r语言|R中处理空间面板模型的包spdep的用法 r语言|空间数据分析

1、载入相应的包文件
建议安装R3.3.3此包适用

install.packages("spdep") library(spdep) install.packages("spDataLarge") library(spDataLarge) 2、读取需要处理的原始文件文件放在什么位置就如何读

mydata=https://www.it610.com/article/read.csv(“D:/regionResearchMethods/Ch4_R/industrialProduction.csv”,header = T)
3、如何使用空间权重矩阵
这是在geoda这个软件中已经做好的

setwd("D:/regionResearchMethods/Ch4_R") gal<-read.gal("Province31.gal") gwt<-read.gwt2nb("Province31.gwt") gal.mat<-nb2mat(gal)

4、计算全域空间自相关

mydata <-read.csv("industrialProduction.csv") lapply(mydata[2:4],moran.test,listw = mat2listw(gal.mat)) $productMoran I test under randomisationdata:X[[i]] weights: mat2listw(gal.mat)Moran I statistic standard deviate = 2.3291, p-value = 0.009928 alternative hypothesis: greater sample estimates: Moran I statisticExpectation 0.22887903-0.03333333 Variance 0.01267477 $assetsMoran I test under randomisationdata:X[[i]] weights: mat2listw(gal.mat)Moran I statistic standard deviate = 2.3024, p-value = 0.01066 alternative hypothesis: greater sample estimates: Moran I statisticExpectation 0.23222902-0.03333333 Variance 0.01330330 $laborsMoran I test under randomisationdata:X[[i]] weights: mat2listw(gal.mat)Moran I statistic standard deviate = 1.4397, p-value = 0.07498 alternative hypothesis: greater sample estimates: Moran I statisticExpectation 0.12428349-0.03333333 Variance 0.01198569 > moran.mc(mydata$product,listw = mat2listw(gal.mat),nsim = 999)Monte-Carlo simulation of Moran Idata:mydata$product weights: mat2listw(gal.mat) number of simulations + 1: 1000 statistic = 0.22888, observed rank = 983, p-value = https://www.it610.com/article/0.017 alternative hypothesis: greater> moran.plot(mydata$product,list=mat2listw(gal.mat),xlab="product",ylab="product.slag") > 计算其空间滞后值

product.lag <- gal.mat %*% mydata$product
product.lag

【r语言|R中处理空间面板模型的包spdep的用法】5、计算局域空间自相关

lisa = localmoran(mydata$product,mat2listw(gal.mat)) > lisa IiE.IiVar.IiZ.IiPr(z > 0) 1-0.895039931 -0.03333333 0.41371853 -1.33969808 9.098282e-01 20.061274825 -0.03333333 0.194711390.21440401 4.151160e-01 3-0.072696996 -0.03333333 0.08520782 -0.13485150 5.536354e-01 40.106720593 -0.03333333 0.413718530.21774230 4.138150e-01 50.099568620 -0.03333333 0.267713770.25685972 3.986435e-01 6-0.313556199 -0.03333333 0.10085119 -0.88239505 8.112184e-01 70.104591260 -0.03333333 0.413718530.21443182 4.151052e-01 80.088169423 -0.03333333 0.267713770.23482849 4.071709e-01 90.410821690 -0.03333333 0.121709011.27313136 1.014857e-01 101.924724136 -0.03333333 0.194711394.43741204 4.552345e-06 11 -0.249562987 -0.03333333 0.19471139 -0.49002651 6.879424e-01 120.060841444 -0.03333333 0.150909960.24242402 4.042258e-01 130.022436213 -0.03333333 0.194711390.12638672 4.497129e-01 140.193035354 -0.03333333 0.267713770.43750296 3.308733e-01 153.663150857 -0.03333333 0.194711398.37708989 2.712620e-17 160.046699584 -0.03333333 0.121709010.22940733 4.092762e-01 17 -0.043668169 -0.03333333 0.10085119 -0.03254341 5.129807e-01 18 -0.089914742 -0.03333333 0.12170901 -0.16218564 5.644202e-01 191.283136603 -0.03333333 0.150909963.38884723 3.509355e-04 200.122279554 -0.03333333 0.851732800.16861409 4.330501e-01 210.209267893 -0.03333333 0.121709010.69539510 2.434039e-01 220.204469655 -0.03333333 0.121709010.68164137 2.477329e-01 23 -0.005677445 -0.03333333 0.121709010.07927317 4.684077e-01 24 -0.691333698 -0.03333333 0.26771377 -1.27171789 8.982633e-01 250.147522747 -0.03333333 0.194711390.40986179 3.409537e-01 260.181482071 -0.03333333 0.150909960.55297623 2.901398e-01 270.021717245 -0.03333333 0.267713770.10639630 4.576340e-01 280.369445450 -0.03333333 0.150909961.03683018 1.499075e-01 290.024374970 -0.03333333 0.085207820.19769632 4.216413e-01 300.100125941 -0.03333333 0.194711390.30244965 3.811547e-01 310.010844072 -0.03333333 0.413718530.06868276 4.726211e-01