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corrplot {corrplot}R Documentation
A visualization of a correlation matrix.
Description
A graphical display of a correlation matrix, confidence interval. The details are paid great attention to. It can also visualize a general matrix by setting is.corr = FALSE.
Usage
corrplot(corr, method = c("circle", "square", "ellipse", "number", "shade",
"color", "pie"), type = c("full", "lower", "upper"), add = FALSE,
col = NULL, bg = "white", title = "", is.corr = TRUE, diag = TRUE,
outline = FALSE, mar = c(0, 0, 0, 0), addgrid.col = NULL,
addCoef.col = NULL, addCoefasPercent = FALSE, order = c("original",
"AOE", "FPC", "hclust", "alphabet"), hclust.method = c("complete", "ward",
"ward.D", "ward.D2", "single", "average", "mcquitty", "median", "centroid"),
addrect = NULL, rect.col = "black", rect.lwd = 2, tl.pos = NULL,
tl.cex = 1, tl.col = "red", tl.offset = 0.4, tl.srt = 90,
cl.pos = NULL, cl.lim = NULL, cl.length = NULL, cl.cex = 0.8,
cl.ratio = 0.15, cl.align.text = "c", cl.offset = 0.5, number.cex = 1,
number.font = 2, number.digits = NULL, addshade = c("negative",
"positive", "all"), shade.lwd = 1, shade.col = "white", p.mat = NULL,
sig.level = 0.05, insig = c("pch", "p-value", "blank", "n"), pch = 4,
pch.col = "black", pch.cex = 3, plotCI = c("n", "square", "circle",
"rect"), lowCI.mat = NULL, uppCI.mat = NULL, na.label = "?",
na.label.col = "black", ...)
Arguments
corr
The correlation matrix to visualize, must be square if order is not "original". For general matrix, please using is.corr = FALSE to convert.
method
Character, the visualization method of correlation matrix to be used. Currently, it supports seven methods, named "circle" (default), "square", "ellipse", "number", "pie", "shade" and "color". See examples for details.
The areas of circles or squares show the absolute value of corresponding correlation coefficients. Method "pie" and "shade" came from Michael Friendly's job (with some adjustment about the shade added on), and "ellipse" came from D.J. Murdoch and E.D. Chow's job, see in section References.
type
Character, "full" (default), "upper" or "lower", display full matrix, lower triangular or upper triangular matrix.
add
Logical, if TRUE, the graph is added to an existing plot, otherwise a new plot is created.
col
Vector, the color of glyphs. It is distributed uniformly in cl.lim. If NULL, col will be colorRampPalette(col2)(200), see example about col2.
bg
The background color.
title
Character, title of the graph.
is.corr
Logical, whether the input matrix is a correlation matrix or not. We can visualize the non-correlation matrix by setting is.corr = FALSE.
diag
Logical, whether display the correlation coefficients on the principal diagonal.
outline
Logical or character, whether plot outline of circles, square and ellipse, or the color of these glyphs. If outline is TRUE, the default value is "black".
mar
See par.
addgrid.col
The color of the grid. If NA, don't add grid. If NULL the default value is chosen. The default value depends on method, if method is color or shade, the color of the grid is NA, that is, not draw grid; otherwise "grey".
addCoef.col
Color of coefficients added on the graph. If NULL (default), add no coefficients.
addCoefasPercent
Logic, whether translate coefficients into percentage style for spacesaving.
order
Character, the ordering method of the correlation matrix.
"original" for original order (default).
"AOE" for the angular order of the eigenvectors.
"FPC" for the first principal component order.
"hclust" for the hierarchical clustering order.
"alphabet" for alphabetical order.
See function corrMatOrder for details.
hclust.method
Character, the agglomeration method to be used when order is hclust. This should be one of "ward", "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median" or "centroid".
addrect
Integer, the number of rectangles draws on the graph according to the hierarchical cluster, only valid when order is hclust. If NULL (default), then add no rectangles.
rect.col
Color for rectangle border(s), only valid when addrect is equal or greater than 1.
rect.lwd
Numeric, line width for borders for rectangle border(s), only valid when addrect is equal or greater than 1.
tl.pos
Character or logical, position of text labels. If character, it must be one of "lt", "ld", "td", "d" or "n". "lt"(default if type=="full") means left and top, "ld"(default if type=="lower") means left and diagonal, "td"(default if type=="upper") means top and diagonal(near), "d" means diagonal, "n" means don't add textlabel.
tl.cex
Numeric, for the size of text label (variable names).
tl.col
The color of text label.
tl.offset
Numeric, for text label, see text.
tl.srt
Numeric, for text label string rotation in degrees, see text.
cl.pos
Character or logical, position of color labels; If character, it must be one of "r" (default if type=="upper" or "full"), "b" (default if type=="lower") or "n", "n" means don't draw colorlabel.
cl.lim
The limits (x1, x2) in the colorlabel.
cl.length
Integer, the number of number-text in colorlabel, passed to colorlegend. If NULL, cl.length is length(col) + 1 when length(col) <=20; cl.length is 11 when length(col) > 20
cl.cex
Numeric, cex of number-label in colorlabel, passed to colorlegend.
cl.ratio
Numeric, to justify the width of colorlabel, 0.1~0.2 is suggested.
cl.align.text
Character, "l", "c" (default) or "r", for number-label in colorlabel, "l" means left, "c" means center, and "r" means right.
cl.offset
Numeric, for number-label in colorlabel, see text.
number.cex
The cex parameter to send to the call to text when writing the correlation coefficients into the plot.
number.font
the font parameter to send to the call to text when writing the correlation coefficients into the plot.
number.digits
indicating the number of decimal digits to be added into the plot. Non-negative integer or NULL, default NULL.
addshade
Character for shade style, "negative", "positive" or "all", only valid when method is "shade". If "all", all correlation coefficients' glyph will be shaded; if "positive", only the positive will be shaded; if "negative", only the negative will be shaded. Note: the angle of shade line is different, 45 degrees for positive and 135 degrees for negative.
shade.lwd
Numeric, the line width of shade.
shade.col
The color of shade line.
p.mat
Matrix of p-value, if NULL, arguments sig.level, insig, pch, pch.col, pch.cex is invalid.
sig.level
Significant level, if the p-value in p-mat is bigger than sig.level, then the corresponding correlation coefficient is regarded as insignificant.
insig
Character, specialized insignificant correlation coefficients, "pch" (default), "p-value", "blank" or "n". If "blank", wipe away the corresponding glyphs; if "p-value", add p-values the corresponding glyphs; if "pch", add characters (see pch for details) on corresponding glyphs; if "n", don't take any measures.
pch
Add character on the glyphs of insignificant correlation coefficients(only valid when insig is "pch"). See par.
pch.col
The color of pch (only valid when insig is "pch").
pch.cex
The cex of pch (only valid when insig is "pch").
plotCI
Character, method of ploting confidence interval. If "n", don't plot confidence interval. If "rect", plot rectangles whose upper side means upper bound and lower side means lower bound, respectively, and meanwhile correlation coefficients are also added on the rectangles. If "circle", first plot a circle with the bigger absolute bound, and then plot the smaller. Warning: if the two bounds are the same sign, the smaller circle will be wiped away, thus forming a ring. Method "square" is similar to "circle".
lowCI.mat
Matrix of the lower bound of confidence interval.
uppCI.mat
Matrix of the upper bound of confidence interval.
na.label
Label to be used for rendering NA cells. Default is "?". If "square", then the cell is rendered as a square with the na.label.col color.
na.label.col
Color used for rendering NA cells. Default is "black".
...
Additional arguments passing to function text for drawing text lable.
Details
corrplot function offers flexible ways to visualize correlation matrix, lower and upper bound of confidence interval matrix.
Value
(Invisibly) returns a reordered correlation matrix.
Note
Cairo and cairoDevice packages is strongly recommended to produce high-quality PNG, JPEG, TIFF bitmap files, especially for that method circle, ellipse.
Author(s)
Taiyun Wei (weitaiyun@gmail.com)
Viliam Simko (viliam.simko@gmail.com)
References
Michael Friendly (2002). Corrgrams: Exploratory displays for correlation matrices. The American Statistician, 56, 316–324.
D.J. Murdoch, E.D. Chow (1996). A graphical display of large correlation matrices. The American Statistician, 50, 178–180.
See Also
Function plotcorr in the ellipse package and corrgram in the corrgram package have some similarities.
Package seriation offered more methods to reorder matrices, such as ARSA, BBURCG, BBWRCG, MDS, TSP, Chen and so forth.
Examples
data(mtcars)
M <- cor(mtcars)
## different color series
col1 <- colorRampPalette(c("#7F0000","red","#FF7F00","yellow","white",
"cyan", "#007FFF", "blue","#00007F"))
col2 <- colorRampPalette(c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7",
"#FFFFFF", "#D1E5F0", "#92C5DE", "#4393C3", "#2166AC", "#053061"))
col3 <- colorRampPalette(c("red", "white", "blue"))
col4 <- colorRampPalette(c("#7F0000","red","#FF7F00","yellow","#7FFF7F",
"cyan", "#007FFF", "blue","#00007F"))
wb <- c("white","black")
par(ask = TRUE)
## different color scale and methods to display corr-matrix
corrplot(M, method = "number", col = "black", cl.pos = "n")
corrplot(M, method = "number")
corrplot(M)
corrplot(M, order = "AOE")
corrplot(M, order = "AOE", addCoef.col = "grey")
corrplot(M, order = "AOE", col = col1(20), cl.length = 21, addCoef.col = "grey")
corrplot(M, order = "AOE", col = col1(10), addCoef.col = "grey")
corrplot(M, order = "AOE", col = col2(200))
corrplot(M, order = "AOE", col = col2(200), addCoef.col = "grey")
corrplot(M, order = "AOE", col = col2(20), cl.length = 21, addCoef.col = "grey")
corrplot(M, order = "AOE", col = col2(10), addCoef.col = "grey")
corrplot(M, order = "AOE", col = col3(100))
corrplot(M, order = "AOE", col = col3(10))
corrplot(M, method="color", col=col1(20), cl.length=21,order = "AOE", addCoef.col="grey")
corrplot(M, method="square", col=col2(200),order = "AOE")
corrplot(M, method="ellipse", col=col1(200),order = "AOE")
corrplot(M, method="shade", col=col3(20),order = "AOE")
corrplot(M, method="pie", order = "AOE")
## col=wb
corrplot(M, col = wb, order="AOE", outline=TRUE, cl.pos="n")
## like Chinese wiqi, suit for either on screen or white-black print.
corrplot(M, col = wb, bg="gold2", order="AOE", cl.pos="n")
## mixed methods: It's more efficient if using function "corrplot.mixed"
## circle + ellipse
corrplot(M,order="AOE",type="upper",tl.pos="d")
corrplot(M,add=TRUE, type="lower", method="ell",order="AOE",
diag=FALSE,tl.pos="n", cl.pos="n")
## circle + square
corrplot(M,order="AOE",type="upper",tl.pos="d")
corrplot(M,add=TRUE, type="lower", method="square",order="AOE",
diag=FALSE,tl.pos="n", cl.pos="n")
## circle + colorful number
corrplot(M,order="AOE",type="upper",tl.pos="d")
corrplot(M,add=TRUE, type="lower", method="number",order="AOE",
diag=FALSE,tl.pos="n", cl.pos="n")
## circle + black number
corrplot(M,order="AOE",type="upper",tl.pos="tp")
corrplot(M,add=TRUE, type="lower", method="number",order="AOE", col="black",
diag=FALSE,tl.pos="n", cl.pos="n")
## order is hclust and draw rectangles
corrplot(M, order="hclust")
corrplot(M, order="hclust", addrect = 2)
corrplot(M, order="hclust", addrect = 3, rect.col = "red")
corrplot(M, order="hclust", addrect = 4, rect.col = "blue")
corrplot(M, order="hclust", hclust.method="ward", addrect = 4)
## visualize a matrix in [0, 1]
corrplot(abs(M),order="AOE", cl.lim=c(0,1))
corrplot(abs(M),order="AOE", col=col1(20), cl.lim=c(0,1))
corrplot(abs(M),order="AOE", col=col3(200), cl.lim=c(0,1))
## visualize a matrix in [-100, 100]
ran <- round(matrix(runif(225, -100,100), 15))
corrplot(ran, is.corr=FALSE)
corrplot(ran, is.corr=FALSE, cl.lim=c(-100, 100))
## text-labels and plot type
corrplot(M, order="AOE", tl.srt=45)
corrplot(M, order="AOE", tl.srt=60)
corrplot(M, order="AOE", tl.pos="d",cl.pos="n")
corrplot(M, order="AOE", diag=FALSE, tl.pos="d")
corrplot(M, order="AOE", type="upper")
corrplot(M, order="AOE", type="upper", diag=FALSE)
corrplot(M, order="AOE", type="lower", cl.pos="b")
corrplot(M, order="AOE", type="lower", cl.pos="b", diag=FALSE)
#### color-legend
corrplot(M, order="AOE", cl.ratio=0.2, cl.align="l")
corrplot(M, order="AOE", cl.ratio=0.2, cl.align="c")
corrplot(M, order="AOE", cl.ratio=0.2, cl.align="r")
corrplot(M, order="AOE", cl.pos="b")
corrplot(M, order="AOE", cl.pos="b", tl.pos="d")
corrplot(M, order="AOE", cl.pos="n")
## deal with missing Values
M2 <- M
diag(M2) = NA
corrplot(M2)
corrplot(M2, na.label = "o")
corrplot(M2, na.label = "NA")
##the input matrix is not square
corrplot(M[1:8,])
corrplot(M[,1:8])
cor.mtest <- function(mat, conf.level = 0.95){
mat <- as.matrix(mat)
n <- ncol(mat)
p.mat <- lowCI.mat <- uppCI.mat <- matrix(NA, n, n)
diag(p.mat) <- 0
diag(lowCI.mat) <- diag(uppCI.mat) <- 1
for(i in 1:(n-1)){
for(j in (i+1):n){
tmp <- cor.test(mat[,i], mat[,j], conf.level = conf.level)
p.mat[i,j] <- p.mat[j,i] <- tmp$p.value
lowCI.mat[i,j] <- lowCI.mat[j,i] <- tmp$conf.int[1]
uppCI.mat[i,j] <- uppCI.mat[j,i] <- tmp$conf.int[2]
}
}
return(list(p.mat, lowCI.mat, uppCI.mat))
}
res1 <- cor.mtest(mtcars,0.95)
res2 <- cor.mtest(mtcars,0.99)
## specialized the insignificant value according to the significant level
corrplot(M, p.mat = res1[[1]], sig.level=0.2)
corrplot(M, p.mat = res1[[1]], sig.level=0.05)
corrplot(M, p.mat = res1[[1]], sig.level=0.01)
corrplot(M, p.mat = res1[[1]], insig = "blank")
corrplot(M, p.mat = res1[[1]], insig = "p-value")
corrplot(M, p.mat = res1[[1]], insig = "p-value", sig.level=-1) ## add all p-values
corrplot(M, p.mat = res1[[1]], order="hclust", insig = "blank", addrect=3)
corrplot(M, p.mat = res1[[1]], order="hclust", insig = "pch", addrect=3)
## plot confidence interval(0.95), "square" method
corrplot(M,low=res1[[2]], upp=res1[[3]],
plotC="circle", addg="grey20",cl.pos="n")
corrplot(M, p.mat = res1[[1]],low=res1[[2]], upp=res1[[3]],
plotC="circle", addg="grey20",cl.pos="n")
corrplot(M, low=res1[[2]], upp=res1[[3]],
col=c("white","black"),bg="gold2",order="AOE",
plotCI="circle",cl.pos="n",pch.col="red")
corrplot(M, p.mat = res1[[1]], low=res1[[2]], upp=res1[[3]],
col=c("white","black"),bg="gold2",order="AOE",
plotCI="circle",cl.pos="n",pch.col="red")
## plot confidence interval(0.95), "square" method
corrplot(M, low=res1[[2]], upp=res1[[3]],
col=c("white","black"),bg="gold2", order="AOE",
plotCI="square",addg=NULL,cl.pos="n")
corrplot(M, p.mat = res1[[1]],low=res1[[2]], upp=res1[[3]],
col=c("white","black"),bg="gold2",order="AOE",pch.col="red",
plotC="square", addg=NULL,cl.pos="n")
## plot confidence interval(0.95, 0.95, 0.99), "rect" method
corrplot(M, low=res1[[2]], upp=res1[[3]], order="hclust",
rect.col="navy", plotC="rect",cl.pos="n")
corrplot(M, p.mat = res1[[1]], low=res1[[2]], upp=res1[[3]], order="hclust",
pch.col="red", sig.level = 0.05, addrect=3, rect.col="navy",
plotC="rect",cl.pos="n")
corrplot(M, p.mat = res2[[1]], low=res2[[2]], upp=res2[[3]], order="hclust",
pch.col="red", sig.level = 0.01, addrect=3, rect.col="navy",
plotC="rect",cl.pos="n")
## an animation of changing confidence interval in different significance level
## begin.animaton
par(ask=FALSE)
for(i in seq(0.1, 0, -0.005)){
tmp <- cor.mtest(mtcars,1-i)
corrplot(M, p.mat = tmp[[1]], low=tmp[[2]], upp=tmp[[3]], order="hclust",
pch.col="red", sig.level = i, plotC="rect", cl.pos="n",
mar=c(0,0,1,0),
title=substitute(alpha == x,list(x=format(i,digits=3,nsmall=3))))
Sys.sleep(0.15)
}
## end.animaton
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