方法1、散点图
hscat(log(zinc)~1, meuse, (0:9)*100)
方法2、变异函数云图
library(gstat) cld <- variogram(log(zinc) ~ 1, meuse, cloud = TRUE) svgm <- variogram(log(zinc) ~ 1, meuse) d <- data.frame(gamma = c(cld$gamma, svgm$gamma), dist = c(cld$dist, svgm$dist), id = c(rep("cloud", nrow(cld)), rep("sample variogram", nrow(svgm))) ) xyplot(gamma ~ dist | id, d, scales = list(y = list(relation = "free", #ylim = list(NULL, c(-.005,0.7)))), limits = list(NULL, c(-.005,0.7)))), layout = c(1, 2), as.table = TRUE, panel = function(x,y, ...) { if (panel.number() == 2) ltext(x+10, y, svgm$np, adj = c(0,0.5)) #$ panel.xyplot(x,y,...) }, xlim = c(0, 1590), cex = .5, pch = 3 )
plot(variogram(log(zinc) ~ 1, meuse)) // 对每一个距离去平均
sel <- plot(variogram(zinc ~ 1, meuse, cloud = TRUE), digitize = TRUE) plot(sel, meuse)
v <- variogram(log(zinc) ~ 1, meuse) print(xyplot(gamma ~ dist, v, pch = 3, type = 'b', lwd = 2, col = 'darkblue', panel = function(x, y, ...) { for (i in 1:100) { meuse$random = sample(meuse$zinc) v = variogram(log(random) ~ 1, meuse) llines(v$dist, v$gamma, col = 'grey') } panel.xyplot(x, y, ...) }, ylim = c(0, 0.75), xlab = 'distance', ylab = 'semivariance' ))
