> sink(zz, type = "message")

> ###################################################
> ### code chunk number 14: BMP_submission_0.Rnw:265-267
> ##################################### .... [TRUNCATED] 
Linking to GEOS 3.9.1, GDAL 3.2.2, PROJ 8.0.0

> used.cars <- st_read("Data and Shapes/hanna66.gpkg", quiet=TRUE)

> ###################################################
> ### code chunk number 15: BMP_submission_0.Rnw:271-275
> ##################################### .... [TRUNCATED] 
Loading required package: sp
Loading required package: spData

Attaching package: 'spData'

The following object is masked _by_ '.GlobalEnv':

    used.cars


> nb <- poly2nb(used.cars)

> centroids <- st_centroid(st_geometry(used.cars))

> nbl <- nb2lines(nb, coords=centroids, as_sf=TRUE)

> ###################################################
> ### code chunk number 16: BMP_submission_0.Rnw:279-290
> ##################################### .... [TRUNCATED] 

> nbl_laea <- st_transform(nbl, "EPSG:2163")

> library(tmap)

> a <- tm_shape(used.cars_laea) + tm_fill("av55_59", title=c("1960 USD"), n=8, palette="Reds") + 
+   tm_borders(lwd=0.5) + tm_legend(position=c("RIGH ..." ... [TRUNCATED] 

> b <- tm_shape(used.cars_laea) + tm_fill(c("transp", "salesTax"), title=c("1960 USD"), n=8, palette="YlOrBr") + 
+   tm_legend(position=c("right", "b ..." ... [TRUNCATED] 

> tmap_arrange(a, b, nrow=2)

> ###################################################
> ### code chunk number 18: BMP_submission_0.Rnw:314-316
> ##################################### .... [TRUNCATED] 
Neighbour list object:
Number of regions: 49 
Number of nonzero links: 218 
Percentage nonzero weights: 9.07955 
Average number of links: 4.44898 

> lw <- nb2listw(nb, style="W")

> ###################################################
> ### code chunk number 19: BMP_submission_0.Rnw:329-333
> ##################################### .... [TRUNCATED] 

> transp_APLE <- aple(c(scale(used.cars$transp, scale=FALSE)), lw)

> salesTax_APLE <- aple(c(scale(used.cars$salesTax, scale=FALSE)), lw)

> both_APLE <- aple(c(scale(with(used.cars, transp + salesTax), scale=FALSE)), lw)

> ###################################################
> ### code chunk number 20: data cross section dui
> ########################################### .... [TRUNCATED] 

> ###################################################
> ### code chunk number 21: BMP_submission_0.Rnw:382-383
> ##################################### .... [TRUNCATED] 

> ###################################################
> ### code chunk number 22: BMP_submission_0.Rnw:385-386 (eval = FALSE)
> ###################### .... [TRUNCATED] 

> fm2 <- dui ~ nondui + vehicles + dry

> endog2 <- ~ police

> instruments2 <- ~ elect 

> ###################################################
> ### code chunk number 24: ricedata
> ###################################################
> dat .... [TRUNCATED] 

> data(riceww, package="splm")

> ricelw <- mat2listw(riceww)

> ###################################################
> ### code chunk number 25: ricefm
> ###################################################
> ricef .... [TRUNCATED] 

> ###################################################
> ### code chunk number 26: splm data 4
> ###################################################
>  .... [TRUNCATED] 

> data_cor <- read.csv("./Data and Shapes/cornwell.csv")

> shape4 <- st_read("Data and Shapes/north_carol.gpkg", quiet=TRUE)

> coord_NC <- st_centroid(st_geometry(shape4), of_largest_polygon=TRUE)

> nbnc_NC <- knn2nb(knearneigh(coord_NC, k = 5))

> nc_listw <- nb2listw(nbnc_NC, style="W")

> fm4 <- lcrmrte ~ lprbconv + lprbpris + lavgsen + 
+   ldensity + lwcon + lwtuc + 
+   lwtrd + lwfir + lwser + lwmfg + 
+   lwfed+ lwsta + lwloc + lp .... [TRUNCATED] 

> endog4 = ~ lprbarr + lpolpc   

> instruments4 = ~ ltaxpc + lmix

> ###################################################
> ### code chunk number 27: BMP_submission_0.Rnw:656-661
> ##################################### .... [TRUNCATED] 

> suppressPackageStartupMessages(library(xtable))

> print(xtable(mat, align=c("l","r","r","r","r"), digits=c(NA, 4, 4, 4, 4), 
+ display=c("s", "f", "f", "f", "f")), floating=FALSE, comment=FALSE, 
+  .... [TRUNCATED] 
\begin{tabular}{lrrrr}
  \hline
 & $\rho_x$ 0 & $\rho_x$ 0.2 & $\rho_x$ 0.5 & $\rho_x$ 0.8 \\ 
  \hline
$\rho_y$ 0 & 0.0505 & 0.0504 & 0.0499 & 0.0502 \\ 
  $\rho_y$ 0.2 & 0.0497 & 0.0561 & 0.0647 & 0.0802 \\ 
  $\rho_y$ 0.5 & 0.0496 & 0.0647 & 0.1002 & 0.1625 \\ 
  $\rho_y$ 0.8 & 0.0532 & 0.0848 & 0.1650 & 0.3134 \\ 
   \hline
\end{tabular}

> ###################################################
> ### code chunk number 28: BMP_submission_0.Rnw:681-684
> ##################################### .... [TRUNCATED] 

> lm_obj2 <- lm(av55_59 ~ transp + salesTax, used.cars)

> lm_obj3 <- lm(av55_59 ~ offset(transp) + salesTax, used.cars)

> ###################################################
> ### code chunk number 29: BMP_submission_0.Rnw:702-715
> ##################################### .... [TRUNCATED] 

> cmat[1:4, 1] <- formatC(c(t(summary(lm_obj1)$coefficients[,1:2])), format="f", digits=3)

> cmat[c(1:2, 5:8), 2] <- formatC(c(t(summary(lm_obj2)$coefficients[,1:2])), format="f", digits=3)

> cmat[c(1:2, 7:8), 3] <- formatC(c(t(summary(lm_obj3)$coefficients[,1:2])), format="f", digits=3)

> cmat[9,1] <- formatC(summary(lm_obj1)$sigma^2, format="f", digits=3)

> cmat[9,2] <- formatC(summary(lm_obj2)$sigma^2, format="f", digits=3)

> cmat[9,3] <- formatC(summary(lm_obj3)$sigma^2, format="f", digits=3)

> even <- (1:nrow(cmat) %% 2) == 0

> cmat[even,] <- t(apply(cmat[even,], 1, function(x) { sapply(x, function(y) {ifelse(nzchar(y), paste("(", y, ")", sep=""), y)})}))

> rownames(cmat) <- c("(Intercept)", " ", "I(transp + salesTax)", "  ", "transp", "   ", "salesTax", "    ", "$\\sigma^2$")

> colnames(cmat) <- c("(1)", "(2)", "(3)")

> print(xtable(cmat, align=c("l","r","r","r")), floating=FALSE, comment=FALSE, 
+ sanitize.text.function=function(x){x}, hline.after=c(-1,0,9))
\begin{tabular}{lrrr}
  \hline
 & (1) & (2) & (3) \\ 
  \hline
(Intercept) & 1435.971 & 1404.473 & 1436.256 \\ 
    & (27.178) & (23.200) & (11.515) \\ 
  I(transp + salesTax) & 0.686 &  &  \\ 
     & (0.173) &  &  \\ 
  transp &  & 1.297 &  \\ 
      &  & (0.189) &  \\ 
  salesTax &  & -0.073 & -0.080 \\ 
       &  & (0.211) & (0.214) \\ 
  $\sigma^2$ & 3181.985 & 2139.748 & 2206.494 \\ 
   \hline
\end{tabular}

> ###################################################
> ### code chunk number 30: BMP_submission_0.Rnw:733-736
> ##################################### .... [TRUNCATED] 

> moran2 <- lm.morantest(lm_obj2, lw, alternative="two.sided")

> moran3 <- lm.morantest(lm_obj3, lw, alternative="two.sided")

> ###################################################
> ### code chunk number 31: BMP_submission_0.Rnw:752-759
> ##################################### .... [TRUNCATED] 

> cmat <- formatC(mat[1:4,], format="f", digits=4)

> cmat <- rbind(cmat, format.pval(mat[5,]))

> rownames(cmat)[5] <- "Pr(z != 0)"

> colnames(cmat) <- paste("(", 1:3, ")", sep="")

> print(xtable(cmat, align=c("l","r","r","r")), floating=FALSE, comment=FALSE, 
+ sanitize.text.function=function(x){x}, hline.after=c(-1,0,5))
\begin{tabular}{lrrr}
  \hline
 & (1) & (2) & (3) \\ 
  \hline
Observed Moran I & 0.5738 & 0.5385 & 0.5917 \\ 
  Expectation & -0.0297 & -0.0361 & -0.0175 \\ 
  Variance & 0.0089 & 0.0090 & 0.0094 \\ 
  Standard deviate & 6.4071 & 6.0731 & 6.2897 \\ 
  Pr(z != 0) & 1.4830e-10 & 1.2543e-09 & 3.1804e-10 \\ 
   \hline
\end{tabular}

> ###################################################
> ### code chunk number 32: BMP_submission_0.Rnw:775-778
> ##################################### .... [TRUNCATED] 

> LM2 <- lm.LMtests(lm_obj2, lw, test="all")

> LM3 <- lm.LMtests(lm_obj3, lw, test="all")

> ###################################################
> ### code chunk number 33: BMP_submission_0.Rnw:787-792
> ##################################### .... [TRUNCATED] 

> cmat <- t(apply(mat, 1, format.pval))

> colnames(cmat) <- paste("(", 1:3, ")", sep="")

> print(xtable(cmat, align=c("l","r","r","r")), floating=FALSE, comment=FALSE, 
+ sanitize.text.function=function(x){x}, hline.after=c(-1,0,5))
\begin{tabular}{lrrr}
  \hline
 & (1) & (2) & (3) \\ 
  \hline
LMerr & 1.4160e-08 & 1.0208e-07 & 4.9702e-09 \\ 
  LMlag & 1.5194e-10 & 6.4062e-09 & 8.8371e-09 \\ 
  RLMerr & 0.839688 & 0.615013 & 0.015588 \\ 
  RLMlag & 0.0028841 & 0.0176952 & 0.0296537 \\ 
  SARMA & 1.2226e-09 & 4.2232e-08 & 3.5187e-09 \\ 
   \hline
\end{tabular}

> ###################################################
> ### code chunk number 34: BMP_submission_0.Rnw:902-904
> ##################################### .... [TRUNCATED] 
Loading required package: Matrix

Attaching package: 'spatialreg'

The following objects are masked from 'package:spdep':

    as_dgRMatrix_listw, as_dsCMatrix_I, as_dsCMatrix_IrW,
    as_dsTMatrix_listw, as.spam.listw, can.be.simmed, cheb_setup,
    create_WX, do_ldet, eigen_pre_setup, eigen_setup, eigenw,
    errorsarlm, get.ClusterOption, get.coresOption, get.mcOption,
    get.VerboseOption, get.ZeroPolicyOption, GMargminImage, GMerrorsar,
    griffith_sone, gstsls, Hausman.test, impacts, intImpacts,
    Jacobian_W, jacobianSetup, l_max, lagmess, lagsarlm, lextrB,
    lextrS, lextrW, lmSLX, LU_prepermutate_setup, LU_setup,
    Matrix_J_setup, Matrix_setup, mcdet_setup, MCMCsamp, ME, mom_calc,
    mom_calc_int2, moments_setup, powerWeights, sacsarlm,
    SE_classic_setup, SE_interp_setup, SE_whichMin_setup,
    set.ClusterOption, set.coresOption, set.mcOption,
    set.VerboseOption, set.ZeroPolicyOption, similar.listw, spam_setup,
    spam_update_setup, SpatialFiltering, spautolm, spBreg_err,
    spBreg_lag, spBreg_sac, stsls, subgraph_eigenw, trW


> sem_obj2 <- spatialreg::errorsarlm(av55_59 ~ transp + salesTax, used.cars, listw=lw)

> ###################################################
> ### code chunk number 35: BMP_submission_0.Rnw:917-919
> ##################################### .... [TRUNCATED] 

> sdm_obj2 <- spatialreg::lagsarlm(av55_59 ~ transp + salesTax, used.cars, listw=lw, Durbin=TRUE)

> ###################################################
> ### code chunk number 36: BMP_submission_0.Rnw:929-950
> ##################################### .... [TRUNCATED] 

> cmat[1:6, 1] <- formatC(c(t(summary(lm_obj2)$coefficients[,1:2])), format="f", digits=3)

> cmat[1:6, 2] <- formatC(c(t(summary(sem_obj2)$Coef[,1:2])), format="f", digits=3)

> cmat[1:6, 3] <- formatC(c(t(summary(slm_obj2)$Coef[,1:2])), format="f", digits=3)

> cmat[1:10, 4] <- formatC(c(t(summary(sdm_obj2)$Coef[,1:2])), format="f", digits=3)

> cmat[15, 1] <- formatC(summary(lm_obj2)$sigma^2, format="f", digits=3)

> cmat[15, 2] <- formatC(summary(sem_obj2)$s2, format="f", digits=3)

> cmat[15, 3] <- formatC(summary(slm_obj2)$s2, format="f", digits=3)

> cmat[15, 4] <- formatC(summary(sdm_obj2)$s2, format="f", digits=3)

> cmat[11, 2] <- formatC(summary(sem_obj2)$lambda, format="f", digits=3)

> cmat[12, 2] <- formatC(summary(sem_obj2)$lambda.se, format="f", digits=3)

> cmat[13, 3] <- formatC(summary(slm_obj2)$rho, format="f", digits=3)

> cmat[14, 3] <- formatC(summary(slm_obj2)$rho.se, format="f", digits=3)

> cmat[13, 4] <- formatC(summary(sdm_obj2)$rho, format="f", digits=3)

> cmat[14, 4] <- formatC(summary(sdm_obj2)$rho.se, format="f", digits=3)

> even <- (1:nrow(cmat) %% 2) == 0

> cmat[even,] <- t(apply(cmat[even,], 1, function(x) { sapply(x, function(y) {ifelse(nzchar(y), paste("(", y, ")", sep=""), y)})}))

> rownames(cmat) <- c("(Intercept)", " ", "transp", "  ", "salesTax", "   ", "lag(transp)", "    ", "lag(salesTax)", "     ", "$\\rhoerr$", "      ",  .... [TRUNCATED] 

> colnames(cmat) <- c("OLS", "SEM", "SLM", "SDM")

> print(xtable(cmat, align=c("l","r","r","r","r")), floating=FALSE, comment=FALSE, 
+ sanitize.text.function=function(x){x}, hline.after=c(-1,0,15))
\begin{tabular}{lrrrr}
  \hline
 & OLS & SEM & SLM & SDM \\ 
  \hline
(Intercept) & 1404.473 & 1445.411 & 441.828 & 516.990 \\ 
    & (23.200) & (36.934) & (150.080) & (166.969) \\ 
  transp & 1.297 & 0.873 & 0.466 & 0.230 \\ 
     & (0.189) & (0.299) & (0.168) & (0.454) \\ 
  salesTax & -0.073 & 0.043 & -0.053 & -0.161 \\ 
      & (0.211) & (0.122) & (0.143) & (0.156) \\ 
  lag(transp) &  &  &  & 0.317 \\ 
       &  &  &  & (0.536) \\ 
  lag(salesTax) &  &  &  & -0.474 \\ 
        &  &  &  & (0.301) \\ 
  $\rhoerr$ &  & 0.721 &  &  \\ 
         &  & (0.100) &  &  \\ 
  $\rholag$ &  &  & 0.683 & 0.645 \\ 
          &  &  & (0.105) & (0.115) \\ 
  $\sigma^2$ & 2139.748 & 999.691 & 974.969 & 942.052 \\ 
   \hline
\end{tabular}

> ###################################################
> ### code chunk number 37: lag model spatialreg
> ############################################# .... [TRUNCATED] 

> err_gmm_sreg <- spatialreg::GMerrorsar(dui ~ nondui + vehicles 
+                                        + dry + police, data = shape2, 
+           .... [TRUNCATED] 

> sarar_gmm_sreg <- spatialreg::gstsls(dui ~ nondui + vehicles 
+                                      + dry + police, 
+                              .... [TRUNCATED] 

> ###################################################
> ### code chunk number 38: BMP_submission_0.Rnw:1038-1052
> ################################### .... [TRUNCATED] 

> cmat[1:10, 2] <- formatC(c(t(summary(lag_gmm_sreg)$Coef[-1,1:2])), format="f", digits=3)

> cmat[13:14, 2] <- formatC(c(t(summary(lag_gmm_sreg)$Coef[1,1:2])), format="f", digits=3)

> cmat[1:10, 1] <- formatC(c(t(summary(err_gmm_sreg)$Coef[,1:2])), format="f", digits=3)

> cmat[11:12, 1] <- formatC(c(t(c(summary(err_gmm_sreg)$lambda, as.numeric(summary(err_gmm_sreg)$lambda.se)))), format="f", digits=3)

> cmat[1:10, 3] <- formatC(c(t(summary(sarar_gmm_sreg)$Coef[-1,1:2])), format="f", digits=3)

> cmat[13:14, 3] <- formatC(c(t(summary(sarar_gmm_sreg)$Coef[1,1:2])), format="f", digits=3)

> cmat[11, 3] <- formatC(c(summary(sarar_gmm_sreg)$lambda), format="f", digits=3)

> even <- (1:nrow(cmat) %% 2) == 0

> cmat[even,] <- t(apply(cmat[even,], 1, function(x) { sapply(x, function(y) {ifelse(nzchar(y), paste("(", y, ")", sep=""), y)})}))

> rownames(cmat) <- c("(Intercept)", " ", "nondui", "  ", "vehicles", "   ", "dry", "    ", "police", "     ", "$\\rhoerr$", "      ", "$\\rholag$", " ..." ... [TRUNCATED] 

> colnames(cmat) <- c("SEM", "SLM", "SARAR")

> print(xtable(cmat, align=c("l","r","r","r")), floating=FALSE, comment=FALSE, 
+ sanitize.text.function=function(x){x}, hline.after=c(-1,0,14))
\begin{tabular}{lrrr}
  \hline
 & SEM & SLM & SARAR \\ 
  \hline
(Intercept) & -5.432 & -6.410 & -6.410 \\ 
    & (0.229) & (0.418) & (0.418) \\ 
  nondui & 0.000 & 0.000 & 0.000 \\ 
     & (0.001) & (0.001) & (0.001) \\ 
  vehicles & 0.016 & 0.016 & 0.016 \\ 
      & (0.001) & (0.001) & (0.001) \\ 
  dry & 0.104 & 0.106 & 0.106 \\ 
       & (0.035) & (0.035) & (0.035) \\ 
  police & 0.600 & 0.598 & 0.598 \\ 
        & (0.015) & (0.015) & (0.015) \\ 
  $\rhoerr$ & 0.051 &  & 0.001 \\ 
         & (0.080) &  &  \\ 
  $\rholag$ &  & 0.047 & 0.047 \\ 
          &  & (0.017) & (0.017) \\ 
   \hline
\end{tabular}

> ###################################################
> ### code chunk number 39: sarar model sphet
> ################################################ .... [TRUNCATED] 

> ###################################################
> ### code chunk number 40: BMP_submission_0.Rnw:1097-1105
> ################################### .... [TRUNCATED] 

> cmat[1:14, 1] <- formatC(c(t(summary(het_old)$Coef[,1:2])), format="f", digits=3)

> even <- (1:nrow(cmat) %% 2) == 0

> cmat[even,] <-  apply(as.matrix(cmat[even,]),1, function(y) {ifelse(nzchar(y), paste("(", y, ")", sep=""), y)})

> rownames(cmat) <- c("(Intercept)", " ", "nondui", "  ", "vehicles", "   ", "dry", "    ", "police", "     ", "$\\rholag$", "      ", "$\\rhoerr$", " ..." ... [TRUNCATED] 

> colnames(cmat) <- c("SARAR Het")

> print(xtable(cmat, align=c("l","r")), floating=FALSE, comment=FALSE, 
+ sanitize.text.function=function(x){x}, hline.after=c(-1,0,14))
\begin{tabular}{lr}
  \hline
 & SARAR Het \\ 
  \hline
(Intercept) & -6.410 \\ 
    & (0.446) \\ 
  nondui & 0.000 \\ 
     & (0.001) \\ 
  vehicles & 0.016 \\ 
      & (0.001) \\ 
  dry & 0.106 \\ 
       & (0.034) \\ 
  police & 0.598 \\ 
        & (0.018) \\ 
  $\rholag$ & 0.047 \\ 
         & (0.018) \\ 
  $\rhoerr$ & -0.000 \\ 
          & (0.037) \\ 
   \hline
\end{tabular}

> ###################################################
> ### code chunk number 41: hac stslshac
> ###################################################
> .... [TRUNCATED] 

> knn_dui <- spdep::knearneigh(crd_dui, k = 10)

> nb_dui <- spdep::knn2nb(knn_dui)

> dst_dui <- spdep::nbdists(nb_dui, crd_dui)

> k10lw <- spdep::nb2listw(nb_dui, glist = dst_dui, style="B")

> class(k10lw) <- "distance"

> hac_old <- sphet::stslshac(dui ~ nondui + vehicles + dry 
+                             + police,  data = shape2, 
+                             lis .... [TRUNCATED] 

> ###################################################
> ### code chunk number 42: BMP_submission_0.Rnw:1156-1167
> ################################### .... [TRUNCATED] 

> seq1 <- seq(1,18,3)

> seq2 <- seq(2,18,3)

> seq3 <- seq(3,18,3)

> cmat[seq1, 1] <- formatC(c(t(summary(lag_gmm_sreg)$Coef[-1,1]), t(summary(lag_gmm_sreg)$Coef[1,1])), format="f", digits=3)

> cmat[seq2, 1] <- formatC(c(t(summary(lag_gmm_sreg)$Coef[-1,2]), t(summary(lag_gmm_sreg)$Coef[1,2])), format="f", digits=3)

> cmat[seq3, 1] <- formatC(c(t(summary(hac_old)$Coef[-1,2]), t(summary(hac_old)$Coef[1,2])), format="f", digits=3)

> cmat[sort(c(seq2, seq3)),] <-  apply(as.matrix(cmat[sort(c(seq2,seq3)),]),1, function(y) {ifelse(nzchar(y), paste("(", y, ")", sep=""), y)})

> row.names(cmat) <- c("(Intercept)", "", " ", "nondui", "  ", "   ", "vehicles", "    ", "     ", "dry", "       ","        ", "police", "         ", .... [TRUNCATED] 

> colnames(cmat) <- c("LAG  HAC")

> print(xtable(cmat, align=c("l","r")), floating=FALSE, comment=FALSE,sanitize.text.function=function(x){x}, hline.after=c(-1,0,18))
\begin{tabular}{lr}
  \hline
 & LAG  HAC \\ 
  \hline
(Intercept) & -6.410 \\ 
   & (0.418) \\ 
    & (0.466) \\ 
  nondui & 0.000 \\ 
     & (0.001) \\ 
      & (0.001) \\ 
  vehicles & 0.016 \\ 
       & (0.001) \\ 
        & (0.001) \\ 
  dry & 0.106 \\ 
          & (0.035) \\ 
           & (0.034) \\ 
  police & 0.598 \\ 
            & (0.015) \\ 
             & (0.020) \\ 
  $\rholag$ & 0.047 \\ 
               & (0.017) \\ 
                 & (0.019) \\ 
   \hline
\end{tabular}

> ###################################################
> ### code chunk number 43: BMP_submission_0.Rnw:1210-1268 (eval = FALSE)
> #################### .... [TRUNCATED] 

> print(xtable(cmat, align=c("l","r","r","r","r","r")), floating=FALSE, comment=FALSE, 
+ sanitize.text.function=function(x){x}, hline.after=c(-1,0,8, .... [TRUNCATED] 
\begin{tabular}{lrrrrr}
  \hline
 & GMM & eigen & Cholesky & MCMC & INLA \\ 
  \hline
$\rhoerr$ & 0.0509 & 0.0459 & 0.0459 & 0.0464 & 0.0461 \\ 
  (Intercept) & -5.4319 & -5.4329 & -5.4329 & -5.4344 & -5.4331 \\ 
  nondui & 0.0003 & 0.0003 & 0.0003 & 0.0003 & 0.0003 \\ 
  vehicles & 0.0156 & 0.0156 & 0.0156 & 0.0156 & 0.0156 \\ 
  dry & 0.1037 & 0.1039 & 0.1039 & 0.1049 & 0.1039 \\ 
  police & 0.5999 & 0.5998 & 0.5998 & 0.5995 & 0.5998 \\ 
  $\rhoerr$ s.e. & 0.0805 & 0.0299 &   NaN & 0.0302 & 0.0299 \\ 
  LR test &  & 0.1287 & 0.1287 &  &  \\ 
   \hline
Set up &  & 11.8420s & 0.0390s & 4.3530s & 0.6579s \\ 
  Fitting &  & 0.0170s & 0.0820s &  & 25.1193s \\ 
  Sampling &  &  &  & 2.9090s &  \\ 
  Completion &  & 92.8660s & 0.1140s & 0.0000s & 0.6290s \\ 
   \hline
\end{tabular}

> ###################################################
> ### code chunk number 47: BMP_submission_0.Rnw:1433-1439
> ################################### .... [TRUNCATED] 

> transp1 <- used.cars

> transp1$transp <- transp1$transp + 1

> p1 <- predict(lm_obj2, newdata=transp1)

> mean(p1-p0)
[1] 1.297165

> coefficients(lm_obj2)["transp"]
  transp 
1.297165 

> ###################################################
> ### code chunk number 48: BMP_submission_0.Rnw:1446-1450
> ################################### .... [TRUNCATED] 

> p1_slm <- predict(slm_obj2, newdata=transp1, listw=lw)

> mean(p1_slm-p0_slm)
[1] 1.468775

> coefficients(slm_obj2)["transp"]
   transp 
0.4660211 

> ###################################################
> ### code chunk number 49: BMP_submission_0.Rnw:1464-1468
> ################################### .... [TRUNCATED] 

> n <- nrow(used.cars)

> S_transp <- invIrW %*% (diag(n) * coef(slm_obj2)["transp"])

> sum(c(S_transp))/n
[1] 1.468775

> ###################################################
> ### code chunk number 50: BMP_submission_0.Rnw:1477-1479
> ################################### .... [TRUNCATED] 
[1] 0.5527551

> impacts(slm_obj2, listw=lw)
Impact measures (lag, exact):
              Direct   Indirect      Total
transp    0.55275511  0.9160198  1.4687749
salesTax -0.06233369 -0.1032987 -0.1656324

> ###################################################
> ### code chunk number 51: BMP_submission_0.Rnw:1486-1487
> ################################### .... [TRUNCATED] 
Impact measures (lag, evalues):
              Direct   Indirect      Total
transp    0.55275511  0.9160198  1.4687749
salesTax -0.06233369 -0.1032987 -0.1656324

> ###################################################
> ### code chunk number 52: BMP_submission_0.Rnw:1496-1498
> ################################### .... [TRUNCATED] 
Impact measures (lag, trace):
              Direct   Indirect      Total
transp    0.55275463  0.9160046  1.4687592
salesTax -0.06233364 -0.1032970 -0.1656307

> impacts(slm_obj2, tr=trW(as(lw, "CsparseMatrix"), m=100, type="mult"))
Impact measures (lag, trace):
              Direct   Indirect      Total
transp    0.55275511  0.9160198  1.4687749
salesTax -0.06233369 -0.1032987 -0.1656324

> ###################################################
> ### code chunk number 53: lag model sphet
> ################################################## .... [TRUNCATED] 

> err_gmm_sphet_e <- sphet::spreg(formula = fm2, data = shape2, 
+                                  listw = listw_dui, endog = endog2, 
+              .... [TRUNCATED] 

> lag_gmm_sphet <- sphet::spreg(formula = dui ~ nondui + vehicles + dry
+                                 + police, data = shape2, 
+                  .... [TRUNCATED] 

> lag_gmm_sphet_e <- sphet::spreg(formula = fm2, data = shape2, 
+                                  listw = listw_dui, endog = endog2, 
+              .... [TRUNCATED] 

> sarar_gmm_sphet <- sphet::spreg(formula = dui ~ nondui + vehicles + dry
+                                 + police, data = shape2, 
+                .... [TRUNCATED] 

> sarar_gmm_sphet_e <- sphet::spreg(formula = fm2, data = shape2, 
+                                  listw = listw_dui, endog = endog2, 
+            .... [TRUNCATED] 

> ###################################################
> ### code chunk number 54: BMP_submission_0.Rnw:1552-1571
> ################################### .... [TRUNCATED] 

> cmat[1:12, 1] <- formatC(c(t(as.matrix(summary(err_gmm_sphet)$Coef[,1:2]))), format="f", digits=3)

> cmat[1:12, 2] <- formatC(c(t(as.matrix(summary(err_gmm_sphet_e)$Coef[,1:2]))), format="f", digits=3)

> cmat[1:10, 3] <- formatC(c(t(as.matrix(summary(lag_gmm_sphet)$Coef[-6,1:2]))), format="f", digits=3)

> cmat[13:14, 3] <- formatC(c(t(as.matrix(summary(lag_gmm_sphet)$Coef[6,1:2]))), format="f", digits=3)

> cmat[1:10, 4] <- formatC(c(t(as.matrix(summary(lag_gmm_sphet_e)$Coef[-6,1:2]))), format="f", digits=3)

> cmat[13:14, 4] <- formatC(c(t(as.matrix(summary(lag_gmm_sphet_e)$Coef[6,1:2]))), format="f", digits=3)

> cmat[1:10, 5] <- formatC(c(t(as.matrix(summary(sarar_gmm_sphet)$Coef[-c(6,7),1:2]))), format="f", digits=3)

> cmat[13:14, 5] <- formatC(c(t(as.matrix(summary(sarar_gmm_sphet)$Coef[6,1:2]))), format="f", digits=3)

> cmat[11:12, 5] <- formatC(c(t(as.matrix(summary(sarar_gmm_sphet)$Coef[7,1:2]))), format="f", digits=3)

> cmat[1:10, 6] <- formatC(c(t(as.matrix(summary(sarar_gmm_sphet_e)$Coef[-c(6,7),1:2]))), format="f", digits=3)

> cmat[13:14, 6] <- formatC(c(t(as.matrix(summary(sarar_gmm_sphet_e)$Coef[6,1:2]))), format="f", digits=3)

> cmat[11:12, 6] <- formatC(c(t(as.matrix(summary(sarar_gmm_sphet_e)$Coef[7,1:2]))), format="f", digits=)

> even <- (1:nrow(cmat) %% 2) == 0

> cmat[even,] <- t(apply(cmat[even,], 1, function(x) { sapply(x, function(y) {ifelse(nzchar(y), paste("(", y, ")", sep=""), y)})}))

> rownames(cmat) <- c("(Intercept)", " ", "nondui", "  ", "vehicles", "   ", "dry", "    ", "police", "     ", "$\\rhoerr$", "      ", "$\\rholag$", " ..." ... [TRUNCATED] 

> colnames(cmat) <- c("SEM","SEM-end", "SLM", "SLM-end", "SARAR", "SARAR-end")

> print(xtable(cmat, align=c("l","r","r","r","r", "r", "r")), floating=FALSE, comment=FALSE, 
+ sanitize.text.function=function(x){x}, hline.after=c(- .... [TRUNCATED] 
\begin{tabular}{lrrrrrr}
  \hline
 & SEM & SEM-end & SLM & SLM-end & SARAR & SARAR-end \\ 
  \hline
(Intercept) & -5.432 & 15.782 & -6.410 & 11.850 & -6.410 & 11.920 \\ 
    & (0.229) & (1.606) & (0.418) & (1.724) & (0.416) & (1.696) \\ 
  nondui & 0.000 & -0.000 & 0.000 & -0.000 & 0.000 & -0.000 \\ 
     & (0.001) & (0.003) & (0.001) & (0.003) & (0.001) & (0.003) \\ 
  vehicles & 0.016 & 0.094 & 0.016 & 0.094 & 0.016 & 0.094 \\ 
      & (0.001) & (0.006) & (0.001) & (0.006) & (0.001) & (0.006) \\ 
  dry & 0.104 & 0.400 & 0.106 & 0.400 & 0.106 & 0.401 \\ 
       & (0.035) & (0.092) & (0.035) & (0.092) & (0.035) & (0.092) \\ 
  police & 0.600 & -1.365 & 0.598 & -1.366 & 0.598 & -1.367 \\ 
        & (0.015) & (0.144) & (0.015) & (0.143) & (0.015) & (0.142) \\ 
  $\rhoerr$ & 0.047 & -0.005 &  &  & -0.006 & -0.0819 \\ 
         & (0.030) & (0.025) &  &  & (0.035) & (0.0304) \\ 
  $\rholag$ &  &  & 0.047 & 0.188 & 0.047 & 0.186 \\ 
          &  &  & (0.017) & (0.047) & (0.017) & (0.046) \\ 
   \hline
\end{tabular}

> ###################################################
> ### code chunk number 55: sem
> ###################################################
> library( .... [TRUNCATED] 

> sem <- spml(ricefm, data = RiceFarms, listw = ricelw,
+               model="pooling", lag = FALSE, spatial.error = "b")

> ###################################################
> ### code chunk number 56: bsktests
> ###################################################
> LMH .... [TRUNCATED] 

> LM1 <- bsktest(ricefm, data = RiceFarms, listw = ricelw, test="LM1")

> LM2 <- bsktest(ricefm, data = RiceFarms, listw = ricelw, test="LM2")

> CLMm <- bsktest(ricefm, data = RiceFarms, listw = ricelw, test="CLMmu")

> CLMl <- bsktest(ricefm, data = RiceFarms, listw = ricelw, test="CLMlambda")

> LMtab <- cbind(c(LMH$statistic, LMH$p.value),
+                c(LM1$statistic, LM1$p.value),
+                c(LM2$statistic, LM2$p.value),
+      .... [TRUNCATED] 

> dimnames(LMtab) <- list(c("test", "p-value"), c("LM joint", "LM mu", "LM lambda", "CLM mu", "CLM lambda"))

> round(LMtab, 5)
        LM joint   LM mu LM lambda  CLM mu CLM lambda
test      1034.1 4.11991  31.89241 7.99082   35.10134
p-value      0.0 0.00004   0.00000 0.00000    0.00000

> ###################################################
> ### code chunk number 57: localCD
> ###################################################
> libr .... [TRUNCATED] 

> CDp.pool <- pcdtest(plm(ricefm, data = RiceFarms, model='pooling'),
+                w = listw2mat(ricelw), test='cd')

> CDp.FE <- pcdtest(plm(ricefm, data = RiceFarms, model='within'),
+                w = listw2mat(ricelw), test='cd')

> CDp.RE <- pcdtest(plm(ricefm, data = RiceFarms, model='random'),
+                w = listw2mat(ricelw), test='cd')

> CDtab <- cbind(c(CDp.pool$statistic, CDp.pool$p.value),
+                c(CDp.FE$statistic, CDp.FE$p.value),
+                c(CDp.RE$statistic, C .... [TRUNCATED] 

> dimnames(CDtab) <- list(c("test", "p-value"), c("Pooled","Fixed","Random"))

> round(CDtab, 5)
          Pooled    Fixed  Random
test    31.89157 31.47463 31.8113
p-value  0.00000  0.00000  0.0000

> ###################################################
> ### code chunk number 58: sarre
> ###################################################
> sarre  .... [TRUNCATED] 

> ###################################################
> ### code chunk number 59: semfe
> ###################################################
> librar .... [TRUNCATED] 

> semfe <- spml(ricefm, data = RiceFarms, listw = ricelw,
+               lag = FALSE, spatial.error = "b")

> ###################################################
> ### code chunk number 60: saremfe
> ###################################################
> sare .... [TRUNCATED] 

> summary(saremfe)$CoefTable[1:2, ]
        Estimate Std. Error   t-value     Pr(>|t|)
lambda 0.2134885 0.09556428  2.233978 2.548455e-02
rho    0.6901826 0.05309268 12.999581 1.230162e-38

> ###################################################
> ### code chunk number 61: semre
> ###################################################
> librar .... [TRUNCATED] 

> semre <- spml(ricefm, data = RiceFarms, listw = ricelw,
+               model="random", lag = FALSE, spatial.error = "b")

> ###################################################
> ### code chunk number 62: sem2re
> ###################################################
> sem2r .... [TRUNCATED] 

> ###################################################
> ### code chunk number 63: saremre
> ###################################################
> sare .... [TRUNCATED] 

> ###################################################
> ### code chunk number 64: bsjktests
> ###################################################
> J  .... [TRUNCATED] 

> C.1 <- bsjktest(ricefm, data = RiceFarms, listw = ricelw, test="C.1")

> C.2 <- bsjktest(ricefm, data = RiceFarms, listw = ricelw, test="C.2")

> C.3 <- bsjktest(ricefm, data = RiceFarms, listw = ricelw, test="C.3")

> LMtab2 <- cbind(c(J$statistic, J$p.value),
+                c(C.1$statistic, C.1$p.value),
+                c(C.2$statistic, C.2$p.value),
+         .... [TRUNCATED] 

> dimnames(LMtab2) <- list(c("test", "p-value"), c("LM joint", "CLM lambda", "CLM psi", "CLM mu"))

> round(LMtab2, 5)
        LM joint CLM lambda CLM psi   CLM mu
test    1050.143   1047.052 7.00581 78.21034
p-value    0.000      0.000 0.00812  0.00000

> ###################################################
> ### code chunk number 65: saremsrre
> ###################################################
> sa .... [TRUNCATED] 

> ###################################################
> ### code chunk number 66: sarem2srre
> ###################################################
> s .... [TRUNCATED] 

> ###################################################
> ### code chunk number 67: Baltagi2
> ###################################################
> ec2 .... [TRUNCATED] 

> sarar_ec <-spgm(fm4, data = data_cor, lag = TRUE, listw = nc_listw, 
+ endog = endog4, instruments = instruments4, spatial.error = TRUE, 
+ model =  .... [TRUNCATED] 

> ###################################################
> ### code chunk number 68: BMP_submission_0.Rnw:2123-2135
> ################################### .... [TRUNCATED] 

> cmat <- matrix("", ncol=4, nrow=28)

> cmat[1:27, 1] <- formatC(c(t(summary(ec2sls)$Coef[,1])), format="f", digits=3)

> cmat[1:27, 2] <- paste("(", formatC(c(t(summary(ec2sls)$Coef[,2])), format="f", digits=3), ")", sep="")

> cmat[1:27, 3] <- formatC(c(t(summary(sarar_ec)$Coef[-3, 1])), format="f", digits=3)

> cmat[1:27, 4] <- paste("(", formatC(c(t(summary(sarar_ec)$Coef[-3, 2])), format="f", digits=3), ")", sep="")

> cmat[28, 3] <- formatC(c(t(summary(sarar_ec)$Coef[3,1])), format="f", digits=3)

> cmat[28, 4] <- paste("(", formatC(c(t(summary(sarar_ec)$Coef[3,2])), format="f", digits=3), ")", sep="")

> rownames(cmat) <- c(rownames(summary(ec2sls)$Coef),"$\\rholag$")

> colnames(cmat) <- c("EC2SLS", "(std. err.)", "Spatial EC2SLS", "(std. err.)")

> print(xtable(cmat, align=c("l","r","r","r","r")), floating=FALSE, comment=FALSE, 
+ sanitize.text.function=function(x){x}, hline.after=c(-1,0,28))
\begin{tabular}{lrrrr}
  \hline
 & EC2SLS & (std. err.) & Spatial EC2SLS & (std. err.) \\ 
  \hline
lprbarr & -0.413 & (0.097) & -0.340 & (0.059) \\ 
  lpolpc & 0.435 & (0.090) & 0.354 & (0.050) \\ 
  (Intercept) & -0.954 & (1.284) & -0.698 & (1.144) \\ 
  lprbconv & -0.323 & (0.054) & -0.275 & (0.031) \\ 
  lprbpris & -0.186 & (0.042) & -0.164 & (0.033) \\ 
  lavgsen & -0.010 & (0.027) & -0.014 & (0.025) \\ 
  ldensity & 0.429 & (0.055) & 0.446 & (0.049) \\ 
  lwcon & -0.007 & (0.040) & -0.005 & (0.037) \\ 
  lwtuc & 0.045 & (0.020) & 0.039 & (0.017) \\ 
  lwtrd & -0.008 & (0.041) & -0.012 & (0.039) \\ 
  lwfir & -0.004 & (0.029) & -0.006 & (0.027) \\ 
  lwser & 0.006 & (0.020) & 0.004 & (0.019) \\ 
  lwmfg & -0.204 & (0.080) & -0.185 & (0.074) \\ 
  lwfed & -0.164 & (0.159) & -0.067 & (0.141) \\ 
  lwsta & -0.054 & (0.106) & -0.041 & (0.097) \\ 
  lwloc & 0.163 & (0.120) & 0.118 & (0.110) \\ 
  lpctymle & -0.108 & (0.140) & -0.066 & (0.116) \\ 
  lpctmin & 0.189 & (0.041) & 0.185 & (0.036) \\ 
  west & -0.227 & (0.100) & -0.224 & (0.089) \\ 
  central & -0.194 & (0.060) & -0.210 & (0.056) \\ 
  urban & -0.225 & (0.116) & -0.179 & (0.100) \\ 
  d82 & 0.011 & (0.026) & 0.006 & (0.020) \\ 
  d83 & -0.084 & (0.031) & -0.064 & (0.026) \\ 
  d84 & -0.103 & (0.037) & -0.077 & (0.032) \\ 
  d85 & -0.096 & (0.049) & -0.073 & (0.044) \\ 
  d86 & -0.069 & (0.060) & -0.057 & (0.054) \\ 
  d87 & -0.031 & (0.071) & -0.034 & (0.064) \\ 
  $\rholag$ &  &  & 0.268 & (0.069) \\ 
   \hline
\end{tabular}

> ###################################################
> ### code chunk number 69: BMP_submission_0.Rnw:2163-2170
> ################################### .... [TRUNCATED] 

> names(nb) <- as.character(used.cars$State)

> N <- nrow(used.cars)

> library(mgcv)
Loading required package: nlme
This is mgcv 1.8-34. For overview type 'help("mgcv-package")'.

> gam_obj2 <- gam(av55_59 ~ transp + salesTax + s(State, bs="mrf", k=N-2, xt=list(nb=nb)), 
+                 data = used.cars)

> printCoefmat(summary(gam_obj2)$p.table)
               Estimate  Std. Error t value  Pr(>|t|)    
(Intercept) 1424.593015   31.349097 45.4429 < 2.2e-16 ***
transp         1.082638    0.280730  3.8565  0.001058 ** 
salesTax      -0.013184    0.143074 -0.0921  0.927544    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

> ###################################################
> ### code chunk number 70: BMP_submission_0.Rnw:2182-2187
> ################################### .... [TRUNCATED] 
Loading required package: MASS
Loading required package: hglm.data

hglm: Hierarchical Generalized Linear Models
Version 2.2-1 (2019-04-04) installed
Authors: Moudud Alam, Lars Ronnegard, Xia Shen
Maintainer: Xia Shen <xia.shen@ki.se>

Use citation("hglm") to know how to cite our work.

Discussion: https://r-forge.r-project.org/forum/?group_id=558
BugReports: https://r-forge.r-project.org/tracker/?group_id=558
VideoTutorials: http://www.youtube.com/playlist?list=PLn1OmZECD-n15vnYzvJDy5GxjNpVV5Jr8

> hglm_obj2 <- hglm(fixed=av55_59 ~ transp + salesTax, random= ~ 1|State, 
+                   data=used.cars, 
+                   rand.family=SAR(D= .... [TRUNCATED] 

> printCoefmat(summary(hglm_obj2)$FixCoefMat)
               Estimate  Std. Error t-value  Pr(>|t|)    
(Intercept) 1437.977652   94.727074 15.1802 < 2.2e-16 ***
transp         0.969334    0.315983  3.0677  0.004288 ** 
salesTax      -0.038411    0.139840 -0.2747  0.785277    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

> ###################################################
> ### code chunk number 71: BMP_submission_0.Rnw:2193-2199
> ################################### .... [TRUNCATED] 

> sf0$hglm_sar <- unname(summary(hglm_obj2, print.ranef=TRUE)$RandCoefMat)[,1]

> sf0$GAM_mrf_re <- predict(gam_obj2, type="terms", se=TRUE)$fit[,3]

> tm_shape(sf0) + tm_fill(c("hglm_sar", "GAM_mrf_re"), midpoint=c(0), n=8, palette="BrBG", title="1960 USD RE") + tm_facets(free.scales=FALSE) + tm_bo .... [TRUNCATED] 

> ###################################################
> ### code chunk number 72: BMP_submission_0.Rnw:2258-2262
> ################################### .... [TRUNCATED] 
  Step SelEvec      Eval       MinMi     ZMinMi       Pr(ZI)        R2
0    0       0 0.0000000  0.53854945 6.07314120 1.254321e-09 0.5065515
1    1       2 0.9076713  0.27063108 3.55731861 3.746596e-04 0.7140799
2    2       1 0.9680162  0.03553382 1.31173941 1.896081e-01 0.7861660
3    3       4 0.7721324 -0.03794120 0.72371743 4.692392e-01 0.8055611
4    4       6 0.6176878 -0.11016441 0.08390477 9.331321e-01 0.8248549
       gamma
0    0.00000
1 -203.45955
2  119.91270
3  -62.19928
4  -62.03656

> SF_obj2 <- lm(av55_59 ~ transp + salesTax + fitted(SF0), data = used.cars)

> summary(SF_obj2)

Call:
lm(formula = av55_59 ~ transp + salesTax + fitted(SF0), data = used.cars)

Residuals:
   Min     1Q Median     3Q    Max 
-66.49 -22.54   4.02  19.95  52.34 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)     1404.47266   14.46494  97.095  < 2e-16 ***
transp             1.29717    0.11799  10.994 6.14e-14 ***
salesTax          -0.07302    0.13128  -0.556 0.580987    
fitted(SF0)vec2 -203.45955   28.84123  -7.054 1.22e-08 ***
fitted(SF0)vec1  119.91270   28.84123   4.158 0.000155 ***
fitted(SF0)vec4  -62.19928   28.84123  -2.157 0.036808 *  
fitted(SF0)vec6  -62.03656   28.84123  -2.151 0.037279 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 28.84 on 42 degrees of freedom
Multiple R-squared:  0.8249,	Adjusted R-squared:  0.7998 
F-statistic: 32.97 on 6 and 42 DF,  p-value: 2.268e-14


> lm.morantest(SF_obj2, lw)

	Global Moran I for regression residuals

data:  
model: lm(formula = av55_59 ~ transp + salesTax + fitted(SF0), data =
used.cars)
weights: lw

Moran I statistic standard deviate = 0.083905, p-value = 0.4666
alternative hypothesis: greater
sample estimates:
Observed Moran I      Expectation         Variance 
    -0.110164405     -0.117270378      0.007172552 


> ###################################################
> ### code chunk number 73: BMP_submission_0.Rnw:2268-2275
> ################################### .... [TRUNCATED] 

> pvecs <- vecs %*% diag(coefficients(SF_obj2)[4:7])

> colnames(pvecs) <- colnames(vecs)

> sf1 <- cbind(used.cars_laea, pvecs)

> tm_shape(sf1) + tm_fill(c("vec2", "vec1", "vec4", "vec6"), n=8, palette="BrBG", midpoint=0, title="1960 USD") + tm_facets(free.scales=FALSE) + tm_bo .... [TRUNCATED] 

> ###################################################
> ### code chunk number 74: BMP_submission_0.Rnw:2295-2302
> ################################### .... [TRUNCATED] 

> ME0 <- ME(av55_59 ~ transp + salesTax, data = used.cars, listw=lw, alpha=0.15, nsim=99)

> ME_obj2 <- lm(av55_59 ~ transp + salesTax + fitted(ME0), data = used.cars)

> mvecs <- fitted(ME0) %*% diag(coefficients(ME_obj2)[4:6])

> sem_obj2a <- spautolm(av55_59 ~ transp + salesTax, used.cars, listw=lw)

> W <- as(lw, "CsparseMatrix")

> SAR_ssre <- 2*as.vector((sem_obj2a$lambda * W) %*% sem_obj2a$Y - (sem_obj2a$lambda * W) %*% (sem_obj2a$X %*% sem_obj2a$fit$coefficients))

> ###################################################
> ### code chunk number 75: BMP_submission_0.Rnw:2316-2322
> ################################### .... [TRUNCATED] 

> centroids_laea <- st_centroid(st_geometry(used.cars_laea))

> meig <- meigen(st_coordinates(centroids_laea))
 10/49 eigen-pairs are extracted

> y <- model.response(model.frame(av55_59 ~ transp + salesTax, used.cars))

> X <- model.matrix( ~ transp + salesTax, used.cars)

> (esf_obj2 <- esf(y, X, meig=meig))
  6/10 eigenvectors are selected
Call:
esf(y = y, x = X, meig = meig)

----Coefficients------------------------------
                 Estimate         SE     t_value      p_value
(Intercept)  1.498349e+03 18.2316763 82.18384571 3.146807e-46
transp       3.832013e-01  0.1600957  2.39357598 2.146609e-02
salesTax    -4.411189e-03  0.1148358 -0.03841303 9.695495e-01

----Spatial effects (residuals)---------------
                       Estimate
SE                   51.3659411
Moran.I/max(Moran.I)  0.6453173

----Error statistics--------------------------
                 stat
resid_SE   23.4927492
adjR2       0.8671902
logLik   -219.2338371
AIC       458.4676741
BIC       477.3858771

> ###################################################
> ### code chunk number 76: BMP_submission_0.Rnw:2328-2334
> ################################### .... [TRUNCATED] 

> colnames(evecs) <- rownames(esf_obj2$r)[1:4]

> sf2 <- cbind(sf1, evecs)

> tm_shape(sf2) + tm_fill(c("sf2", "sf4", "sf1", "sf6"), n=8, palette="BrBG", midpoint=0, title="1960 USD") + tm_facets(free.scales=FALSE) + tm_border .... [TRUNCATED] 

> ###################################################
> ### code chunk number 77: BMP_submission_0.Rnw:2351-2357
> ################################### .... [TRUNCATED] 

> colnames(mat) <- rownames(mat) <- c("HGLM", "GAM", "SF", "ESF", "ME", "SAR")

> suppressPackageStartupMessages(library(xtable))

> print(xtable(mat, align=c("l","r","r","r","r","r","r"), digits=c(NA, 4, 4, 4, 4, 4, 4), 
+ display=c("s", "f", "f", "f", "f", "f", "f")), floating=F .... [TRUNCATED] 
\begin{tabular}{lrrrrrr}
  \hline
 & HGLM & GAM & SF & ESF & ME & SAR \\ 
  \hline
HGLM & 1.0000 & 0.9593 & 0.9106 & 0.9046 & 0.9588 & 0.9500 \\ 
  GAM & 0.9593 & 1.0000 & 0.8436 & 0.8833 & 0.8856 & 0.8462 \\ 
  SF & 0.9106 & 0.8436 & 1.0000 & 0.7088 & 0.9051 & 0.9235 \\ 
  ESF & 0.9046 & 0.8833 & 0.7088 & 1.0000 & 0.8751 & 0.8336 \\ 
  ME & 0.9588 & 0.8856 & 0.9051 & 0.8751 & 1.0000 & 0.9410 \\ 
  SAR & 0.9500 & 0.8462 & 0.9235 & 0.8336 & 0.9410 & 1.0000 \\ 
   \hline
\end{tabular}

> sessionInfo()
R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Fedora 33 (Workstation Edition)

Matrix products: default
BLAS:   /home/rsb/topics/R/R404-share/lib64/R/lib/libRblas.so
LAPACK: /home/rsb/topics/R/R404-share/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] spmoran_0.2.1    hglm_2.2-1       hglm.data_1.0-1  MASS_7.3-53.1   
 [5] mgcv_1.8-34      nlme_3.1-152     plm_2.4-1        spatialreg_1.1-7
 [9] Matrix_1.3-2     xtable_1.8-4     splm_1.5-2       tmap_3.3-1      
[13] spdep_1.1-7      spData_0.3.8     sp_1.4-6         sf_0.9-9        

loaded via a namespace (and not attached):
 [1] doParallel_1.0.16  gmodels_2.18.1     RColorBrewer_1.1-2 sphet_1.10.01     
 [5] tools_4.0.4        vegan_2.5-7        utf8_1.2.1         R6_2.5.0          
 [9] KernSmooth_2.23-18 colorspace_2.0-0   DBI_1.1.1          permute_0.9-5     
[13] raster_3.4-5       tidyselect_1.1.0   leaflet_2.0.4.1    compiler_4.0.4    
[17] leafem_0.1.3       expm_0.999-6       sandwich_3.0-0     scales_1.1.1      
[21] lmtest_0.9-38      classInt_0.4-3     mvtnorm_1.1-1      proxy_0.4-25      
[25] stringr_1.4.0      digest_0.6.27      dbscan_1.1-6       base64enc_0.1-3   
[29] dichromat_2.0-0    pkgconfig_2.0.3    htmltools_0.5.1.1  maps_3.3.0        
[33] ibdreg_0.3.1       htmlwidgets_1.5.3  rlang_0.4.10       generics_0.1.0    
[37] zoo_1.8-9          crosstalk_1.1.1    gtools_3.8.2       dplyr_1.0.5       
[41] magrittr_2.0.1     Formula_1.2-4      dotCall64_1.0-1    munsell_0.5.0     
[45] Rcpp_1.0.6         fansi_0.4.2        abind_1.4-5        lifecycle_1.0.0   
[49] stringi_1.5.3      leafsync_0.1.0     tmaptools_3.1-1    grid_4.0.4        
[53] parallel_4.0.4     gdata_2.18.0       bdsmatrix_1.3-4    crayon_1.4.1      
[57] deldir_0.2-10      lattice_0.20-41    stars_0.5-2        splines_4.0.4     
[61] pillar_1.5.1       boot_1.3-27        codetools_0.2-18   LearnBayes_2.15.1 
[65] XML_3.99-0.6       glue_1.4.2         foreach_1.5.1      png_0.1-7         
[69] vctrs_0.3.7        spam_2.6-0         Rdpack_2.1.1       miscTools_0.6-26  
[73] gtable_0.3.0       purrr_0.3.4        assertthat_0.2.1   ggplot2_3.3.3     
[77] rbibutils_2.0      lwgeom_0.2-5       e1071_1.7-6        RSpectra_0.16-0   
[81] coda_0.19-4        class_7.3-18       viridisLite_0.3.0  rARPACK_0.11-0    
[85] tibble_3.1.0       iterators_1.0.13   cluster_2.1.1      fields_11.6       
[89] units_0.7-1        maxLik_1.4-8       ellipsis_0.3.1     spDataLarge_0.5.1 

> sf_extSoftVersion()
          GEOS           GDAL         proj.4 GDAL_with_GEOS     USE_PROJ_H 
       "3.9.1"        "3.2.2"        "8.0.0"         "true"         "true" 

> sink(type = "message")

> sink()