我希望在对角线上获得标签,如图3所示,但是corrplot
如图1-2所示。
我正在研究corrplot
手册here以获取数字对角线标签。
我不知道任何允许在corrplot
对角线上放置数字标签的方法,因为我设法伪造了所有潜在的选择。
伪造的东西
数字对角线标签无法通过函数cor.mtest
colnames(p.mat) <- rownames(p.mat) <- colnames(mat) <- diag.labels
colorlegend
显然不是正确的选择
corrplot(...)
colorlegend(colbar = grey(1:100 / 100), labels=ids, addlabels = TRUE)
部分事情很高兴知道但不限制我们
diag=FALSE, tl.pos="d"
适用于单个单元格。如何为N个细胞提供tl.pos="d"
? - - tl.pos=c("d")
会导致错误。 - - 数字对角标签是否需要tl.pos
?代码,但here代表不同的例子,其中 K.J.J.K 的第一个答案提案作为测试用例,但对于任务显示为false
library("corrplot")
# http://www.sthda.com/english/wiki/visualize-correlation-matrix-using-correlogram
cor.mtest <- function(mat, diag.labels, ...) {
mat <- as.matrix(mat)
n <- ncol(mat)
p.mat<- matrix(NA, n, n)
diag(p.mat) <- 0
for (i in 1:(n - 1)) {
for (j in (i + 1):n) {
tmp <- cor.test(mat[, i], mat[, j], ...)
p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
}
}
colnames(p.mat) <- rownames(p.mat) <- colnames(mat) <- diag.labels
p.mat
}
ids <- c(seq(1,11))
M<-cor(mtcars)
p.mat <- cor.mtest(mtcars, diag.labels=ids)
corrplot(M, type="upper", order="hclust", diag=FALSE, # TODO tl.pos=c("d"),
p.mat = p.mat, sig.level = 0.05)
图。 1输出,对角线上没有预期的标签,
图2伪造K.J.J.K的提议,其中对角标签没有影响,
图3找到corrgram
enumerated()
对角线上的标签示例
预期输出:对角线上的数字标签,如图3所示,但装饰另有所需,如图(1-2)所示
答案 0 :(得分:2)
library("corrplot")
# http://rstudio-pubs-static.s3.amazonaws.com/6382_886fbab74fd5499ba455f11360f78de7.html
# plotcorr(R, col = colorRampPalette(c("#E08214", "white", "#8073AC"))(10), type = "lower")
# http://www.sthda.com/english/wiki/visualize-correlation-matrix-using-correlogram
# corrplot(M, type="upper", order="hclust", tl.col="black", tl.srt=45)
## Compute p-value of correlations
# mat : is a matrix of data
# ... : further arguments to pass to the native R cor.test function
M<-cor(mtcars)
# http://www.sthda.com/english/wiki/visualize-correlation-matrix-using-correlogram
cor.mtest <- function(mat, ...) {
mat <- as.matrix(mat)
n <- ncol(mat)
p.mat<- matrix(NA, n, n)
diag(p.mat) <- 0
for (i in 1:(n - 1)) {
for (j in (i + 1):n) {
tmp <- cor.test(mat[, i], mat[, j], ...)
p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
}
}
colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
p.mat
}
# matrix of the p-value of the correlation
p.mat <- cor.mtest(mtcars)
head(p.mat[, 1:5])
corrplot(M, type="upper", order="hclust",
p.mat = p.mat, sig.level = 0.05)
# Leave blank on no significant coefficient
corrplot(M, type="upper", order="hclust",
p.mat = p.mat, sig.level = 0.01, insig = "blank")
col <- colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
corrplot(M, method="color", col=col(200),
type="upper", order="hclust",
addCoef.col = "black", # Add coefficient of correlation
tl.col="black", tl.srt=45, #Text label color and rotation
# Combine with significance
p.mat = p.mat, sig.level = 0.01, insig = "blank",
# hide correlation coefficient on the principal diagonal
diag=FALSE
)
ids <- c(seq(1,11))
M<-cor(mtcars)
colnames(M)<-ids
rownames(M)<-c("I","told","you","row","names","controls","the","diag","labels","kj","jk")
corrplot(M, type="upper",p.mat = p.mat, sig.level = 0.05)