TransformationAxes {car} | R Documentation |

These functions produce axes for the original scale of
transformed variables. Typically these would appear as additional
axes to the right or
at the top of the plot, but if the plot is produced with
`axes=FALSE`

, then these functions could be used for axes below or to
the left of the plot as well.

basicPowerAxis(power, base=exp(1), side=c("right", "above", "left", "below"), at, start=0, lead.digits=1, n.ticks, grid=FALSE, grid.col=gray(0.50), grid.lty=2, axis.title="Untransformed Data", cex=1, las=par("las")) bcPowerAxis(power, side=c("right", "above", "left", "below"), at, start=0, lead.digits=1, n.ticks, grid=FALSE, grid.col=gray(0.50), grid.lty=2, axis.title="Untransformed Data", cex=1, las=par("las")) bcnPowerAxis(power, shift, side=c("right", "above", "left", "below"), at, start=0, lead.digits=1, n.ticks, grid=FALSE, grid.col=gray(0.50), grid.lty=2, axis.title="Untransformed Data", cex=1, las=par("las")) yjPowerAxis(power, side=c("right", "above", "left", "below"), at, lead.digits=1, n.ticks, grid=FALSE, grid.col=gray(0.50), grid.lty=2, axis.title="Untransformed Data", cex=1, las=par("las")) probabilityAxis(scale=c("logit", "probit"), side=c("right", "above", "left", "below"), at, lead.digits=1, grid=FALSE, grid.lty=2, grid.col=gray(0.50), axis.title = "Probability", interval = 0.1, cex = 1, las=par("las"))

`power` |
power for Box-Cox, Box-Cox with negatives, Yeo-Johnson, or simple power transformation. |

`shift` |
the shift (gamma) parameter for the Box-Cox with negatives family. |

`scale` |
transformation used for probabilities, |

`side` |
side at which the axis is to be drawn; numeric
codes are also permitted: |

`at` |
numeric vector giving location of tick marks on original scale; if missing, the function will try to pick nice locations for the ticks. |

`start` |
if a |

`lead.digits` |
number of leading digits for determining ‘nice’ numbers
for tick labels (default is |

`n.ticks` |
number of tick marks; if missing, same as corresponding transformed axis. |

`grid` |
if |

`grid.col` |
color of grid lines. |

`grid.lty` |
line type for grid lines. |

`axis.title` |
title for axis. |

`cex` |
relative character expansion for axis label. |

`las` |
if |

`base` |
base of log transformation for |

`interval` |
desired interval between tick marks on the probability scale. |

The transformations corresponding to the three functions are as follows:

`basicPowerAxis`

:Simple power transformation,

*x' = x^p*for*p != 0*and*x' = log x*for*p = 0*.`bcPowerAxis`

:Box-Cox power transformation,

*x' = (x^p - 1)/p*for*x != 0*and*x' = log(x)*for*p = 0*.`bcnPowerAxis`

:Box-Cox with negatives power transformation, the Box-Cox power transformation of

*z = .5 * (y + (y^2 + γ^2)^{1/2})*, where*gamma*is strictly positive if*y*includes negative values and non-negative otherwise. The value of*z*is always positive.`yjPowerAxis`

:Yeo-Johnson power transformation, for non-negative

*x*, the Box-Cox transformation of*x + 1*; for negative*x*, the Box-Cox transformation of*|x| + 1*with power*2 - p*.`probabilityAxis`

:logit or probit transformation, logit

*= log[p/(1 - p)]*, or probit*= Phi^-1(p)*, where*Phi^-1*is the standard-normal quantile function.

These functions will try to place tick marks at reasonable locations, but
producing a good-looking graph sometimes requires some fiddling with the
`at`

argument.

These functions are used for their side effects: to draw axes.

John Fox jfox@mcmaster.ca

Fox, J. and Weisberg, S. (2019)
*An R Companion to Applied Regression*, Third Edition, Sage.

`basicPower`

, `bcPower`

, `yjPower`

,
`logit`

.

UN <- na.omit(UN) par(mar=c(5, 4, 4, 4) + 0.1) # leave space on right with(UN, plot(log(ppgdp, 10), log(infantMortality, 10))) basicPowerAxis(0, base=10, side="above", at=c(50, 200, 500, 2000, 5000, 20000), grid=TRUE, axis.title="GDP per capita") basicPowerAxis(0, base=10, side="right", at=c(5, 10, 20, 50, 100), grid=TRUE, axis.title="infant mortality rate per 1000") with(UN, plot(bcPower(ppgdp, 0), bcPower(infantMortality, 0))) bcPowerAxis(0, side="above", grid=TRUE, axis.title="GDP per capita") bcPowerAxis(0, side="right", grid=TRUE, axis.title="infant mortality rate per 1000") with(UN, qqPlot(logit(infantMortality/1000))) probabilityAxis() with(UN, qqPlot(qnorm(infantMortality/1000))) probabilityAxis(at=c(.005, .01, .02, .04, .08, .16), scale="probit") qqPlot(bcnPower(Ornstein$interlocks, lambda=1/3, gamma=0.1)) bcnPowerAxis(1/3, 0.1, at=c(o=0, 5, 10, 20, 40, 80))

[Package *car* version 3.0-11 Index]