pairs.ridge | R Documentation |

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

```
## S3 method for class 'ridge'
pairs(
x,
variables,
radius = 1,
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,
...
)
```

`x` |
A |

`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 |

`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 |

None. Used for its side effect of plotting.

Michael Friendly

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

```
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)
pairs(lridge, radius=0.5, diag.cex=1.75)
data(prostate)
py <- prostate[, "lpsa"]
pX <- data.matrix(prostate[, 1:8])
pridge <- ridge(py, pX, df=8:1)
pairs(pridge)
```

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