correlation: Correlation matrices.
In SciViews: SciViews - Main package
Description
Usage
Arguments
Value
Author(s)
See Also
Examples
Description
Compute the correlation matrix between two variables, or more (between all
columns of a matrix or data frame).
Usage
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correlation(x, ...)
Correlation(x, ...)
## S3 method for class 'formula'
correlation(formula, data = NULL, subset, na.action, ...)
## Default S3 method:
correlation(x, y = NULL, use = "everything",
method = c("pearson", "kendall", "spearman"), ...)
is.Correlation(x)
is.correlation(x)
as.Correlation(x)
as.correlation(x)
## S3 method for class 'Correlation'
print(x, digits = 3, cutoff = 0, ...)
## S3 method for class 'Correlation'
summary(object, cutpoints = c(0.3, 0.6, 0.8, 0.9, 0.95),
symbols = c(" ", ".", ",", "+", "*", "B"), ...)
## S3 method for class 'summary.Correlation'
print(x, ...)
## S3 method for class 'Correlation'
plot(x, y = NULL, outline = TRUE, cutpoints = c(0.3,
0.6, 0.8, 0.9, 0.95), palette = rwb.colors, col = NULL, numbers = TRUE,
digits = 2, type = c("full", "lower", "upper"), diag = (type == "full"),
cex.lab = par("cex.lab"), cex = 0.75 * par("cex"), ...)
## S3 method for class 'Correlation'
lines(x, choices = 1L:2L, col = par("col"), lty = 2,
ar.length = 0.1, pos = NULL, cex = par("cex"), labels = rownames(x),
...)
Arguments
x
A numeric vector, matrix or data frame (or any object for
is.Correlation(), as.Correlation().
...
Further arguments passed to functions.
formula
A formula with no response variable, referring only to numeric
variables.
data
An optional data frame (or similar: see model.frame())
containing the variables in the formula formula. By default the variables
are taken from environment(formula).
subset
An optional vector used to select rows (observations) of the
data matrix x.
na.action
A function which indicates what should happen when the data
contain NAs. The default is set by the na.action = setting of options()
and na.fail() is used if that is not set. The 'factory-fresh' default is
na.omit().
y
NULL (default), or a vector, matrix or data frame with compatible
dimensions to x for Correlation(). The default is equivalent to x = y,
but more efficient.
use
An optional character string giving a method for computing
correlations in the presence of missing values. This must be (an abbreviation
of) one of the strings "everything", "all.obs", "complete.obs",
"na.or.complete", or "pairwise.complete.obs".
method
A character string indicating which correlation coefficient is
to be computed. One of "pearson" (default), "kendall", or "spearman",
can be abbreviated.
digits
Digits to print after the decimal separator.
cutoff
Correlation coefficients lower than this (in absolute value)
are suppressed.
object
A 'Correlation' object.
cutpoints
The cut points to use for categories. Specify only positive
values (absolute value of correlation coefficients are summarized, or
negative equivalents are automatically computed for the graph. Do not include
0 or 1 in the cutpoints).
symbols
The symbols to use to summarize the correlation matrix.
outline
Do we draw the outline of the ellipse?
palette
A function that can produce a palette of colors.
col
Color of the ellipse. If NULL (default), the colors will be
computed using cutpoints = and palette =.
numbers
Do we print correlation values in the center of the ellipses?
type
Do we plot a complete matrix, or only lower or upper triangle?
diag
Do we plot items on the diagonal? They have always a correlation
of one.
cex.lab
The expansion factor for labels.
cex
The expansion factor for text.
choices
The items to select
lty
The line type to draw.
ar.length
The length of the arrow head.
pos
The position relative to arrows.
labels
The label to draw nead arrows.
Value
Correlation() and as.Correlation()`` create a 'Correlation' object, whileis.Correlation()“ tests for it.
There are print() and summary() methods for the 'Correlation' object
that differ in the symbolic encoding of the correlations in summary(),
using5 symnum()], which makes large correlation matrices more readable.
The method plot() returns nothing, but it draws ellipses on a graph that
represent the correlation matrix visually. This is essentially the
plotcorr() function from package ellipse, with slightly different
default arguments and with default cutpoints equivalent to those used in
the summary() method.
Author(s)
Philippe Grosjean phgrosjean@sciviews.org, wrapping code in package
ellipse, function plotcorr() for the plot.Correlation() method.
See Also
cov(), cov2cor(), cov.wt(), symnum(), plotcorr() and look
at panel_cor()
Examples
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# This is a simple correlation coefficient
cor(rnorm(10), runif(10))
Correlation(rnorm(10), runif(10))
# 'Correlation' objects allow better inspection of the correlation matrices
# than the output of default R cor() function
(longley.cor <- Correlation(longley))
summary(longley.cor) # Synthetic view of the correlation matrix
plot(longley.cor) # Graphical representation
# Use of the formula interface
(mtcars.cor <- Correlation(~ mpg + cyl + disp + hp, data = mtcars,
method = "spearman", na.action = "na.omit"))
mtcars.cor2 <- Correlation(mtcars, method = "spearman")
print(mtcars.cor2, cutoff = 0.6)
summary(mtcars.cor2)
plot(mtcars.cor2, type = "lower")
mtcars.cor2["mpg", "cyl"] # Extract a correlation from the correlation matrix
Example output
[1] 0.1975934
Matrix of Pearson's product-moment correlation:
(calculation uses everything)
x y
x 1.000 -0.149
y -0.149 1.000
Matrix of Pearson's product-moment correlation:
(calculation uses everything)
GNP.deflator GNP Unemployed Armed.Forces Population Year
GNP.deflator 1.000 0.992 0.621 0.465 0.979 0.991
GNP 0.992 1.000 0.604 0.446 0.991 0.995
Unemployed 0.621 0.604 1.000 -0.177 0.687 0.668
Armed.Forces 0.465 0.446 -0.177 1.000 0.364 0.417
Population 0.979 0.991 0.687 0.364 1.000 0.994
Year 0.991 0.995 0.668 0.417 0.994 1.000
Employed 0.971 0.984 0.502 0.457 0.960 0.971
Employed
GNP.deflator 0.971
GNP 0.984
Unemployed 0.502
Armed.Forces 0.457
Population 0.960
Year 0.971
Employed 1.000
Matrix of Pearson's product-moment correlation:
(calculation uses everything)
GNP. GNP U A P Y E
GNP.deflator 1
GNP B 1
Unemployed , , 1
Armed.Forces . . 1
Population B B , . 1
Year B B , . B 1
Employed B B . . B B 1
attr(,"legend")
[1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1
Matrix of Spearman's rank correlation rho:
(missing values are managed with na.omit)
mpg cyl disp hp
mpg 1.000 -0.911 -0.909 -0.895
cyl -0.911 1.000 0.928 0.902
disp -0.909 0.928 1.000 0.851
hp -0.895 0.902 0.851 1.000
Matrix of Spearman's rank correlation rho:
(calculation uses everything)
mpg cyl disp hp drat wt qsec vs am gear
mpg 1.000 -0.911 -0.909 -0.895 0.651 -0.886 0.707
cyl -0.911 1.000 0.928 0.902 -0.679 0.858 -0.814
disp -0.909 0.928 1.000 0.851 -0.684 0.898 -0.724 -0.624
hp -0.895 0.902 0.851 1.000 0.775 -0.667 -0.752
drat 0.651 -0.679 -0.684 1.000 -0.750 0.687 0.745
wt -0.886 0.858 0.898 0.775 -0.750 1.000 -0.738 -0.676
qsec -0.667 1.000 0.792
vs 0.707 -0.814 -0.724 -0.752 0.792 1.000
am -0.624 0.687 -0.738 1.000 0.808
gear 0.745 -0.676 0.808 1.000
carb -0.657 0.733 -0.659 -0.634
carb
mpg -0.657
cyl
disp
hp 0.733
drat
wt
qsec -0.659
vs -0.634
am
gear
carb 1.000
Matrix of Spearman's rank correlation rho:
(calculation uses everything)
m cy ds h dr w q v a g cr
mpg 1
cyl * 1
disp * * 1
hp + * + 1
drat , , , . 1
wt + + + , , 1
qsec . . . , 1
vs , + , , . . , 1
am . . , . , , 1
gear . . . . , , + 1
carb , . . , . , , 1
attr(,"legend")
[1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1
[1] -0.9108013
SciViews documentation built on Nov. 17, 2019, 1:06 a.m.
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Description Usage Arguments Value Author(s) See Also Examples
Description
Compute the correlation matrix between two variables, or more (between all columns of a matrix or data frame).
Usage
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 | correlation(x, ...)
Correlation(x, ...)
## S3 method for class 'formula'
correlation(formula, data = NULL, subset, na.action, ...)
## Default S3 method:
correlation(x, y = NULL, use = "everything",
method = c("pearson", "kendall", "spearman"), ...)
is.Correlation(x)
is.correlation(x)
as.Correlation(x)
as.correlation(x)
## S3 method for class 'Correlation'
print(x, digits = 3, cutoff = 0, ...)
## S3 method for class 'Correlation'
summary(object, cutpoints = c(0.3, 0.6, 0.8, 0.9, 0.95),
symbols = c(" ", ".", ",", "+", "*", "B"), ...)
## S3 method for class 'summary.Correlation'
print(x, ...)
## S3 method for class 'Correlation'
plot(x, y = NULL, outline = TRUE, cutpoints = c(0.3,
0.6, 0.8, 0.9, 0.95), palette = rwb.colors, col = NULL, numbers = TRUE,
digits = 2, type = c("full", "lower", "upper"), diag = (type == "full"),
cex.lab = par("cex.lab"), cex = 0.75 * par("cex"), ...)
## S3 method for class 'Correlation'
lines(x, choices = 1L:2L, col = par("col"), lty = 2,
ar.length = 0.1, pos = NULL, cex = par("cex"), labels = rownames(x),
...)
|
Arguments
x |
A numeric vector, matrix or data frame (or any object for
|
... |
Further arguments passed to functions. |
formula |
A formula with no response variable, referring only to numeric variables. |
data |
An optional data frame (or similar: see |
subset |
An optional vector used to select rows (observations) of the
data matrix |
na.action |
A function which indicates what should happen when the data
contain |
y |
|
use |
An optional character string giving a method for computing
correlations in the presence of missing values. This must be (an abbreviation
of) one of the strings |
method |
A character string indicating which correlation coefficient is
to be computed. One of |
digits |
Digits to print after the decimal separator. |
cutoff |
Correlation coefficients lower than this (in absolute value) are suppressed. |
object |
A 'Correlation' object. |
cutpoints |
The cut points to use for categories. Specify only positive values (absolute value of correlation coefficients are summarized, or negative equivalents are automatically computed for the graph. Do not include 0 or 1 in the cutpoints). |
symbols |
The symbols to use to summarize the correlation matrix. |
outline |
Do we draw the outline of the ellipse? |
palette |
A function that can produce a palette of colors. |
col |
Color of the ellipse. If |
numbers |
Do we print correlation values in the center of the ellipses? |
type |
Do we plot a complete matrix, or only lower or upper triangle? |
diag |
Do we plot items on the diagonal? They have always a correlation of one. |
cex.lab |
The expansion factor for labels. |
cex |
The expansion factor for text. |
choices |
The items to select |
lty |
The line type to draw. |
ar.length |
The length of the arrow head. |
pos |
The position relative to arrows. |
labels |
The label to draw nead arrows. |
Value
Correlation() and as.Correlation()`` create a 'Correlation' object, whileis.Correlation()“ tests for it.
There are print() and summary() methods for the 'Correlation' object
that differ in the symbolic encoding of the correlations in summary(),
using5 symnum()], which makes large correlation matrices more readable.
The method plot() returns nothing, but it draws ellipses on a graph that
represent the correlation matrix visually. This is essentially the
plotcorr() function from package ellipse, with slightly different
default arguments and with default cutpoints equivalent to those used in
the summary() method.
Author(s)
Philippe Grosjean phgrosjean@sciviews.org, wrapping code in package
ellipse, function plotcorr() for the plot.Correlation() method.
See Also
cov(), cov2cor(), cov.wt(), symnum(), plotcorr() and look
at panel_cor()
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # This is a simple correlation coefficient
cor(rnorm(10), runif(10))
Correlation(rnorm(10), runif(10))
# 'Correlation' objects allow better inspection of the correlation matrices
# than the output of default R cor() function
(longley.cor <- Correlation(longley))
summary(longley.cor) # Synthetic view of the correlation matrix
plot(longley.cor) # Graphical representation
# Use of the formula interface
(mtcars.cor <- Correlation(~ mpg + cyl + disp + hp, data = mtcars,
method = "spearman", na.action = "na.omit"))
mtcars.cor2 <- Correlation(mtcars, method = "spearman")
print(mtcars.cor2, cutoff = 0.6)
summary(mtcars.cor2)
plot(mtcars.cor2, type = "lower")
mtcars.cor2["mpg", "cyl"] # Extract a correlation from the correlation matrix
|
Example output
[1] 0.1975934
Matrix of Pearson's product-moment correlation:
(calculation uses everything)
x y
x 1.000 -0.149
y -0.149 1.000
Matrix of Pearson's product-moment correlation:
(calculation uses everything)
GNP.deflator GNP Unemployed Armed.Forces Population Year
GNP.deflator 1.000 0.992 0.621 0.465 0.979 0.991
GNP 0.992 1.000 0.604 0.446 0.991 0.995
Unemployed 0.621 0.604 1.000 -0.177 0.687 0.668
Armed.Forces 0.465 0.446 -0.177 1.000 0.364 0.417
Population 0.979 0.991 0.687 0.364 1.000 0.994
Year 0.991 0.995 0.668 0.417 0.994 1.000
Employed 0.971 0.984 0.502 0.457 0.960 0.971
Employed
GNP.deflator 0.971
GNP 0.984
Unemployed 0.502
Armed.Forces 0.457
Population 0.960
Year 0.971
Employed 1.000
Matrix of Pearson's product-moment correlation:
(calculation uses everything)
GNP. GNP U A P Y E
GNP.deflator 1
GNP B 1
Unemployed , , 1
Armed.Forces . . 1
Population B B , . 1
Year B B , . B 1
Employed B B . . B B 1
attr(,"legend")
[1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1
Matrix of Spearman's rank correlation rho:
(missing values are managed with na.omit)
mpg cyl disp hp
mpg 1.000 -0.911 -0.909 -0.895
cyl -0.911 1.000 0.928 0.902
disp -0.909 0.928 1.000 0.851
hp -0.895 0.902 0.851 1.000
Matrix of Spearman's rank correlation rho:
(calculation uses everything)
mpg cyl disp hp drat wt qsec vs am gear
mpg 1.000 -0.911 -0.909 -0.895 0.651 -0.886 0.707
cyl -0.911 1.000 0.928 0.902 -0.679 0.858 -0.814
disp -0.909 0.928 1.000 0.851 -0.684 0.898 -0.724 -0.624
hp -0.895 0.902 0.851 1.000 0.775 -0.667 -0.752
drat 0.651 -0.679 -0.684 1.000 -0.750 0.687 0.745
wt -0.886 0.858 0.898 0.775 -0.750 1.000 -0.738 -0.676
qsec -0.667 1.000 0.792
vs 0.707 -0.814 -0.724 -0.752 0.792 1.000
am -0.624 0.687 -0.738 1.000 0.808
gear 0.745 -0.676 0.808 1.000
carb -0.657 0.733 -0.659 -0.634
carb
mpg -0.657
cyl
disp
hp 0.733
drat
wt
qsec -0.659
vs -0.634
am
gear
carb 1.000
Matrix of Spearman's rank correlation rho:
(calculation uses everything)
m cy ds h dr w q v a g cr
mpg 1
cyl * 1
disp * * 1
hp + * + 1
drat , , , . 1
wt + + + , , 1
qsec . . . , 1
vs , + , , . . , 1
am . . , . , , 1
gear . . . . , , + 1
carb , . . , . , , 1
attr(,"legend")
[1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1
[1] -0.9108013
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