![]() r <- function(x, y, digits = 2, prefix = "", cex.cor. # Function to add correlation coefficients Note that you can add smoothed regression lines passing the panel.smooth function to the lower.panel argument. On the other hand, you can add the correlation coefficients in absolute terms, resized by the level of correlation, with the code of the following block. Just make sure that you set up your axes with scaling before you start to plot the ordered pairs. Creating a scatter plot is not difficult. 1: Scatter Plots Showing Types of Linear Correlation. Upper.panel = NULL, # Disabling the upper panelĭiag.panel = panel.hist) # Adding the histograms Here are some examples of scatter plots and how strong the linear correlation is between the two variables. # lines(density(x), col = 2, lwd = 2) # Uncomment to add density lines On the one hand, you can add histograms and density lines to the diagonal with the following code: # Function to add histograms A correlation coefficient is a bivariate statistic when it summarizes the relationship between two variables, and it’s a multivariate statistic when you have more than two variables. Note that if you want to delete some panels you can set them to NULL. The pairs function also allows you to specify custom functions on the upper.panel, lower.panel and diag.panel arguments. Row1attop = TRUE, # If FALSE, changes the direction of the diagonalĬex.labels = NULL, # Size of the diagonal textįont.labels = 1) # Font style of the diagonal text Main = "Iris dataset", # Title of the plot Labels = colnames(data), # Variable namesīg = rainbow(3), # Background color of the symbol (pch 21 to 25)Ĭol = rainbow(3), # Border color of the symbol In the following example we show you how to fully customize the scatter matrix plot, coloring the data points by group. The function can be customized with several arguments. ![]() Pairs(~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris) Note that you can also specify a formula if preferred. With the pairs function you can create a pairs or correlation plot from a data frame. Groups <- iris # Factor variable (groups) For explanation purposes we are going to use the well-known iris dataset. The most common function to create a matrix of scatter plots is the pairs function. Plot pairwise correlation: pairs and cpairs functions On the other hand, if you have more than two variables, there are several functions to visualize correlation matrices in R, which we will review in the following sections. ![]() You can also calculate Kendall and Spearman correlation with the cor function, setting the method argument to "kendall" or "spearman".
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