Description Usage Arguments Details Author(s) Examples
Plots the relationship between two variables using a Spearman Plot
1 2  | spearman.plot(x, y = NULL, dcol = "blue", lhist = 20, num.dnorm = 5 *
  lhist, plot.cor = TRUE, ...)
 | 
x | 
 either a matrix with two columns or a vector (if y is not   | 
y | 
 a vector  | 
dcol | 
 the color of the lines drawn for the density plot  | 
lhist | 
 the number of breaks in the histogram  | 
num.dnorm | 
 the number of breaks in the density line  | 
plot.cor | 
 logical. Should the spearman correlation be outputted in the plot?  | 
... | 
 arguments passed to   | 
Often data are not normally distributed, requiring the use of a spearman correlation to determine their relationship. However, doing so makes it difficult to visualize the data since scatterplots of raw data present the data as if a pearson correlation were used. This function plots the ranks of the data, while plotting along the axes the distributions of the raw data.
Dustin Fife
1 2 3 4 5 6  | ### generate skewed data
x = rnorm(1000)^2
y = .6*x + rnorm(1000, 0, sqrt(1-.6^2))
spearman.plot(cbind(x,y), col="red", lhist=50)
spearman.plot(x=iris$Sepal.Length, y=iris$Sepal.Width)
 | 
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