如何为使用splom在晶格中绘制的相关系数添加p值?

本文介绍了如何为使用splom在晶格中绘制的相关系数添加p值?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 我正在使用晶格制作散点图矩阵，并在面板的上半部分绘制12个变量的相关系数.我还想在相关系数或星号下方添加p值，以指示其显着性水平.这是我的R代码.我怎样才能做到这一点?提前非常感谢！

这是我的数据样本

d.corr1 = structure(list(maxt1.res = c(-0.944678376630112, 0.324463929632583, -1.18820341118942, -0.656600399095673, 0.332432965913295, 0.696656683837386 ), maxt2.res = c(1.81878373188327, -0.437581385609662, 0.305933316224282, -3.20946216261864, 0.629812177862245, -1.49044366233353), maxt3.res = c(-1.21422295698813, -1.31516252550763, 0.570370111383564, 1.73177495368256, 2.18742200139099, 0.413531254505875), mint1.res = c(0.783488332204165, 0.35387082927864, -0.528584845400234, 0.772682308165534, 0.421127289975828, 1.06059010003109 ), mint2.res = c(0.262876147753049, 0.588802881606123, 0.745673830291112, -1.22383100619312, -1.01594162784602, -0.135018034667641), mint3.res = c(0.283732674541107, -0.406567031719476, 0.390198644741853, 0.860359703924238, 1.27865614582901, 0.346477970454206), sr1.res = c(1.7258974480523, -1.71718783477085, 3.98573602228491, -4.42153098079411, 0.602511156003456, -3.07683756735513 ), sr2.res = c(9.98631829246284, -6.91757809846195, 0.418977023594041, -6.10811634134865, 14.6495418067316, 2.44365146778955), sr3.res = c(-3.8809447886743, 2.35230122374257, 2.8673756880306, 7.1449786041902, 2.07480997224678, 4.93316979213985), rain1.res = c(0.112986181584307, 0.0445969189874017, -0.446757191502526, 1.76152475011467, -0.395540856161192, -0.175756810329735 ), rain2.res = c(-0.645121126413379, 1.74415111794381, -0.122876137090066, 1.68048850848576, -0.570490345329031, 0.00308540146622738), rain3.res = c(-0.202762644577954, 0.0528174267822909, -0.0616752465852931, -0.167769364680304, -0.152822027502996, -0.139253335052929)), .Names = c("maxt1.res", "maxt2.res", "maxt3.res", "mint1.res", "mint2.res", "mint3.res", "sr1.res", "sr2.res", "sr3.res", "rain1.res", "rain2.res", "rain3.res" ), row.names = c(NA, 6L), class = "data.frame") attach(d.corr1) library(lattice) library(RColorBrewer) splom(~d.corr1[seq(1:12)], lower.panel = panel.splom, upper.panel = function(x, y, ...) { panel.fill(col = brewer.pal(9, "RdBu")[ round(cor(x, y) * 4 + 5)]) cpl <- current.panel.limits() panel.text(mean(cpl$xlim), mean(cpl$ylim), round(cor(x, y),2), font=2) }, scales = list(x = list( draw = TRUE, cex=0.1)), type = c("g", "p", "smooth"),layout = c(1, 1), pscales=0, pch=".", main="correlation between the weather variables after removing district F.E and yearly trends") dev.off() detach(d.corr1)

解决方案

另一种选择是两次使用panel.text，并使用不同的adj参数.

例如:

splom(~d.corr1[seq(1:12)], lower.panel = panel.splom, upper.panel = function(x, y, ...) { panel.fill(col = brewer.pal(9, "RdBu")[ round(cor(x, y) * 4 + 5)]) cpl <- current.panel.limits() ## translate upward panel.text(mean(cpl$xlim), mean(cpl$ylim), round(cor(x, y),2), font=2, adj=c(0.5,-0.6)) ## translate downward panel.text(mean(cpl$xlim), mean(cpl$ylim), round( cor.test(x,y)$p.value, 2), font=1, adj=c(0.5,0.6),col='blue') },

I am making a scatterplot matrix using lattice and plotting the correlation coefficients of 12 variables in the upper half of the panel. I would also like to add the p values beneath the correlation coeffiecients or stars indicating their level of significance. Here is my R code. How can I achieve this? Many thanks in advance!

Here is a sample of my data

d.corr1 = structure(list(maxt1.res = c(-0.944678376630112, 0.324463929632583, -1.18820341118942, -0.656600399095673, 0.332432965913295, 0.696656683837386 ), maxt2.res = c(1.81878373188327, -0.437581385609662, 0.305933316224282, -3.20946216261864, 0.629812177862245, -1.49044366233353), maxt3.res = c(-1.21422295698813, -1.31516252550763, 0.570370111383564, 1.73177495368256, 2.18742200139099, 0.413531254505875), mint1.res = c(0.783488332204165, 0.35387082927864, -0.528584845400234, 0.772682308165534, 0.421127289975828, 1.06059010003109 ), mint2.res = c(0.262876147753049, 0.588802881606123, 0.745673830291112, -1.22383100619312, -1.01594162784602, -0.135018034667641), mint3.res = c(0.283732674541107, -0.406567031719476, 0.390198644741853, 0.860359703924238, 1.27865614582901, 0.346477970454206), sr1.res = c(1.7258974480523, -1.71718783477085, 3.98573602228491, -4.42153098079411, 0.602511156003456, -3.07683756735513 ), sr2.res = c(9.98631829246284, -6.91757809846195, 0.418977023594041, -6.10811634134865, 14.6495418067316, 2.44365146778955), sr3.res = c(-3.8809447886743, 2.35230122374257, 2.8673756880306, 7.1449786041902, 2.07480997224678, 4.93316979213985), rain1.res = c(0.112986181584307, 0.0445969189874017, -0.446757191502526, 1.76152475011467, -0.395540856161192, -0.175756810329735 ), rain2.res = c(-0.645121126413379, 1.74415111794381, -0.122876137090066, 1.68048850848576, -0.570490345329031, 0.00308540146622738), rain3.res = c(-0.202762644577954, 0.0528174267822909, -0.0616752465852931, -0.167769364680304, -0.152822027502996, -0.139253335052929)), .Names = c("maxt1.res", "maxt2.res", "maxt3.res", "mint1.res", "mint2.res", "mint3.res", "sr1.res", "sr2.res", "sr3.res", "rain1.res", "rain2.res", "rain3.res" ), row.names = c(NA, 6L), class = "data.frame") attach(d.corr1) library(lattice) library(RColorBrewer) splom(~d.corr1[seq(1:12)], lower.panel = panel.splom, upper.panel = function(x, y, ...) { panel.fill(col = brewer.pal(9, "RdBu")[ round(cor(x, y) * 4 + 5)]) cpl <- current.panel.limits() panel.text(mean(cpl$xlim), mean(cpl$ylim), round(cor(x, y),2), font=2) }, scales = list(x = list( draw = TRUE, cex=0.1)), type = c("g", "p", "smooth"),layout = c(1, 1), pscales=0, pch=".", main="correlation between the weather variables after removing district F.E and yearly trends") dev.off() detach(d.corr1)

解决方案

Another option is to use panel.text twice , with different adj parameter.

For example :

splom(~d.corr1[seq(1:12)], lower.panel = panel.splom, upper.panel = function(x, y, ...) { panel.fill(col = brewer.pal(9, "RdBu")[ round(cor(x, y) * 4 + 5)]) cpl <- current.panel.limits() ## translate upward panel.text(mean(cpl$xlim), mean(cpl$ylim), round(cor(x, y),2), font=2, adj=c(0.5,-0.6)) ## translate downward panel.text(mean(cpl$xlim), mean(cpl$ylim), round( cor.test(x,y)$p.value, 2), font=1, adj=c(0.5,0.6),col='blue') },