[英]Creating a trellised (faceted) thin-plate spline response surface
I am trying to plot a bunch of thin-plate spline response surfaces for measurements related to two continuous variables plus one discrete variable. 我试图绘制一组薄板样条响应曲面,用于与两个连续变量加一个离散变量相关的测量。 So far, I have been subsetting the data based on the discrete variable to generate pairs of plots, but it seems to me there should be a way to create some slick trellised plots instead.
到目前为止,我已经基于离散变量对数据进行了子集化以生成图对,但在我看来应该有一种方法来创建一些光滑的格子图。 It seems like this could be done by faceting heatmaps in
ggplot2
with geom_tile
and geom_contour
, but I am stuck on 看起来这可以通过使用
geom_tile
和geom_contour
在ggplot2
中geom_tile
热图来完成,但我被困在
(1) how to reorganize the data (or interpret the predicted surface data) for plotting with ggplot2
? (1)如何重组数据(或解释预测的表面数据)以便用
ggplot2
进行绘图?
(2) syntax for creating trellised heatmaps with base graphics? (2)使用基本图形创建格子化热图的语法? or
要么
(3) ways to use graphics from rsm
to accomplish this ( rsm
can cope with higher-order surfaces, so I could coerce things to some extent, but plots aren't fully trellised). (3)使用
rsm
图形来实现这一目标的方法( rsm
可以处理高阶表面,所以我可以在某种程度上强制执行,但是情节并没有完全格式化)。
Here's an example of what I've been working with so far: 这是我到目前为止一直在使用的一个例子:
library(fields)
library(ggplot2)
sumframe<-structure(list(Morph = c("LW", "LW", "LW", "LW", "LW", "LW",
"LW", "LW", "LW", "LW", "LW", "LW", "LW", "SW", "SW", "SW", "SW",
"SW", "SW", "SW", "SW", "SW", "SW", "SW", "SW", "SW"), xvalue = c(4,
8, 9, 9.75, 13, 14, 16.25, 17.25, 18, 23, 27, 28, 28.75, 4, 8,
9, 9.75, 13, 14, 16.25, 17.25, 18, 23, 27, 28, 28.75), yvalue = c(17,
34, 12, 21.75, 29, 7, 36.25, 14.25, 24, 19, 36, 14, 23.75, 17,
34, 12, 21.75, 29, 7, 36.25, 14.25, 24, 19, 36, 14, 23.75), zvalue = c(126.852666666667,
182.843333333333, 147.883333333333, 214.686666666667, 234.511333333333,
198.345333333333, 280.9275, 246.425, 245.165, 247.611764705882,
266.068, 276.744, 283.325, 167.889, 229.044, 218.447777777778,
207.393, 278.278, 203.167, 250.495, 329.54, 282.463, 299.825,
286.942, 372.103, 307.068)), .Names = c("Morph", "xvalue", "yvalue",
"zvalue"), row.names = c(NA, -26L), class = "data.frame")
sumframeLW<-subset(sumframe, Morph=="LW")
sumframeSW<-subset(sumframe, Morph="SW")
split.screen(c(1,2))
screen(n=1)
surf.teLW<-Tps(cbind(sumframeLW$xvalue, sumframeLW$yvalue), sumframeLW$zvalue, lambda=0.01)
summary(surf.teLW)
surf.te.outLW<-predict.surface(surf.teLW)
image(surf.te.outLW, col=tim.colors(128), xlim=c(0,38), ylim=c(0,38), zlim=c(100,400), lwd=5, las=1, font.lab=2, cex.lab=1.3, mgp=c(2.7,0.5,0), font.axis=1, lab=c(5,5,6), xlab=expression("X value"), ylab=expression("Y value"),main="LW plot")
contour(surf.te.outLW, lwd=2, labcex=1, add=T)
points(sumframeLW$xvalue, sumframeLW$yvalue, pch=21)
abline(a=0, b=1, lty=1, lwd=1.5)
abline(a=0, b=1.35, lty=2)
screen(n=2)
surf.teSW<-Tps(cbind(sumframeSW$xvalue, sumframeSW$yvalue), sumframeSW$zvalue, lambda=0.01)
summary(surf.teSW)
surf.te.outSW<-predict.surface(surf.teSW)
image(surf.te.outSW, col=tim.colors(128), xlim=c(0,38), ylim=c(0,38), zlim=c(100,400), lwd=5, las=1, font.lab=2, cex.lab=1.3, mgp=c(2.7,0.5,0), font.axis=1, lab=c(5,5,6), xlab=expression("X value"), ylab=expression("Y value"),main="SW plot")
contour(surf.te.outSW, lwd=2, labcex=1, add=T)
points(sumframeSW$xvalue, sumframeSW$yvalue, pch=21)
abline(a=0, b=1, lty=1, lwd=1.5)
abline(a=0, b=1.35, lty=2)
close.screen(all.screens=TRUE)
As noted in a comment, melt()
can be used to reshape the Tps()
output, then it can be reformatted a bit (to remove NA's), recombined into a single data frame, and plotted. 如注释中所述,可以使用
melt()
来重新Tps()
输出,然后可以重新格式化(删除NA),重新组合成单个数据帧并绘制。 Here are plots with ggplot2
and levelplot
: 以下是
ggplot2
和levelplot
:
library(reshape)
library(lattice)
LWsurfm<-melt(surf.te.outLW)
LWsurfm<-rename(LWsurfm, c("value"="z", "Var1"="x", "Var2"="y"))
LWsurfms<-na.omit(LWsurfm)
SWsurfms[,"Morph"]<-c("SW")
SWsurfm<-melt(surf.te.outSW)
SWsurfm<-rename(SWsurfm, c("value"="z", "X1"="x", "X2"="y"))
SWsurfms<-na.omit(SWsurfm)
LWsurfms[,"Morph"]<-c("LW")
LWSWsurf<-rbind(LWsurfms, SWsurfms)
LWSWp<-ggplot(LWSWsurf, aes(x,y,z=z))+facet_wrap(~Morph)
LWSWp<-LWSWp+geom_tile(aes(fill=z))+stat_contour()
LWSWp
or: levelplot(z~x*y|Morph, data=LWSWsurf, contour=TRUE) 或:levelplot(z~x * y | Morph,data = LWSWsurf,contour = TRUE)
require(rgl)
open3d()
plot3d
surface3d(surf.te.outSW$x, surf.te.outSW$y, surf.te.outSW$z, col="red")
surface3d(surf.te.outLW$x, surf.te.outLW$y, surf.te.outLW$z, col="blue")
decorate3d()
rgl.snapshot("OutRGL.png")
Another version where I scaled the x and y values by a factor of 10 and rotated to "look through" the gap. 另一个版本,我将x和y值缩放了10倍,并旋转到“透视”间隙。 If this were your choice you might want to look at ?scaleMatrix
如果这是您的选择,您可能需要查看?scaleMatrix
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