如何从分组数据中的点获得平滑的表面？

本文介绍了如何从分组数据中的点获得平滑的表面？的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述 使用 ggplot（。）+ geom_smooth（），我们可以通过分组数据点获得漂亮的曲线。With ggplot(.) + geom_smooth() we can achieve nice curves through points with grouped data.library(ggplot2)span <- 10ggplot(data, aes(x = x, y = value, group = n)) + geom_smooth(aes(linetype = n), color = "blue", se = FALSE) 现在我有了第三维，想将之间的关系可视化值和两个变量 var1，var2 在3D中。我做了几次尝试，只有 car :: scatter3d 使我更接近自己想要的东西。但是我在那里找不到平滑选项，也没有保存图的选项。Now I have a third dimension and want to visualize the relation between value and the two variables var1, var2 in 3D. I made several attempts of which only car::scatter3d brought me more closely to what I want. But I couldn't find a "smooth" option there and also no option to save the plot.library(car)scatter3d(value ~ var1 + var2, data, surface = FALSE, point.col = "blue", axis.ticks = TRUE, sphere.size = .8) 我也尝试了 rgl，plot3D，lattice 和 plotly 包，但没有成功；从前两个中我收到错误，从最后两个中我收到错误网格。I also tried rgl, plot3D, lattice and plotly package but with no success; from first two I'm receiving errors and from the last two just empty grids.library(rgl)persp3d(value ~ var1 + var2, data, col="skyblue")# Error in seq.default(0, 1, len = nrow(z)) : # argument 'length.out' must be of length 1library(plot3D)surf3D(as.matrix(data1[, 1]), as.matrix(data1[, 2]), as.matrix(data1[, 3]))# Error in if (is.na(var)) ispresent <- FALSE else if (length(var) == 1) if (is.logic# al(var)) if (!var) ispresent <- FALSE : # argument is of length zerolibrary(lattice)wireframe(value ~ var1 + var2, data)# empty or wronglibrary(plotly)plot_ly(x = data$var1, y = data$value, z = data$var1, type = "surface")# empty如何在分组数据中实现具有三维空间的平滑表面？我的目标是这样（仅与上述 ggplot（）中的组一样）：How is it possible to achieve smoothed surfaces with third dimension in grouped data? I'm aiming at something like this (just with the groups like in ggplot() above): 数据： Data:data <- structure(list(n = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("1", "2", "3", "4" ), class = "factor"), x = c(0, 0, 0, 0, 0, 0, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5), y = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.5), value = c(0, 0.000253671562082777, 0.00048064085447263, 0.000680907877169559, 0.000854472630173565, 0.00100133511348465, 0.000253671562082777, 0.00048064085447263, 0.000680907877169559, 0.000854472630173565, 0.00100133511348465, 0.0011214953271028, 0.00048064085447263, 0.000680907877169559, 0.000854472630173565, 0.00100133511348465, 0.0011214953271028, 0.00121495327102804, 0.000680907877169559, 0.000854472630173565, 0.00100133511348465, 0.0011214953271028, 0.00121495327102804, 0.00128170894526035, 0.000854472630173565, 0.00100133511348465, 0.0011214953271028, 0.00121495327102804, 0.00128170894526035, 0.00132176234979973, 0.00100133511348465, 0.0011214953271028, 0.00121495327102804, 0.00128170894526035, 0.00132176234979973, 0.00133511348464619, 0, 0.000126751167444963, 0.000240160106737825, 0.000340226817878586, 0.000426951300867245, 0.000500333555703803, 0.000126751167444963, 0.000240160106737825, 0.000340226817878586, 0.000426951300867245, 0.000500333555703803, 0.000560373582388259, 0.000240160106737825, 0.000340226817878586, 0.000426951300867245, 0.000500333555703803, 0.000560373582388259, 0.000607071380920614, 0.000340226817878586, 0.000426951300867245, 0.000500333555703803, 0.000560373582388259, 0.000607071380920614, 0.000640426951300867, 0.000426951300867245, 0.000500333555703803, 0.000560373582388259, 0.000607071380920614, 0.000640426951300867, 0.000660440293529019, 0.000500333555703803, 0.000560373582388259, 0.000607071380920614, 0.000640426951300867, 0.000660440293529019, 0.00066711140760507, 0, 6.33544514838279e-05, 0.000120040013337779, 0.000170056685561854, 0.000213404468156052, 0.000250083361120373, 6.33544514838279e-05, 0.000120040013337779, 0.000170056685561854, 0.000213404468156052, 0.000250083361120373, 0.000280093364454818, 0.000120040013337779, 0.000170056685561854, 0.000213404468156052, 0.000250083361120373, 0.000280093364454818, 0.000303434478159386, 0.000170056685561854, 0.000213404468156052, 0.000250083361120373, 0.000280093364454818, 0.000303434478159386, 0.000320106702234078, 0.000213404468156052, 0.000250083361120373, 0.000280093364454818, 0.000303434478159386, 0.000320106702234078, 0.000330110036678893, 0.000250083361120373, 0.000280093364454818, 0.000303434478159386, 0.000320106702234078, 0.000330110036678893, 0.000333444481493831, 0, 1.26675111674112e-05, 2.40016001066738e-05, 3.40022668177879e-05, 4.26695113007534e-05, 5.00033335555704e-05, 1.26675111674112e-05, 2.40016001066738e-05, 3.40022668177879e-05, 4.26695113007534e-05, 5.00033335555704e-05, 5.60037335822388e-05, 2.40016001066738e-05, 3.40022668177879e-05, 4.26695113007534e-05, 5.00033335555704e-05, 5.60037335822388e-05, 6.06707113807587e-05, 3.40022668177879e-05, 4.26695113007534e-05, 5.00033335555704e-05, 5.60037335822388e-05, 6.06707113807587e-05, 6.40042669511301e-05, 4.26695113007534e-05, 5.00033335555704e-05, 5.60037335822388e-05, 6.06707113807587e-05, 6.40042669511301e-05, 6.60044002933529e-05, 5.00033335555704e-05, 5.60037335822388e-05, 6.06707113807587e-05, 6.40042669511301e-05, 6.60044002933529e-05, 6.66711114074272e-05 )), .Names = c("n", "x", "y", "value"), row.names = c(NA, -144L), class = "data.frame") 推荐答案您的数据在每个级别 n 中看起来像一个网格，因此在 rgl 中，您可以强制 x ， y 和 value 列到矩阵并使用 persp3d ：Your data looks like a grid within each level of n, so in rgl you could coerce the x, y, and value columns to matrices and use persp3d:library(rgl)open3d()for (level in unique(data$n)) { sub <- subset(data, n == level) x <- matrix(sub$x, 6,6) y <- matrix(sub$y, 6,6) value <- matrix(sub$value, 6,6) persp3d(x, y, value, col = level, alpha = 0.5, add = level > 1)}（ add = level> 1 行是为了将第一个之后的级别添加到同一图中。）(The add = level > 1 line is there so that levels after the first one are added to the same plot.)如果您的数据不一定在网格中，您仍然可以绘制曲面，但是还需要做更多的工作。您需要 deldir 包对数据进行三角测量。例如，If your data isn't necessarily in a grid, you can still plot the surfaces, but it's a bit more work. You need the deldir package to triangulate your data. For example,library(rgl)library(deldir)open3d()for (level in unique(data$n)) { sub <- subset(data, n == level) surf <- deldir(sub$x, sub$y, z = sub$value, suppressMsge = TRUE) persp3d(surf, col = level, alpha = 0.5, add = TRUE)}aspect3d(1,1,1)decorate3d(zlab = "value")您需要 aspect3d 和 decorate3d 最后调用，因为 persp3d.deldir 不会自动设置纵横比或设置自定义轴标签。这样会You need the aspect3d and decorate3d calls at the end because persp3d.deldir doesn't automatically set the aspect ratio or set custom axis labels. This gives 这篇关于如何从分组数据中的点获得平滑的表面？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！