Package 'corrplot'

Title: Visualization of a Correlation Matrix
Description: Provides a visual exploratory tool on correlation matrix that supports automatic variable reordering to help detect hidden patterns among variables.
Authors: Taiyun Wei [cre, aut], Viliam Simko [aut], Michael Levy [ctb], Yihui Xie [ctb], Yan Jin [ctb], Jeff Zemla [ctb], Moritz Freidank [ctb], Jun Cai [ctb], Tomas Protivinsky [ctb]
Maintainer: Taiyun Wei <[email protected]>
License: MIT + file LICENSE
Version: 0.94
Built: 2024-09-01 06:18:22 UTC
Source: https://github.com/taiyun/corrplot

Help Index

Visualization of a correlation matrix

Description

The corrplot package is a graphical display of a correlation matrix, confidence interval or general matrix. It also contains some algorithms to do matrix reordering. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout, etc.

Author(s)

Taiyun Wei ([email protected])

Viliam Simko ([email protected])

Maintainer: Taiyun Wei ([email protected])

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

The plotcorr function in the ellipse package and corrgram function in the corrgram package has some similarities.

Get sequential colors

Description

Get sequential colors from palette theme name and n. The color palettes are from RColorBrewer. Sequential colors are suitable for visualize a non-negative or non-positive matrix (e.g. matrix in [0, 20], or [-100, -10], or [100, 500]).

Usage

COL1(
  sequential = c("Oranges", "Purples", "Reds", "Blues", "Greens", "Greys", "OrRd",
    "YlOrRd", "YlOrBr", "YlGn"),
  n = 200
)

Arguments

sequential

Sequential color Palettes
n

the number of colors (>= 1) to be in the palette.

Value

A character vector containing color names

See Also

Function colorRampPalette, package RColorBrewer

Examples

## diverging colors
par(mar = c(0, 0, 0, 0) + 0.1)
plot(0, xlim = c(-0.1, 1), ylim = c(0, 1), type = 'n')

col = c('RdBu', 'BrBG', 'PiYG', 'PRGn', 'PuOr', 'RdYlBu')

for(i in 1:length(col)) {
  colorlegend(COL2(col[i]), -10:10/10, align = 'l', cex = 0.8, xlim = c(0, 1),
              ylim = c(i/length(col)-0.1, i/length(col)), vertical = FALSE)
  text(-0.01, i/length(col)-0.02, col[i], adj = 0.5, pos = 2, cex = 0.8)
}



## sequential colors
par(mar = c(0, 0, 0, 0) + 0.1)
plot(0, xlim = c(-0.1, 1), ylim = c(0, 1), type = 'n')

col = c('Oranges', 'Purples', 'Reds', 'Blues', 'Greens', 'Greys', 'OrRd',
        'YlOrRd', 'YlOrBr', 'YlGn')

for(i in 1:length(col)) {
  colorlegend(COL1(col[i]), 0:10, align = 'l', cex = 0.8, xlim = c(0, 1),
              ylim = c(i/length(col)-0.1, i/length(col)), vertical = FALSE)
  text(-0.01, i/length(col)-0.02, col[i], adj = 0.5, pos = 2)
}



## other examples to show colorlegend function
par(mar = rep(0, 4))
plot(0, xlim = c(0, 6), ylim = c(-0.5, 1.2), type = 'n')

colorlegend(rainbow(100), 0:9)

colorlegend(heat.colors(100), LETTERS[1:12], xlim = c(1, 2))

colorlegend(terrain.colors(100), 0:9, ratio.colbar = 0.6,
            lim.segment = c(0, 0.6), xlim = c(2, 3), align = 'l')

colorlegend(topo.colors(100), 0:9, lim.segment = c(0, 0.6),
            xlim = c(3, 4), align = 'l', offset = 0)

colorlegend(cm.colors(100), 1:5, xlim = c(4, 5))

colorlegend(sample(rainbow(12)), labels = LETTERS[1:12],
            at = seq(0.05, 0.95, len = 12), xlim = c(5, 6), align = 'r')

colorlegend(colbar = grey(1:100 / 100), 1:10, col = 'red', align = 'l',
            xlim = c(0, 6), ylim = c(-0.5, -0.1), vertical = FALSE)

colorlegend(sample(rainbow(12)),
            labels = LETTERS[1:12], at = seq(0.05, 0.95, len = 12),
            xlim = c(0, 6), ylim = c(1.1, 1.2), vertical = FALSE)

Get diverging colors

Description

Get diverging colors from palette theme name and n. The color palettes are from RColorBrewer, but with the middle color changing to '#FFFFFF'(white), thus we can visualize element 0 with white color. Diverging colors are suitable for visualize a matrix which elements are partly positive and partly negative (e.g. correlation matrix in [-1, 1], or [-20, 100]).

Usage

COL2(diverging = c("RdBu", "BrBG", "PiYG", "PRGn", "PuOr", "RdYlBu"), n = 200)

Arguments

diverging

Diverging color Palettes
n

the number of colors (>= 1) to be in the palette.

Value

A character vector containing color names

See Also

Function colorRampPalette, package RColorBrewer

Examples

## diverging colors
par(mar = c(0, 0, 0, 0) + 0.1)
plot(0, xlim = c(-0.1, 1), ylim = c(0, 1), type = 'n')

col = c('RdBu', 'BrBG', 'PiYG', 'PRGn', 'PuOr', 'RdYlBu')

for(i in 1:length(col)) {
  colorlegend(COL2(col[i]), -10:10/10, align = 'l', cex = 0.8, xlim = c(0, 1),
              ylim = c(i/length(col)-0.1, i/length(col)), vertical = FALSE)
  text(-0.01, i/length(col)-0.02, col[i], adj = 0.5, pos = 2, cex = 0.8)
}



## sequential colors
par(mar = c(0, 0, 0, 0) + 0.1)
plot(0, xlim = c(-0.1, 1), ylim = c(0, 1), type = 'n')

col = c('Oranges', 'Purples', 'Reds', 'Blues', 'Greens', 'Greys', 'OrRd',
        'YlOrRd', 'YlOrBr', 'YlGn')

for(i in 1:length(col)) {
  colorlegend(COL1(col[i]), 0:10, align = 'l', cex = 0.8, xlim = c(0, 1),
              ylim = c(i/length(col)-0.1, i/length(col)), vertical = FALSE)
  text(-0.01, i/length(col)-0.02, col[i], adj = 0.5, pos = 2)
}



## other examples to show colorlegend function
par(mar = rep(0, 4))
plot(0, xlim = c(0, 6), ylim = c(-0.5, 1.2), type = 'n')

colorlegend(rainbow(100), 0:9)

colorlegend(heat.colors(100), LETTERS[1:12], xlim = c(1, 2))

colorlegend(terrain.colors(100), 0:9, ratio.colbar = 0.6,
            lim.segment = c(0, 0.6), xlim = c(2, 3), align = 'l')

colorlegend(topo.colors(100), 0:9, lim.segment = c(0, 0.6),
            xlim = c(3, 4), align = 'l', offset = 0)

colorlegend(cm.colors(100), 1:5, xlim = c(4, 5))

colorlegend(sample(rainbow(12)), labels = LETTERS[1:12],
            at = seq(0.05, 0.95, len = 12), xlim = c(5, 6), align = 'r')

colorlegend(colbar = grey(1:100 / 100), 1:10, col = 'red', align = 'l',
            xlim = c(0, 6), ylim = c(-0.5, -0.1), vertical = FALSE)

colorlegend(sample(rainbow(12)),
            labels = LETTERS[1:12], at = seq(0.05, 0.95, len = 12),
            xlim = c(0, 6), ylim = c(1.1, 1.2), vertical = FALSE)

Draw color legend.

Description

Draw color legend.

Usage

colorlegend(
  colbar,
  labels,
  at = NULL,
  xlim = c(0, 1),
  ylim = c(0, 1),
  vertical = TRUE,
  ratio.colbar = 0.4,
  lim.segment = "auto",
  align = c("c", "l", "r"),
  addlabels = TRUE,
  ...
)

Arguments

colbar

Vector, color of colbar.
labels

Vector, numeric or character to be written.
at

Numeric vector (quantile), the position to put labels. See examples for details.
xlim

See in plot
ylim

See in plot
vertical

Logical, whether the colorlegend is vertical or horizon.
ratio.colbar

The width ratio of colorbar to the total colorlegend (including colorbar, segments and labels).
lim.segment

Vector (quantile) of length 2, the elements should be in [0,1], giving segments coordinates ranges. If the value is NULL or 'auto', then the ranges are derived automatically.
align

Character, alignment type of labels, 'l' means left, 'c' means center and 'r' right. Only valid when vertical is TRUE.
addlabels

Logical, whether add text label or not.
...

Additional arguments, passed to plot

Author(s)

Taiyun Wei

Examples

## diverging colors
par(mar = c(0, 0, 0, 0) + 0.1)
plot(0, xlim = c(-0.1, 1), ylim = c(0, 1), type = 'n')

col = c('RdBu', 'BrBG', 'PiYG', 'PRGn', 'PuOr', 'RdYlBu')

for(i in 1:length(col)) {
  colorlegend(COL2(col[i]), -10:10/10, align = 'l', cex = 0.8, xlim = c(0, 1),
              ylim = c(i/length(col)-0.1, i/length(col)), vertical = FALSE)
  text(-0.01, i/length(col)-0.02, col[i], adj = 0.5, pos = 2, cex = 0.8)
}



## sequential colors
par(mar = c(0, 0, 0, 0) + 0.1)
plot(0, xlim = c(-0.1, 1), ylim = c(0, 1), type = 'n')

col = c('Oranges', 'Purples', 'Reds', 'Blues', 'Greens', 'Greys', 'OrRd',
        'YlOrRd', 'YlOrBr', 'YlGn')

for(i in 1:length(col)) {
  colorlegend(COL1(col[i]), 0:10, align = 'l', cex = 0.8, xlim = c(0, 1),
              ylim = c(i/length(col)-0.1, i/length(col)), vertical = FALSE)
  text(-0.01, i/length(col)-0.02, col[i], adj = 0.5, pos = 2)
}



## other examples to show colorlegend function
par(mar = rep(0, 4))
plot(0, xlim = c(0, 6), ylim = c(-0.5, 1.2), type = 'n')

colorlegend(rainbow(100), 0:9)

colorlegend(heat.colors(100), LETTERS[1:12], xlim = c(1, 2))

colorlegend(terrain.colors(100), 0:9, ratio.colbar = 0.6,
            lim.segment = c(0, 0.6), xlim = c(2, 3), align = 'l')

colorlegend(topo.colors(100), 0:9, lim.segment = c(0, 0.6),
            xlim = c(3, 4), align = 'l', offset = 0)

colorlegend(cm.colors(100), 1:5, xlim = c(4, 5))

colorlegend(sample(rainbow(12)), labels = LETTERS[1:12],
            at = seq(0.05, 0.95, len = 12), xlim = c(5, 6), align = 'r')

colorlegend(colbar = grey(1:100 / 100), 1:10, col = 'red', align = 'l',
            xlim = c(0, 6), ylim = c(-0.5, -0.1), vertical = FALSE)

colorlegend(sample(rainbow(12)),
            labels = LETTERS[1:12], at = seq(0.05, 0.95, len = 12),
            xlim = c(0, 6), ylim = c(1.1, 1.2), vertical = FALSE)

Significance test which produces p-values and confidence intervals for each pair of input features.

Description

Significance test which produces p-values and confidence intervals for each pair of input features.

Usage

cor.mtest(mat, ...)

Arguments

mat

Input matrix of size NxF, with N rows that represent samples and F columns that represent features.
...

Additional arguments passed to function cor.test, e.g. conf.level = 0.95.

Value

Return a list containing:

p

Square matrix of size FxF with p-values as cells
lowCI

Square matrix of size FxF, each cell represents the lower part of a confidence interval
uppCI

Square matrix of size FxF, each cell represents the upper part of a confidence interval

See Also

Function cor.test

Reorder a correlation matrix.

Description

Draw rectangle(s) around the chart of corrrlation matrix based on the number of each cluster's members.

Usage

corrMatOrder(
  corr,
  order = c("AOE", "FPC", "hclust", "alphabet"),
  hclust.method = c("complete", "ward", "ward.D", "ward.D2", "single", "average",
    "mcquitty", "median", "centroid")
)

Arguments

corr

Correlation matrix to reorder.
order

Character, the ordering method for the correlation matrix.

  • 'AOE' for the angular order of the eigenvectors. It is calculated from the order of the angles, aia_i:

    ai=arctan(ei2/ei1),ifei1>0a_i = arctan (e_{i2} / e_{i1}), if e_{i1} > 0

    ai=arctan(ei2/ei1)+π,otherwise.a_i = arctan (e_{i2} / e_{i1}) + \pi, otherwise.

    where e1e_1 and e2e_2 are the largest two eigenvalues of matrix corr. See Michael Friendly (2002) for details.

  • 'FPC' for the first principal component order.

  • 'hclust' for hierarchical clustering order.

  • 'alphabet' for alphabetical order.

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'.

Value

Returns a single permutation vector.

Author(s)

Taiyun Wei

See Also

Package seriation offers more methods to reorder matrices, such as ARSA, BBURCG, BBWRCG, MDS, TSP, Chen and so forth.

Examples

M = cor(mtcars)

(order.AOE = corrMatOrder(M, order = 'AOE'))
(order.FPC = corrMatOrder(M, order = 'FPC'))
(order.hc = corrMatOrder(M, order = 'hclust'))
(order.hc2 = corrMatOrder(M, order = 'hclust', hclust.method = 'ward.D'))

M.AOE = M[order.AOE, order.AOE]
M.FPC = M[order.FPC, order.FPC]
M.hc  = M[order.hc, order.hc]
M.hc2 = M[order.hc2, order.hc2]



par(ask = TRUE)
corrplot(M)
corrplot(M.AOE)
corrplot(M.FPC)
corrplot(M.hc)

corrplot(M.hc)
corrRect.hclust(corr = M.hc, k = 2)

corrplot(M.hc)
corrRect.hclust(corr = M.hc, k = 3)

corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 2, method = 'ward.D')

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,
  is.corr = TRUE,
  bg = "white",
  title = "",
  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",
  transKeepSign = TRUE,
  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,
  ...
)

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.
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.
bg

The background color.
title

Character, title of the graph.
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.
transKeepSign

Logical, whether or not to keep matrix values' sign when transforming non-corr matrix for plotting. Only valid when is.corr = FALSE. The default value is TRUE.

NOTE: If FALSE,the non-corr matrix will be
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.

Details

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

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.

Note

Cairo and cairoDevice packages is strongly recommended to produce high-quality PNG, JPEG, TIFF bitmap files, especially for that method circle, ellipse.

Row- and column names of the input matrix are used as labels rendered in the corrplot. Plothmath expressions will be used if the name is prefixed by one of the following characters: :, = or $. For example ':alpha + beta'.

Author(s)

Taiyun Wei ([email protected])

Viliam Simko ([email protected])

Michael Levy ([email protected])

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

Using mixed methods to visualize a correlation matrix.

Description

Using mixed methods to visualize a correlation matrix.

Usage

corrplot.mixed(
  corr,
  lower = "number",
  upper = "circle",
  tl.pos = c("d", "lt", "n"),
  diag = c("n", "l", "u"),
  bg = "white",
  addgrid.col = "grey",
  lower.col = NULL,
  upper.col = NULL,
  plotCI = c("n", "square", "circle", "rect"),
  mar = c(0, 0, 0, 0),
  ...
)

Arguments

corr

Matrix, the correlation matrix to visualize.
lower

Character, the visualization method for the lower triangular correlation matrix.
upper

Character, the visualization method for the upper triangular correlation matrix.
tl.pos

Character, 'lt', 'd' or 'n', giving position of text labels, 'lt' means left and top, 'd' means diagonal. If 'n', add no textlabel.
diag

Character, for specifying the glyph on the principal diagonal. It is one of 'n' (default, draw nothing), 'l' (draw the glyphs of lower triangular) or 'u' (draw the glyphs of upper triangular).
bg

The background color.
addgrid.col

See the addgrid.col parameter in the function corrplot
lower.col

Passed as col parameter to the lower matrix.
upper.col

Passed as col parameter to the upper matrix.
plotCI

See the plotCI parameter in the function corrplot
mar

See par.
...

Additional arguments for corrplot's wrappers

Author(s)

Taiyun Wei

Examples

M = cor(mtcars)
ord = corrMatOrder(M, order = 'AOE')
M2 = M[ord, ord]


corrplot.mixed(M2)
corrplot.mixed(M2, lower = 'ellipse', upper = 'circle')
corrplot.mixed(M2, lower = 'square', upper = 'circle')
corrplot.mixed(M2, lower = 'shade', upper = 'circle')
corrplot.mixed(M2, tl.pos = 'lt')
corrplot.mixed(M2, tl.pos = 'lt', diag = 'u')
corrplot.mixed(M2, tl.pos = 'lt', diag = 'l')
corrplot.mixed(M2, tl.pos = 'n')

Draw rectangle(s) on the correlation matrix graph.

Description

Draw rectangle(s) after the correlation matrix plotted. SUGGESTION: It's more convenient to draw rectangle(s) by using pipe operator '|>' since R 4.1.0.

Usage

corrRect(
  corrRes = NULL,
  index = NULL,
  name = NULL,
  namesMat = NULL,
  col = "black",
  lwd = 2,
  ...
)

Arguments

corrRes

List of the corrplot() returns.
index

Vector, variable index of diag rect c(Rect1from, Rect2from, Rect3from, ..., RectNto) on the correlation matrix graph. It works when the colnames are the same as rownames, or both of them is NULL. It needs corrRes inputted.
name

Vector, variable name of diag rect c(Rect1from, Rect2from, Rect3from, ..., RectNto) on the correlation matrix graph. OIt works when the colnames are the same as rownames. It needs corrRes inputted.
namesMat

4-length character vector or 4-columns character matrix, represents the names of xleft, ybottom, xright, ytop correspondingly. It needs corrRes inputted.
col

Color of rectangles.
lwd

Line width of rectangles.
...

Additional arguments passing to function rect().

Details

corrRect needs one of index, name and namesMat inputted. While corrRect.hclust can get the members in each cluster based on hierarchical clustering (hclust).

Value

(Invisibly) returns input parameter corrRes, usually list(corr, corrTrans, arg).

Author(s)

Taiyun Wei

Examples

data(mtcars)
M = cor(mtcars)

r = rbind(c('gear', 'wt', 'qsec', 'carb'),
        c('wt', 'gear', 'carb', 'qsec'))
corrplot(M, order = 'AOE') -> p
corrRect(p, namesMat = r)

#  same as using pipe operator `|>` if R version >= 4.1.0:
#  corrplot(M, order = 'AOE') |> corrRect(namesMat = r)



r = c('gear', 'carb', 'qsec', 'wt')
corrplot(M, order = 'AOE', type='lower') -> p
corrRect(p, namesMat = r)

# same as using pipe operator `|>` if R version >= 4.1.0:
# corrplot(M, order = 'AOE', type='lower') |> corrRect(namesMat = r)



corrplot(M, order = 'hclust', type = 'upper') -> p
corrRect(p, index = c(1, 6, 11))

#  same as using pipe operator `|>` if R version >= 4.1.0:
#  corrplot(M, order = 'AOE', type='lower') |> corrRect(index = c(1, 6, 11))



corrplot(M, order = 'hclust') -> p
corrRect(p, name = c('carb', 'qsec', 'gear'))

#  same as using pipe operator `|>` if R version >= 4.1.0:
#  corrplot(M, order = 'hclust') |> corrRect(name = c('carb', 'qsec', 'gear'))




(order.hc = corrMatOrder(M, order = 'hclust'))
(order.hc2 = corrMatOrder(M, order = 'hclust', hclust.method = 'ward.D'))
M.hc  = M[order.hc, order.hc]
M.hc2 = M[order.hc2, order.hc2]

par(ask = TRUE)

# same as: corrplot(M, order = 'hclust', addrect = 2)
corrplot(M.hc)
corrRect.hclust(corr = M.hc, k = 2)

# same as: corrplot(M, order = 'hclust', addrect = 3)
corrplot(M.hc)
corrRect.hclust(corr = M.hc, k = 3)

# same as: corrplot(M, order = 'hclust', hclust.method = 'ward.D', addrect = 2)
corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 2, method = 'ward.D')

# same as: corrplot(M, order = 'hclust', hclust.method = 'ward.D', addrect = 3)
corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 3, method = 'ward.D')

# same as: corrplot(M, order = 'hclust', hclust.method = 'ward.D', addrect = 4)
corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 4, method = 'ward.D')

Draw rectangles on the correlation matrix graph.

Description

Draw rectangles on the correlation matrix graph based on hierarchical cluster (hclust).

Usage

corrRect.hclust(
  corr,
  k = 2,
  col = "black",
  lwd = 2,
  method = c("complete", "ward", "ward.D", "ward.D2", "single", "average", "mcquitty",
    "median", "centroid")
)

Arguments

corr

Correlation matrix for function corrRect.hclust. It use 1-corr as dist in hierarchical clustering (hclust).
k

Integer, the number of rectangles drawn on the graph according to the hierarchical cluster, for function corrRect.hclust.
col

Color of rectangles.
lwd

Line width of rectangles.
method

Character, the agglomeration method to be used for hierarchical clustering (hclust). This should be (an unambiguous abbreviation of) one of 'ward', 'ward.D', 'ward.D2', 'single', 'complete', 'average', 'mcquitty', 'median' or 'centroid'.

Author(s)

Taiyun Wei

Examples

data(mtcars)
M = cor(mtcars)
corrplot(M, order = 'FPC') -> p
corrRect(p, index = c(1, 6, 11))

if(getRversion() >= '4.1.0') {
  corrplot(M, order = 'FPC') |> corrRect(index = c(1, 6, 11))
}

(order.hc = corrMatOrder(M, order = 'hclust'))
(order.hc2 = corrMatOrder(M, order = 'hclust', hclust.method = 'ward.D2'))
M.hc  = M[order.hc, order.hc]
M.hc2 = M[order.hc2, order.hc2]

par(ask = TRUE)

# same as: corrplot(M, order = 'hclust', addrect = 2)
corrplot(M.hc)
corrRect.hclust(corr = M.hc, k = 2)

# same as: corrplot(M, order = 'hclust', addrect = 3)
corrplot(M.hc)
corrRect.hclust(corr = M.hc, k = 3)

# same as: corrplot(M, order = 'hclust', hclust.method = 'ward.D2', addrect = 2)
corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 2, method = 'ward.D2')

# same as: corrplot(M, order = 'hclust', hclust.method = 'ward.D2', addrect = 3)
corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 3, method = 'ward.D2')

# same as: corrplot(M, order = 'hclust', hclust.method = 'ward.D2', addrect = 4)
corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 4, method = 'ward.D2')