corrplot (version 0.92)

corrplot: 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"),
  col = NULL,
  col.lim = NULL,
  bg = "white",
  title = "",
  is.corr = TRUE,
  add = FALSE,
  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.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", "label_sig"),
  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",
  win.asp = 1,
  ...
)

Value

(Invisibly) returns a list(corr, corrTrans, arg). corr is a reordered correlation matrix for plotting. corrPos is a data frame with xName, yName, x, y, corr and p.value(if p.mat is not NULL) column, which x and y are the position on the correlation matrix plot. arg is a list of some corrplot() input parameters' value. Now type is in.

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.

col

Vector, the colors of glyphs. They are distributed uniformly in col.lim interval. If is.corr is TRUE, the default value will be COL2('RdBu', 200). If is.corr is FALSE and corr is a non-negative or non-positive matrix, the default value will be COL1('YlOrBr', 200); otherwise (elements are partly positive and partly negative), the default value will be COL2('RdBu', 200).

col.lim

The limits (x1, x2) interval for assigning color by col. If NULL, col.lim will be c(-1, 1) when is.corr is TRUE, col.lim will be c(min(corr), max(corr)) when is.corr is FALSE

NOTICE: if you set col.lim when is.corr is TRUE, the assigning colors are still distributed uniformly in [-1, 1], it only affect the display on color-legend.

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.

add

Logical, if TRUE, the graph is added to an existing plot, otherwise a new plot will be created.

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. For pie, this represents the color of the circle outlining the pie. 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), 'l' means left, 'd' means diagonal, 'n' means don't add text-label.

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-legend; 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 color-legend.

cl.length

Integer, the number of number-text in color-legend, 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 color-legend, passed to colorlegend.

cl.ratio

Numeric, to justify the width of color-legend, 0.1~0.2 is suggested.

cl.align.text

Character, 'l', 'c' (default) or 'r', for number-label in color-legend, 'l' means left, 'c' means center, and 'r' means right.

cl.offset

Numeric, for number-label in color-legend, 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, parameter sig.level, insig, pch, pch.col, pch.cex are 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. If insig is 'label_sig', this may be an increasing vector of significance levels, in which case pch will be used once for the highest p-value interval and multiple times (e.g. '*', '**', '***') for each lower p-value interval.

insig

Character, specialized insignificant correlation coefficients, 'pch' (default), 'p-value', 'blank', 'n', or 'label_sig'. 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; if 'label_sig', mark significant correlations with pch (see sig.level).

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. 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'.

win.asp

Aspect ration for the whole plot. Value other than 1 is currently compatible only with methods 'circle' and 'square'.

...

Additional arguments passing to function text for drawing text label.

Author

Taiyun Wei ([e-mail])

Viliam Simko ([e-mail])

Michael Levy ([e-mail])

Details

corrplot function offers flexible ways to visualize correlation matrix, lower and upper bound of confidence interval matrix.

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)
set.seed(0)

##  different color series
## COL2: Get diverging colors
## c('RdBu', 'BrBG', 'PiYG', 'PRGn', 'PuOr', 'RdYlBu')
## COL1: Get sequential colors
## c('Oranges', 'Purples', 'Reds', 'Blues', 'Greens', 'Greys', 'OrRd', 'YlOrRd', 'YlOrBr', 'YlGn')

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',  cl.length = 21, addCoef.col = 'grey')
corrplot(M, order = 'AOE', col = COL2(n=10), addCoef.col = 'grey')

corrplot(M, order = 'AOE', col = COL2('PiYG'))
corrplot(M, order = 'AOE', col = COL2('PRGn'), addCoef.col = 'grey')
corrplot(M, order = 'AOE', col = COL2('PuOr', 20), cl.length = 21, addCoef.col = 'grey')
corrplot(M, order = 'AOE', col = COL2('PuOr', 10), addCoef.col = 'grey')

corrplot(M, order = 'AOE', col = COL2('RdYlBu', 100))
corrplot(M, order = 'AOE', col = COL2('RdYlBu', 10))


corrplot(M, method = 'color', col = COL2(n=20), cl.length = 21, order = 'AOE',
         addCoef.col = 'grey')
corrplot(M, method = 'square', col = COL2(n=200), order = 'AOE')
corrplot(M, method = 'ellipse', col = COL2(n=200), order = 'AOE')
corrplot(M, method = 'shade', col = COL2(n=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 = 'ellipse', 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.D2', addrect = 4)

## visualize a  matrix in [0, 1]
corrplot(abs(M), order = 'AOE', col.lim = c(0, 1))
corrplot(abs(M), order = 'AOE', is.corr = FALSE,  col.lim = c(0, 1))


# when is.corr=TRUE, col.lim only affect the color legend
# If you change it, the color is still assigned on [-1, 1]
corrplot(M/2)
corrplot(M/2, col.lim = c(-0.5, 0.5))

# when is.corr=FALSE, col.lim is also used to assign colors
# if the matrix have both positive and negative values
# the matrix transformation keep every values positive and negative
corrplot(M*2, is.corr = FALSE, col.lim = c(-2, 2))
corrplot(M*2, is.corr = FALSE, col.lim = c(-2, 2) * 2)
corrplot(M*2, is.corr = FALSE, col.lim = c(-2, 2) * 4)

## 0.5~0.6
corrplot(abs(M)/10+0.5, col = COL1('Greens', 10))
corrplot(abs(M)/10+0.5, is.corr = FALSE, col.lim = c(0.5, 0.6), col = COL1('YlGn', 10))


## visualize a  matrix in [-100, 100]
ran = round(matrix(runif(225, -100, 100), 15))
corrplot(ran, is.corr = FALSE)
corrplot(ran, is.corr = FALSE, col.lim = c(-100, 100))

## visualize a matrix in [100, 300]
ran2 = ran + 200

# bad color, not suitable for a matrix in [100, 300]
corrplot(ran2, is.corr = FALSE, col.lim = c(100, 300), col = COL2(, 100))

# good color
corrplot(ran2, is.corr = FALSE, col.lim = c(100, 300), col = COL1(, 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])

testRes = cor.mtest(mtcars, conf.level = 0.95)

## specialized the insignificant value according to the significant level
corrplot(M, p.mat = testRes$p, sig.level = 0.05, order = 'hclust', addrect = 2)

## leave blank on no significant coefficient
corrplot(M, p.mat = testRes$p, method = 'circle', type = 'lower', insig ='blank',
         addCoef.col ='black', number.cex = 0.8, order = 'AOE', diag = FALSE)

## add p-values on no significant coefficients
corrplot(M, p.mat = testRes$p, insig = 'p-value')

## add all p-values
corrplot(M, p.mat = testRes$p, insig = 'p-value', sig.level = -1)

## add significant level stars
corrplot(M, p.mat = testRes$p, method = 'color', diag = FALSE, type = 'upper',
         sig.level = c(0.001, 0.01, 0.05), pch.cex = 0.9,
         insig = 'label_sig', pch.col = 'grey20', order = 'AOE')

## add significant level stars and cluster rectangles
corrplot(M, p.mat = testRes$p, tl.pos = 'd', order = 'hclust', addrect = 2,
         insig = 'label_sig', sig.level = c(0.001, 0.01, 0.05),
         pch.cex = 0.9, pch.col = 'grey20')

# Visualize confidence interval
corrplot(M, lowCI = testRes$lowCI, uppCI = testRes$uppCI, order = 'hclust',
         tl.pos = 'd', rect.col = 'navy', plotC = 'rect', cl.pos = 'n')

# Visualize confidence interval and cross the significant coefficients
corrplot(M, p.mat = testRes$p, lowCI = testRes$lowCI, uppCI = testRes$uppCI,
         addrect = 3, rect.col = 'navy', plotC = 'rect', cl.pos = 'n')



res1 = cor.mtest(mtcars, conf.level = 0.95)
res2 = cor.mtest(mtcars, conf.level = 0.99)


## plot confidence interval(0.95), 'circle' method
corrplot(M, low = res1$uppCI, upp = res1$uppCI,
         plotCI = 'circle', addg = 'grey20', cl.pos = 'n')
corrplot(M, p.mat = res1$p, low = res1$lowCI, upp = res1$uppCI,
         plotCI = 'circle', addg = 'grey20', cl.pos = 'n')
corrplot(M, low = res1$lowCI, upp = res1$uppCI,
         col = c('white', 'black'), bg = 'gold2', order = 'AOE',
         plotCI = 'circle', cl.pos = 'n', pch.col = 'red')
corrplot(M, p.mat = res1$p, low = res1$lowCI, upp = res1$uppCI,
         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$lowCI, upp = res1$uppCI,
         col = c('white', 'black'), bg = 'gold2', order = 'AOE',
         plotCI = 'square', addg = NULL, cl.pos = 'n')
corrplot(M, p.mat = res1$p, low = res1$lowCI, upp = res1$uppCI,
         col = c('white', 'black'), bg = 'gold2', order = 'AOE', pch.col = 'red',
         plotCI = 'square', addg = NULL, cl.pos = 'n')

## plot confidence interval0.95, 0.95, 0.99, 'rect' method
corrplot(M, low = res1$lowCI, upp = res1$uppCI, order = 'hclust',
         rect.col = 'navy', plotCI = 'rect', cl.pos = 'n')
corrplot(M, p.mat = res1$p, low = res1$lowCI, upp = res1$uppCI,
         order = 'hclust', pch.col = 'red', sig.level = 0.05, addrect = 3,
         rect.col = 'navy', plotCI = 'rect', cl.pos = 'n')
corrplot(M, p.mat = res2$p, low = res2$lowCI, upp = res2$uppCI,
         order = 'hclust', pch.col = 'red', sig.level = 0.01, addrect = 3,
         rect.col = 'navy', plotCI = '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, conf.level = 1 - i)
  corrplot(M, p.mat = tmp$p, low = tmp$lowCI, upp = tmp$uppCI, order = 'hclust',
           pch.col = 'red', sig.level = i, plotCI = '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