- Color Image
data(mtcars)
fit = lm(mpg ~ ., mtcars)
cor = summary(fit, correlation = TRUE)$correlation
cor2 = t(cor[11:1, ])
colors = c("#A50F15", "#DE2D26", "#FB6A4A", "#FCAE91", "#FEE5D9",
"white", "#EFF3FF", "#BDD7E7", "#6BAED6", "#3182BD", "#08519C")
image(1:11, 1:11, cor2, axes = FALSE, ann = F, col = colors)
text(rep(1:11, 11), rep(1:11, each = 11), round(100 * cor2))
- Ellipses
library(ellipse)
col = colors[as.vector(apply(corr, 2, rank))]
plotcorr(cor, col = col, mar = rep(0, 4))
Taiyun's circles (my method)
circle.cor = function(cor, axes = FALSE, xlab = "",
ylab = "", asp = 1, title = "Taiyun's cor-matrix circles",
...) {
n = nrow(cor)
par(mar = c(0, 0, 2, 0), bg = "white")
plot(c(0, n + 0.8), c(0, n + 0.8), axes = axes, xlab = "",
ylab = "", asp = 1, type = "n")
##add grid
segments(rep(0.5, n + 1), 0.5 + 0:n, rep(n + 0.5, n + 1),
0.5 + 0:n, col = "gray")
segments(0.5 + 0:n, rep(0.5, n + 1), 0.5 + 0:n, rep(n + 0.5,
n), col = "gray")
##define circles' background color.
##black for positive correlation coefficient and white for negative
bg = cor
bg[cor > 0] = "black"
bg[cor <= 0] = "white" ##plot n*n circles using vector language, suggested by Yihui Xie symbols(rep(1:n, each = n), rep(n:1, n), add = TRUE, inches = F, circles = as.vector(sqrt(abs(cor))/2), bg = as.vector(bg)) text(rep(0, n), 1:n, n:1, col = "red") text(1:n, rep(n + 1), 1:n, col = "red") title(title) } ## an example data(mtcars) fit = lm(mpg ~ ., mtcars) cor = summary(fit, correlation = TRUE)$correlation circle.cor(cor)
The circles with black background denote positive correlation coefficient, and the area of circles denotes the absolute value. See more in my Picasa here.The above three graphs based on the same data. Dear friends, which gives your more information at first galance?
