COL.nb, style="W"), nsim=99, zero.policy=TRUE, + na.action=na.omit) +#> +#> Monte-Carlo simulation of Moran I +#> +#> data: crime +#> weights: nb2listw(COL.nb, style = "W") +#> omitted: 5, 7, 9, 15, 19, 33, 40, 45, 47, 48 +#> number of simulations + 1: 100 +#> +#> statistic = 0.42342, observed rank = 100, p-value = 0.01 +#> alternative hypothesis: greater +#> +moran.mc(crime, nb2listw(COL.nb, style="W"), nsim=99, zero.policy=TRUE, + return_boot=TRUE, na.action=na.omit) +#> NA observations omitted: 5, 7, 9, 15, 19, 33, 40, 45, 47, 48 +#> +#> DATA PERMUTATION +#> +#> +#> Call: +#> boot(data = x, statistic = moran_boot, R = nsim, sim = "permutation", +#> listw = listw, n = n, S0 = S0, zero.policy = zero.policy, +#> parallel = parallel, ncpus = ncpus, cl = cl) +#> +#> +#> Bootstrap Statistics : +#> original bias std. error +#> t1* 0.4234221 -0.4429891 0.1293935 +moran.mc(crime, nb2listw(COL.nb, style="W"), nsim=99, zero.policy=TRUE, + na.action=na.exclude) +#> +#> Monte-Carlo simulation of Moran I +#> +#> data: crime +#> weights: nb2listw(COL.nb, style = "W") +#> omitted: 5, 7, 9, 15, 19, 33, 40, 45, 47, 48 +#> number of simulations + 1: 100 +#> +#> statistic = 0.42342, observed rank = 100, p-value = 0.01 +#> alternative hypothesis: greater +#> +moran.mc(crime, nb2listw(COL.nb, style="W"), nsim=99, zero.policy=TRUE, + return_boot=TRUE, na.action=na.exclude) +#> NA observations omitted: 5, 7, 9, 15, 19, 33, 40, 45, 47, 48 +#> +#> DATA PERMUTATION +#> +#> +#> Call: +#> boot(data = x, statistic = moran_boot, R = nsim, sim = "permutation", +#> listw = listw, n = n, S0 = S0, zero.policy = zero.policy, +#> parallel = parallel, ncpus = ncpus, cl = cl) +#> +#> +#> Bootstrap Statistics : +#> original bias std. error +#> t1* 0.4234221 -0.4384413 0.1243937 +try(moran.mc(crime, nb2listw(COL.nb, style="W"), nsim=99, na.action=na.pass)) +#> Error in moran.mc(crime, nb2listw(COL.nb, style = "W"), nsim = 99, na.action = na.pass) : +#> na.pass not permitted diff --git a/docs/reference/moran.test.html b/docs/reference/moran.test.html index a00a8b64..56cd011f 100644 --- a/docs/reference/moran.test.html +++ b/docs/reference/moran.test.html @@ -344,33 +344,37 @@

Examples

#> <simpleError in na.fail.default(x): missing values in object> moran.test(crime, nb2listw(COL.nb, style="W"), zero.policy=TRUE, na.action=na.omit) +#> Warning: subsetting caused increase in subgraph count #> #> Moran I test under randomisation #> #> data: crime #> weights: nb2listw(COL.nb, style = "W") -#> omitted: 5, 7, 9, 15, 19, 33, 40, 45, 47, 48 +#> omitted: 7, 10, 12, 13, 14, 18, 26, 41, 42, 47 +#> n reduced by no-neighbour observations #> -#> Moran I statistic standard deviate = 3.8759, p-value = 5.311e-05 +#> Moran I statistic standard deviate = 3.9826, p-value = 3.408e-05 #> alternative hypothesis: greater #> sample estimates: #> Moran I statistic Expectation Variance -#> 0.42342213 -0.02631579 0.01346387 +#> 0.43709247 -0.02702703 0.01358066 #> moran.test(crime, nb2listw(COL.nb, style="W"), zero.policy=TRUE, na.action=na.exclude) +#> Warning: subsetting caused increase in subgraph count #> #> Moran I test under randomisation #> #> data: crime #> weights: nb2listw(COL.nb, style = "W") -#> omitted: 5, 7, 9, 15, 19, 33, 40, 45, 47, 48 +#> omitted: 7, 10, 12, 13, 14, 18, 26, 41, 42, 47 +#> n reduced by no-neighbour observations #> -#> Moran I statistic standard deviate = 3.8759, p-value = 5.311e-05 +#> Moran I statistic standard deviate = 3.9826, p-value = 3.408e-05 #> alternative hypothesis: greater #> sample estimates: #> Moran I statistic Expectation Variance -#> 0.42342213 -0.02631579 0.01346387 +#> 0.43709247 -0.02702703 0.01358066 #> moran.test(crime, nb2listw(COL.nb, style="W"), na.action=na.pass) #> Warning: NAs in lagged values @@ -380,11 +384,11 @@

Examples

#> data: crime #> weights: nb2listw(COL.nb, style = "W") #> -#> Moran I statistic standard deviate = 1.3275, p-value = 0.09216 +#> Moran I statistic standard deviate = 2.4925, p-value = 0.006342 #> alternative hypothesis: greater #> sample estimates: #> Moran I statistic Expectation Variance -#> 0.103248466 -0.020833333 0.008736122 +#> 0.212782897 -0.020833333 0.008784908 #> columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE) col_geoms <- st_geometry(columbus) @@ -421,8 +425,8 @@

Examples

#> Moran I test under randomisation #> #> data: COL.OLD$CRIME -#> weights: lw n reduced by no-neighbour observations -#> +#> weights: lw +#> n reduced by no-neighbour observations #> #> Moran I statistic standard deviate = 2.7707, p-value = 0.002797 #> alternative hypothesis: greater diff --git a/docs/reference/mstree.html b/docs/reference/mstree.html index d863af05..a9322aa1 100644 --- a/docs/reference/mstree.html +++ b/docs/reference/mstree.html @@ -142,7 +142,7 @@

Examples

### find a minimum spanning tree system.time(mst.bh <- mstree(nb.w,5)) #> user system elapsed -#> 0.003 0.000 0.003 +#> 0.002 0.000 0.002 dim(mst.bh) #> [1] 97 3 head(mst.bh) diff --git a/docs/reference/nb2blocknb.html b/docs/reference/nb2blocknb.html index a6330a80..0e108571 100644 --- a/docs/reference/nb2blocknb.html +++ b/docs/reference/nb2blocknb.html @@ -127,96 +127,31 @@

See also

Examples

- 
# \dontrun{
+    
if (FALSE) {
 data(boston, package="spData")
 summary(as.vector(table(boston.c$TOWN)))
-#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>     1.0     2.0     4.0     5.5     7.0    30.0 
 townaggr <- aggregate(boston.utm, list(town=boston.c$TOWN), mean)
 block.rel <- graph2nb(relativeneigh(as.matrix(townaggr[,2:3])),
  as.character(townaggr[,1]), sym=TRUE)
 block.rel
-#> Neighbour list object:
-#> Number of regions: 92 
-#> Number of nonzero links: 240 
-#> Percentage nonzero weights: 2.835539 
-#> Average number of links: 2.608696 
 print(is.symmetric.nb(block.rel))
-#> [1] TRUE
 plot(block.rel, as.matrix(townaggr[,2:3]))
 points(boston.utm, pch=18, col="lightgreen")
-
 block.nb <- nb2blocknb(block.rel, as.character(boston.c$TOWN))
 block.nb
-#> Neighbour list object:
-#> Number of regions: 506 
-#> Number of nonzero links: 15234 
-#> Percentage nonzero weights: 5.949945 
-#> Average number of links: 30.10672 
 print(is.symmetric.nb(block.nb))
-#> [1] TRUE
 plot(block.nb, boston.utm)
 points(boston.utm, pch=18, col="lightgreen")
-
 n.comp.nb(block.nb)$nc
-#> [1] 1
 moran.test(boston.c$CMEDV, nb2listw(boston.soi))
-#> 
-#> 	Moran I test under randomisation
-#> 
-#> data:  boston.c$CMEDV  
-#> weights: nb2listw(boston.soi)    
-#> 
-#> Moran I statistic standard deviate = 21.786, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic       Expectation          Variance 
-#>       0.690285059      -0.001980198       0.001009685 
-#> 
 moran.test(boston.c$CMEDV, nb2listw(block.nb))
-#> 
-#> 	Moran I test under randomisation
-#> 
-#> data:  boston.c$CMEDV  
-#> weights: nb2listw(block.nb)    
-#> 
-#> Moran I statistic standard deviate = 22.455, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic       Expectation          Variance 
-#>      0.3122905961     -0.0019801980      0.0001958827 
-#> 
 block.nb <- nb2blocknb(NULL, as.character(boston.c$TOWN))
 block.nb
-#> Neighbour list object:
-#> Number of regions: 506 
-#> Number of nonzero links: 4868 
-#> Percentage nonzero weights: 1.901295 
-#> Average number of links: 9.620553 
-#> 17 regions with no links:
-#> 1 55 56 57 58 65 196 257 284 285 286 287 342 343 348 349 354
-#> 92 disjoint connected subgraphs
 print(is.symmetric.nb(block.nb))
-#> [1] TRUE
 plot(block.nb, boston.utm)
-
 n.comp.nb(block.nb)$nc
-#> [1] 92
 moran.test(boston.c$CMEDV, nb2listw(block.nb, zero.policy=TRUE), zero.policy=TRUE)
-#> 
-#> 	Moran I test under randomisation
-#> 
-#> data:  boston.c$CMEDV  
-#> weights: nb2listw(block.nb, zero.policy = TRUE)  n reduced by no-neighbour observations
-#>   
-#> 
-#> Moran I statistic standard deviate = 21.145, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic       Expectation          Variance 
-#>      0.6188830566     -0.0020491803      0.0008623116 
-#> 
-# }
+}
diff --git a/docs/reference/nb2lines.html b/docs/reference/nb2lines.html index 2f7d0da7..2ef38698 100644 --- a/docs/reference/nb2lines.html +++ b/docs/reference/nb2lines.html @@ -158,12 +158,12 @@

Examples

tf <- paste0(tempfile(), ".gpkg") st_write(res, dsn=tf, driver="GPKG") #> writing: substituting ENGCRS["Undefined Cartesian SRS with unknown unit"] for missing CRS -#> Writing layer `file4f69d37c7043c' to data source -#> `/tmp/RtmpKdrxsr/file4f69d37c7043c.gpkg' using driver `GPKG' +#> Writing layer `file881e628ba198' to data source +#> `/tmp/RtmpTuIGvZ/file881e628ba198.gpkg' using driver `GPKG' #> Writing 230 features with 5 fields and geometry type Line String. inMap <- st_read(tf) -#> Reading layer `file4f69d37c7043c' from data source -#> `/tmp/RtmpKdrxsr/file4f69d37c7043c.gpkg' using driver `GPKG' +#> Reading layer `file881e628ba198' from data source +#> `/tmp/RtmpTuIGvZ/file881e628ba198.gpkg' using driver `GPKG' #> Simple feature collection with 230 features and 5 fields #> Geometry type: LINESTRING #> Dimension: XY diff --git a/docs/reference/nb2listwdist.html b/docs/reference/nb2listwdist.html index 15877f5c..5f28c581 100644 --- a/docs/reference/nb2listwdist.html +++ b/docs/reference/nb2listwdist.html @@ -162,8 +162,8 @@

Examples

#> Moran I test under randomisation #> #> data: world$lifeExp -#> weights: world_weights n reduced by no-neighbour observations -#> +#> weights: world_weights +#> n reduced by no-neighbour observations #> #> Moran I statistic standard deviate = 2.7769, p-value = 0.002744 #> alternative hypothesis: greater @@ -171,43 +171,15 @@

Examples

#> Moran I statistic Expectation Variance #> 0.473883371 -0.007042254 0.029994057 #> -# \dontrun{ +if (FALSE) { # Moran's I (life expectancy) with IDW with alpha = 2, no coding scheme world_weights <- nb2listwdist(nb_world, pts, type = "idw", alpha = 2, zero.policy = TRUE) moran.test(world$lifeExp, world_weights, zero.policy = TRUE, na.action = na.pass) -#> Warning: NAs in lagged values -#> -#> Moran I test under randomisation -#> -#> data: world$lifeExp -#> weights: world_weights n reduced by no-neighbour observations -#> -#> -#> Moran I statistic standard deviate = 2.7769, p-value = 0.002744 -#> alternative hypothesis: greater -#> sample estimates: -#> Moran I statistic Expectation Variance -#> 0.473883371 -0.007042254 0.029994057 -#> # Moran's I (life expectancy), DPD, alpha = 2, dmax = 1000 km, no coding scheme world_weights <- nb2listwdist(nb_world, pts, type = "dpd", dmax = 1000000, alpha = 2, zero.policy = TRUE) moran.test(world$lifeExp, world_weights, zero.policy = TRUE, na.action = na.pass) -#> Warning: NAs in lagged values -#> -#> Moran I test under randomisation -#> -#> data: world$lifeExp -#> weights: world_weights n reduced by no-neighbour observations -#> -#> -#> Moran I statistic standard deviate = 8.8557, p-value < 2.2e-16 -#> alternative hypothesis: greater -#> sample estimates: -#> Moran I statistic Expectation Variance -#> 0.601498970 -0.007042254 0.004722063 -#> # Boston examples data(boston, package="spData") boston_coords <- data.frame(x = boston.utm[,1], y = boston.utm[,2]) @@ -217,37 +189,11 @@

Examples

boston_weights <- nb2listwdist(nb_boston, boston.geoms, type = "exp", alpha = 2, style="raw", zero.policy = TRUE) moran.test(boston.c$CRIM, boston_weights, zero.policy = TRUE, na.action = na.pass) -#> -#> Moran I test under randomisation -#> -#> data: boston.c$CRIM -#> weights: boston_weights n reduced by no-neighbour observations -#> -#> -#> Moran I statistic standard deviate = 51.34, p-value < 2.2e-16 -#> alternative hypothesis: greater -#> sample estimates: -#> Moran I statistic Expectation Variance -#> 0.8845114518 -0.0019960080 0.0002981586 -#> # Moran's I (crime) with idw weights with alpha = 2, coding scheme = W boston_weights <- nb2listwdist(nb_boston, boston.geoms, type = "idw", alpha = 2, style="W", zero.policy = TRUE) moran.test(boston.c$CRIM, boston_weights, zero.policy = TRUE, na.action = na.pass) -#> -#> Moran I test under randomisation -#> -#> data: boston.c$CRIM -#> weights: boston_weights n reduced by no-neighbour observations -#> -#> -#> Moran I statistic standard deviate = 18.976, p-value < 2.2e-16 -#> alternative hypothesis: greater -#> sample estimates: -#> Moran I statistic Expectation Variance -#> 0.4392852330 -0.0019960080 0.0005408065 -#> -# } +} diff --git a/docs/reference/nblag.html b/docs/reference/nblag.html index 78115ec4..3e6e19d6 100644 --- a/docs/reference/nblag.html +++ b/docs/reference/nblag.html @@ -173,16 +173,18 @@

Examples

if (require(igraph, quietly=TRUE) && require(spatialreg, quietly=TRUE)) run <- TRUE if (run) { W <- as(nb2listw(col.gal.nb), "CsparseMatrix") -G <- graph.adjacency(W, mode="directed", weight="W") +G <- graph.adjacency(W, mode="directed", weight="W") D <- diameter(G) nbs <- nblag(col.gal.nb, maxlag=D) n <- length(col.gal.nb) lmat <- lapply(nbs, nb2mat, style="B", zero.policy=TRUE) mat <- matrix(0, n, n) for (i in seq(along=lmat)) mat = mat + i*lmat[[i]] -G2 <- shortest.paths(G) +G2 <- shortest.paths(G) print(all.equal(G2, mat, check.attributes=FALSE)) } +#> Warning: `shortest.paths()` was deprecated in igraph 2.0.0. +#> ℹ Please use `distances()` instead. #> [1] TRUE diff --git a/docs/reference/poly2nb.html b/docs/reference/poly2nb.html index 475da2d4..efa59d19 100644 --- a/docs/reference/poly2nb.html +++ b/docs/reference/poly2nb.html @@ -174,7 +174,7 @@

Examples

nc.sids <- st_read(system.file("shapes/sids.shp", package="spData")[1], quiet=TRUE) system.time(xxnb <- poly2nb(nc.sids)) #> user system elapsed -#> 0.018 0.000 0.018 +#> 0.017 0.000 0.018 system.time(xxnb <- poly2nb(as(nc.sids, "Spatial"))) #> user system elapsed #> 0.03 0.00 0.03 diff --git a/docs/reference/skater.html b/docs/reference/skater.html index 1c977fd3..2b806a99 100644 --- a/docs/reference/skater.html +++ b/docs/reference/skater.html @@ -292,22 +292,18 @@

Examples

table(res5d$groups) #> Error in eval(expr, envir, enclos): object 'res5d' not found -# \dontrun{ +if (FALSE) { data(boston, package="spData") bh.nb <- boston.soi dpad <- data.frame(scale(boston.c[,c(7:10)])) ### calculating costs system.time(lcosts <- nbcosts(bh.nb, dpad)) -#> user system elapsed -#> 0.038 0.000 0.038 ### making listw nb.w <- nb2listw(bh.nb, lcosts, style="B") ### find a minimum spanning tree mst.bh <- mstree(nb.w,5) ### three groups with no restriction system.time(res1 <- skater(mst.bh[,1:2], dpad, 2)) -#> Error in if (w$num_obs < 1) { stop("The weights is not valid.")}: argument is of length zero -#> Timing stopped at: 0 0 0 library(parallel) nc <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L # set nc to 1L here @@ -321,26 +317,20 @@

Examples

} ### calculating costs system.time(plcosts <- nbcosts(bh.nb, dpad)) -#> user system elapsed -#> 0.037 0.000 0.037 all.equal(lcosts, plcosts, check.attributes=FALSE) -#> [1] TRUE ### making listw pnb.w <- nb2listw(bh.nb, plcosts, style="B") ### find a minimum spanning tree pmst.bh <- mstree(pnb.w,5) ### three groups with no restriction system.time(pres1 <- skater(pmst.bh[,1:2], dpad, 2)) -#> Error in if (w$num_obs < 1) { stop("The weights is not valid.")}: argument is of length zero -#> Timing stopped at: 0 0 0 if(!get.mcOption()) { set.ClusterOption(NULL) stopCluster(cl) } all.equal(res1, pres1, check.attributes=FALSE) -#> Error in h(simpleError(msg, call)): error in evaluating the argument 'target' in selecting a method for function 'all.equal': object 'res1' not found invisible(set.coresOption(coresOpt)) -# } +} diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 01b1c18d..8547d8c7 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -48,6 +48,9 @@ /reference/LOSH.mc.html + + /reference/SD.RStests.html + /reference/SDM.RStests.html @@ -153,6 +156,9 @@ /reference/lm.LMtests.html + + /reference/lm.RStests.html + /reference/lm.morantest.exact.html