I am newbie in R scripts :-)
I need build a correlation matrix and I´am trying to configurate some parameters to adapt the graph. I am using the corrplot
package.
I Built a corrplot matrix this way:
corrplot(cor(d1[,2:14], d1[,2:14]), method=c("color"),
bg = "white", addgrid.col = "gray50",
tl.cex=1, type="lower", tl.col = "black",
col = colorRampPalette(c("red","white","blue"))(100))
I need show the values of correlation in the lower matrix inside the color matrix that I built. How i can do that?
Is it possible exclude the main diagonal from the lower matrix? In this diagonl always we have the perfect correlation.
The other doubt - I want to show the significant values for the correlation using stars instead of squares. like (*, , *). Is it possible?
Can you help me guys?
[ R , PValue ] = corrplot( Tbl ) plots the Pearson's correlation coefficients between all pairs of variables in the table or timetable Tbl , and also returns tables for the correlation matrix R and matrix of p-values PValue .
R package corrplot provides a visual exploratory tool on correlation matrix that supports automatic variable reordering to help detect hidden patterns among variables. corrplot is very easy to use and provides a rich array of plotting options in visualization method, graphic layout, color, legend, text labels, etc.
The correlation coefficient value size in correlation matrix plot created by using corrplot function ranges from 0 to 1, 0 referring to the smallest and 1 referring to the largest, by default it is 1. To change this size, we need to use number. cex argument.
With a bit of hackery you can do this in a very similar R package, corrgram
. This one allows you to easily define your own panel functions, and helpfully makes theirs easy to view as templates. Here's the some code and figure produced:
set.seed(42)
library(corrgram)
# This panel adds significance starts, or NS for not significant
panel.signif <- function (x, y, corr = NULL, col.regions, digits = 2, cex.cor,
...) {
usr <- par("usr")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
results <- cor.test(x, y, alternative = "two.sided")
est <- results$p.value
stars <- ifelse(est < 5e-4, "***",
ifelse(est < 5e-3, "**",
ifelse(est < 5e-2, "*", "NS")))
cex.cor <- 0.4/strwidth(stars)
text(0.5, 0.5, stars, cex = cex.cor)
}
# This panel combines edits the "shade" panel from the package
# to overlay the correlation value as requested
panel.shadeNtext <- function (x, y, corr = NULL, col.regions, ...)
{
if (is.null(corr))
corr <- cor(x, y, use = "pair")
ncol <- 14
pal <- col.regions(ncol)
col.ind <- as.numeric(cut(corr, breaks = seq(from = -1, to = 1,
length = ncol + 1), include.lowest = TRUE))
usr <- par("usr")
rect(usr[1], usr[3], usr[2], usr[4], col = pal[col.ind],
border = NA)
box(col = "lightgray")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- formatC(corr, digits = 2, format = "f")
cex.cor <- .8/strwidth("-X.xx")
text(0.5, 0.5, r, cex = cex.cor)
}
# Generate some sample data
sample.data <- matrix(rnorm(100), ncol=10)
# Call the corrgram function with the new panel functions
# NB: call on the data, not the correlation matrix
corrgram(sample.data, type="data", lower.panel=panel.shadeNtext,
upper.panel=panel.signif)
The code isn't very clean, as it's mostly patched together functions from the package, but it should give you a good start to get the plot you want. Possibly you can take a similar approach with the corrplot
package too.
update: Here's a version with stars and cor on the same triangle:
panel.shadeNtext <- function (x, y, corr = NULL, col.regions, ...)
{
corr <- cor(x, y, use = "pair")
results <- cor.test(x, y, alternative = "two.sided")
est <- results$p.value
stars <- ifelse(est < 5e-4, "***",
ifelse(est < 5e-3, "**",
ifelse(est < 5e-2, "*", "")))
ncol <- 14
pal <- col.regions(ncol)
col.ind <- as.numeric(cut(corr, breaks = seq(from = -1, to = 1,
length = ncol + 1), include.lowest = TRUE))
usr <- par("usr")
rect(usr[1], usr[3], usr[2], usr[4], col = pal[col.ind],
border = NA)
box(col = "lightgray")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- formatC(corr, digits = 2, format = "f")
cex.cor <- .8/strwidth("-X.xx")
fonts <- ifelse(stars != "", 2,1)
# option 1: stars:
text(0.5, 0.4, paste0(r,"\n", stars), cex = cex.cor)
# option 2: bolding:
#text(0.5, 0.5, r, cex = cex.cor, font=fonts)
}
# Generate some sample data
sample.data <- matrix(rnorm(100), ncol=10)
# Call the corrgram function with the new panel functions
# NB: call on the data, not the correlation matrix
corrgram(sample.data, type="data", lower.panel=panel.shadeNtext,
upper.panel=NULL)
Also commented out is another way of showing significance, it'll bold those below a threshold rather than using stars. Might be clearer that way, depending on what you want to show.
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