Using the testGrid example provided by https://stat.ethz.ch/pipermail/r-sig-geo/2014-December/022106.html
lng <- rep(seq(0, 7, by=1), 8) counter = 1 subCounter = 0 startNum = 0 lat = NULL while (counter < 65) { if (subCounter == 8) { startNum = startNum + 1 subCounter = 0 } lat = c(lat, startNum) subCounter = subCounter + 1 counter = counter + 1 } # now add the black/ white chessboard pattern chess <- rep(c(0,1,0,1,0,1,0,1,1,0,1,0,1,0,1,0), 4) gridNum <- seq(1:64) testGrid <- data.frame(gridNum, lat, lng, chess) I define require(raster) rtestGrid <- rasterFromXYZ(testGrid[,2:4]) plot(rtestGrid) and run Moran(rtestGrid) which results on: [1] 0.07438017 while require(spdep) nb8q <- cell2nb(8, 8, type="queen", torus=FALSE) moran.test(testGrid$chess, nb2listw(nb8q, style="B"),alternative="two.sided") which results on: Moran's I test under randomisation data: testGrid$chess weights: nb2listw(nb8q, style = "B") Moran I statistic standard deviate = -0.7714, p-value = 0.4404 alternative hypothesis: two.sided sample estimates: Moran I statistic Expectation Variance -0.066666667 -0.015873016 0.004335237 Considering the grid is regular, a negative I value makes sense. Why is Moran() so different? Thanks _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo