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Last data update: 2014.03.03

R: Correlation plots

corPlot

R Documentation

Correlation plots

Description

Produces a scatter plot with the distribution of points according to the tested variables in the lower left triangle and the correlation values in the upper left triangle.

Usage

corPlot(df, method = "spearman", digits = 2, na.action = "keep", ties.method = "average",
        title = "", xlab = "variable.name", ylab = "variable.name", ...)

Arguments

`df`	a matrix or a data.frame. The values of variables (e.g., indices) to be compared are in columns.
`method`	`pearson`, `spearman` or `kendall`. This is the method to be used to produce the plot, according to `cor.test`. See details.
`digits`	Number of digits to round the correlation values on the plot.
`na.action`	for controlling the treatment of NAs in `spearman` or `kendall` plots. If TRUE, missing values in the data are put last; if FALSE, they are put first; if NA, they are removed; if "keep" they are kept with rank NA. See `rank`.
`ties.method`	`average`, `first`, `random`, `max`, or `min`; a character string specifying how ties are treated in `spearman` or `kendall` plots. See `rank` for details.
`title`	Title of the plot.
`xlab`	a character string for labelling x axes. `variable.name` (default value) will produce automatic labelling according to column names of `df`. Otherwise, either a single string can be entered, or a vector of strings of length equal to the number of columns of `df`.
`ylab`	a character string for labelling y axes. `variable.name` (default value) will produce automatic labelling according to column names of `df`. Otherwise, either a single string can be entered, or a vector of strings of length equal to the number of columns of `df`.
`...`	Further arguments to be passed to the individual plots. See `plot` and `par`

Details

The lower half shows the scatter plots of values or ranks of variables. The upper half shows the corresponding correlation coefficients (significativity: 0 '***' 0.001 '**' 0.01 '*' 0.05 '-' 0.1 ' ' 1). The diagonal shows the considered variables and the number of individuals available for each.

If the chosen method is pearson, then the actual values of the variables will be plotted. If the chosen method is a rank-based method, spearman or kendall, then the ranks will be plotted.

Warning

A high number of variables will likely result in a slow generation of plots and a poor readability. Above 10 variables, the readability is greatly reduced.

Author(s)

Boris Leroy leroy.boris@gmail.com

Examples

# Comparisons of species occurrences estimated from 2 different scales
data(spid.occ)
corPlot(spid.occ, method = "pearson")

# Another example:
# Correlation between different variables measured on the same individuals
data(iris)
corPlot(iris[, 1:4], method = "pearson")
corPlot(iris[, 1:4], method = "spearman")

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(Rarity)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Rarity/corPlot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: corPlot
> ### Title: Correlation plots
> ### Aliases: corPlot
> ### Keywords: ~kwd1 ~kwd2
> 
> ### ** Examples
> 
> # Comparisons of species occurrences estimated from 2 different scales
> data(spid.occ)
> corPlot(spid.occ, method = "pearson")
> 
> # Another example:
> # Correlation between different variables measured on the same individuals
> data(iris)
> corPlot(iris[, 1:4], method = "pearson")
> corPlot(iris[, 1:4], method = "spearman")
There were 32 warnings (use warnings() to see them)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>