data_cor: Plotting pairwise correlations
In gamlss.ggplots: Plotting Generalised Additive Model for Location, Scale and Shape

data_cor

R Documentation

Plotting pairwise correlations

Description

The function data_cor takes a data frame and plots the pairwise Pearson's correlation coefficients of all continuous variables.

Usage

data_cor(data,  digits = 3, plot = TRUE, diag.off = TRUE, 
        lower.tri.off = FALSE, method = c("square", "circle"), 
        outline.color = "gray", colors = c("blue", "white", "red"),  
        legend.title = "Corr", title, ggtheme = theme_minimal(), 
        tl.cex = 12, tl.col = "black", tl.srt = 45, lab = TRUE, 
        lab_col = "black", lab_size = 3, circle.size = 20)

Arguments

`data`	a data frame
`digits`	the digits for printing the correlation coefficients
`plot`	whether to plot or not
`diag.off`	whether to show the diagonal ellements
`lower.tri.off`	whether to show the lower part of the matrix
`method`	plotting in `"square"` or `"cicle"`
`outline.color`	the outline colour
`colors`	the range of colours
`legend.title`	title for the legend
`title`	the main tittle
`ggtheme`	the theme for the plot, see package ggthemes for more themes
`tl.cex`	the text size for the marginal labels
`tl.col`	the colour of the he marginal labels
`tl.srt`	the angle of the text in the bottom labels of the table
`lab`	whether to show the correlation coefficients in the table
`lab_col`	the colour of the lettering of the correlation coefficients
`lab_size`	the size of the lettering of the correlation coefficients, increase (or decrease) if the defaul 3 is not appropriate
`circle.size`	the size of the circles, increase (or decrease) if the defaul 20 is not appropriate

Value

creates a plot

Author(s)

Mikis Stasinopoulos

References

Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1201/9780429298547")}.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1201/b21973")}