# Spearman plot

### Description

Plots the relationship between two variables using a Spearman Plot

### Usage

1 2 | ```
spearman.plot(x, y = NULL, dcol = "blue", lhist = 20,
num.dnorm = 5 * lhist, plot.cor = TRUE, ...)
``` |

### Arguments

`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 |

### Details

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.

### Author(s)

Dustin Fife

### Examples

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)
``` |