# pairs.ridge: Scatterplot Matrix of Bivariate Ridge Trace Plots In genridge: Generalized Ridge Trace Plots for Ridge Regression

 pairs.ridge R Documentation

## Scatterplot Matrix of Bivariate Ridge Trace Plots

### Description

Displays all possible pairs of bivariate ridge trace plots for a given set of predictors.

### Usage

``````## S3 method for class 'ridge'
pairs(
x,
variables,
lwd = 1,
lty = 1,
col = c("black", "red", "darkgreen", "blue", "darkcyan", "magenta", "brown",
"darkgray"),
center.pch = 16,
center.cex = 1.25,
digits = getOption("digits") - 3,
diag.cex = 2,
diag.panel = panel.label,
fill = FALSE,
fill.alpha = 0.3,
...
)
``````

### Arguments

 `x` A `ridge` object, as fit by `ridge` `variables` Predictors in the model to be displayed in the plot: an integer or character vector, giving the indices or names of the variables. `radius` Radius of the ellipse-generating circle for the covariance ellipsoids. `lwd, lty` Line width and line type for the covariance ellipsoids. Recycled as necessary. `col` A numeric or character vector giving the colors used to plot the covariance ellipsoids. Recycled as necessary. `center.pch` Plotting character used to show the bivariate ridge estimates. Recycled as necessary. `center.cex` Size of the plotting character for the bivariate ridge estimates `digits` Number of digits to be displayed as the (min, max) values in the diagonal panels `diag.cex` Character size for predictor labels in diagonal panels `diag.panel` Function to draw diagonal panels. Not yet implemented: just uses internal `panel.label` to write the variable name and ranges. `fill` Logical vector: Should the covariance ellipsoids be filled? Recycled as necessary. `fill.alpha` Numeric vector: alpha transparency value(s) for filled ellipsoids. Recycled as necessary. `...` Other arguments passed down

### Value

None. Used for its side effect of plotting.

Michael Friendly

### References

Friendly, M. (2013). The Generalized Ridge Trace Plot: Visualizing Bias and Precision. Journal of Computational and Graphical Statistics, 22(1), 50-68, doi:10.1080/10618600.2012.681237, https://www.datavis.ca/papers/genridge-jcgs.pdf

`ridge` for details on ridge regression as implemented here

`plot.ridge`, `traceplot` for other plotting methods

### Examples

``````
longley.y <- longley[, "Employed"]
longley.X <- data.matrix(longley[, c(2:6,1)])

lambda <- c(0, 0.005, 0.01, 0.02, 0.04, 0.08)
lridge <- ridge(longley.y, longley.X, lambda=lambda)