Description Usage Arguments Value Author(s) Examples
This function provides simultaneous visualization of a correlation matrix, scatter-plot with linear fits, and univariate density plots for multiple variables.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 |
data |
Data frame containing named columns of data only. |
r_size_lims |
Minimum and maximum size of the text reporting the correlation coefficients. Minimum is mapped to coefficients of 0 and maximum is mapped to coefficients of 1, with the mapping proportional to r^2. |
point_alpha |
Transparency of the data points (1 = opaque). |
density_height |
Proportion of the facet height taken up by the density plots. |
density_adjust |
Adjusts the bandwidth of the univariate density estimator. See |
density_colour |
Colour of the density plot. |
label_size |
Size of the variable labels on the diagonal. |
label_colour |
Colour of the variable labels on the diagonal. |
label_alpha |
Transparency of the variable labels on the diagonal (1 = opaque). |
lm_colour |
Colour of the fitted line. |
ci_colour |
Colour of the confidence interval surrounding the fitted line. |
ci_alpha |
Transparency of the confidence interval surrounding the fitted line (1 = opaque). |
test_alpha |
Type-I error rate requested for colouring of the “significant” correlation coefficients. |
test_correction |
Character string specifying the type of correction for multiple comparisons applied to the value specified by |
A printable/modifiable ggplot2 object.
Michael A. Lawrence mike.lwrnc@gmail.com
Visit the ez
development site at http://github.com/mike-lawrence/ez
for the bug/issue tracker and the link to the mailing list.
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 | ########
# Set up some fake data
########
library(MASS)
N=100
#first pair of variables
variance1=1
variance2=2
mean1=10
mean2=20
rho = .8
Sigma=matrix(
c(
variance1
, sqrt(variance1*variance2)*rho
, sqrt(variance1*variance2)*rho
, variance2
)
, 2
, 2
)
pair1=mvrnorm(N,c(mean1,mean2),Sigma,empirical=TRUE)
#second pair of variables
variance1=10
variance2=20
mean1=100
mean2=200
rho = -.4
Sigma=matrix(
c(
variance1
, sqrt(variance1*variance2)*rho
, sqrt(variance1*variance2)*rho
, variance2
)
, 2
, 2
)
pair2=mvrnorm(N,c(mean1,mean2),Sigma,empirical=TRUE)
my_data=data.frame(cbind(pair1,pair2))
########
# Now plot
########
p = ezCor(
data = my_data
)
print(p)
#you can modify the default colours of the
##correlation coefficients as follows
library(ggplot2)
p = p + scale_colour_manual(values = c('red','blue'))
print(p)
#see the following for alternatives:
# http://had.co.nz/ggplot2/scale_manual.html
# http://had.co.nz/ggplot2/scale_hue.html
# http://had.co.nz/ggplot2/scale_brewer.html
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.