Description Usage Arguments Details Value Note Author(s) References See Also Examples
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
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | 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,
...
)
|
corr |
The correlation matrix to visualize, must be square if
|
method |
Character, the visualization method of correlation matrix to be
used. Currently, it supports seven methods, named The areas of circles or squares show the absolute value of corresponding
correlation coefficients. Method |
type |
Character, |
col |
Vector, the colors of glyphs. They are distributed uniformly in
|
col.lim |
The limits NOTICE: if you set |
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
|
add |
Logical, if |
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 |
mar |
See |
addgrid.col |
The color of the grid. If |
addCoef.col |
Color of coefficients added on the graph. If |
addCoefasPercent |
Logic, whether translate coefficients into percentage style for spacesaving. |
order |
Character, the ordering method of the correlation matrix.
See function |
hclust.method |
Character, the agglomeration method to be used when
|
addrect |
Integer, the number of rectangles draws on the graph according
to the hierarchical cluster, only valid when |
rect.col |
Color for rectangle border(s), only valid when |
rect.lwd |
Numeric, line width for borders for rectangle border(s), only
valid when |
tl.pos |
Character or logical, position of text labels. If character, it
must be one of |
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 |
tl.srt |
Numeric, for text label string rotation in degrees, see
|
cl.pos |
Character or logical, position of color-legend; If character,
it must be one of |
cl.length |
Integer, the number of number-text in color-legend, passed to
|
cl.cex |
Numeric, cex of number-label in color-legend, passed to
|
cl.ratio |
Numeric, to justify the width of color-legend, 0.1~0.2 is suggested. |
cl.align.text |
Character, |
cl.offset |
Numeric, for number-label in color-legend, see
|
number.cex |
The |
number.font |
the |
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, |
shade.lwd |
Numeric, the line width of shade. |
shade.col |
The color of shade line. |
p.mat |
Matrix of p-value, if |
sig.level |
Significant level, if the p-value in |
insig |
Character, specialized insignificant correlation coefficients,
|
pch |
Add character on the glyphs of insignificant correlation
coefficients(only valid when |
pch.col |
The color of pch (only valid when |
pch.cex |
The cex of pch (only valid when |
plotCI |
Character, method of ploting confidence interval. If
|
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.label.col |
Color used for rendering |
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 |
corrplot
function offers flexible ways to visualize
correlation matrix, lower and upper bound of confidence interval matrix.
(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.
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'
.
Taiyun Wei (weitaiyun@gmail.com)
Viliam Simko (viliam.simko@gmail.com)
Michael Levy (michael.levy@healthcatalyst.com)
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.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 | 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
|
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