ScatterplotSmoothers | R Documentation |

These smoothers are used to draw nonparametric-regression lines on scatterplots produced by
the `scatterplot`

, `scatterplotMatrix`

, and several other car functions.
The functions are not meant to
be called directly by the user, although the user can supply options via the `smoother.args`

argument,
the contents of which vary by the smoother (see *Details* below). The `gamLine`

smoother uses the
`gam`

function in the mgcv package, the `loessLine`

smoother uses the
`loess`

function in the stats package, and the `quantregLine`

smoother uses the
`rqss`

function in the quantreg package.

gamLine(x, y, col=carPalette()[1], log.x=FALSE, log.y=FALSE, var=FALSE, spread=var, smoother.args=NULL, draw=TRUE, offset=0) loessLine(x, y, col=carPalette()[1], log.x=FALSE, log.y=FALSE, var=FALSE, spread=var, smoother.args=NULL, draw=TRUE, offset=0) quantregLine(x, y, col=carPalette()[1], log.x=FALSE, log.y=FALSE, var=FALSE, spread=var, smoother.args=NULL, draw=TRUE, offset=0)

`x` |
horizontal coordinates of points. |

`y` |
vertical coordinates of points. |

`col` |
line color. |

`log.x` |
should be set to |

`log.y` |
should be set to |

`spread, var` |
the default is to plot only an estimated mean or median function. If either of these arguments is TRUE, then a measure of variability is also plotted. |

`smoother.args` |
additional options accepted by the smoother, in the form of a list of
named values (see |

`draw` |
if TRUE, the default, draw the smoother on the currently active graph.
If FALSE, return a list with coordinates |

`offset` |
For use when |

The `loessLine`

function is a re-implementation of the `loess`

smoother
that was used in car prior to September 2012. The main enhancement is the ability to
set more options through the `smoother.args`

argument.

The `gamLine`

function is more general than `loessLine`

because it supports fitting a generalized spline regression model, with user-specified error
distribution and link function.

The `quantregLine`

function fits a model using splines with estimation
based on L1 regression for the median and quantile regression the (optional) spread. It is
likely to be more robust than the other smoothers.

The `smoother.args`

argument is a list of named elements (or sub-arguments) used to pass
additional arguments to the smoother. As of November, 2016, the smoother is evaluated by default at an equally spaced grid of 50 points in the range of the horizontal variable. With any of the smoothers, you can change to, say, 100 evaluation points via the argument `smoother.args=list(evaluation=100)`

. As of version 3.0-1, the `smoother.args`

elements `col.var`

, `lty.var`

, and `lwd.var`

are equivalent to `col.spread`

, `lty.spread`

, and `lwd.spread`

, respectively. The `style`

sub-argument controls how spread/variance envelopes are displayed, with choices `"filled"`

(the default), `"lines"`

, and `"none"`

(which is equivalent to `var=FALSE`

). The `alpha`

subargument controls the transparency/opacity of filled spread envelopes with allowable values between `0`

and `1`

(default `0.15`

). The `border`

subargument controls whether a border line is drawn around the filled region (the default is `TRUE`

). The `vertical`

subargument controls whether the left and right ends of the filled region are forced to be vertical (the default is `TRUE`

).

For `loessLine`

, the default is
```
smoother.args=list(lty.smooth=1, lwd.smooth=2, lty.spread=4, lwd.spread=2, style="filled", alpha=0.15, span=2/3,
degree=1, family="symmetric", iterations=4)
```

. (Prior to November 2016, the default span was 1/2.)
The elements `lty.smooth`

, `lwd.smooth`

, and `col.spread`

are the line type, line width, and line color,
respectively of the mean or median smooth; `lty.spread`

,
`lwd.spread`

, and `col.spread`

are the line type, width, and color of the spread smooths, if requested.
The elements `span`

, `degree`

, and `family`

are
passed as arguments to the `loess`

function, along with `iterations`

robustness iterations.

For `gamLine`

, the default is
```
smoother.args=list(lty.smooth=1, lwd.smooth=2, lty.spread=4, lwd.spread=2, style="filled", alpha=0.15,
k=-1, bs="tp", family="gaussian", link=NULL, weights=NULL)
```

.
The first six elements are as for `loessLine`

. The next two
elements are passed to the `gam`

function to control smoothing:
`k=-1`

allows `gam`

to choose the number of splines in the basis
function; `bs="tp"`

provides the type of spline basis to be used, with `"tp"`

for the default thin-plate splines. The last three arguments specify
a distributional family, link function, and weights as in generalized linear models. See the examples
below. The `spread`

element is ignored unless `family="gaussian"`

and `link=NULL`

.

For `quantregLine`

, the default is
```
smoother.args=list(lty.smooth=1, lwd.smooth=2, lty.spread=4, lwd.spread=2, style="filled", alpha=0.15,
lambda=IQR(x))
```

. The first six
elements are as for `loessLine`

. The last element is passed to the
`qss`

function in quantreg. It is a smoothing
parameter, by default a robust estimate of the scale of the horizontal axis variable.
This is an arbitrary choice, and may not work well in all circumstances.

John Fox jfox@mcmaster.ca and Sanford Weisberg sandy@umn.edu.

`scatterplot`

, `scatterplotMatrix`

, `gam`

,
`loess`

, and `rqss`

.

scatterplot(prestige ~ income, data=Prestige) scatterplot(prestige ~ income, data=Prestige, smooth=list(smoother=gamLine)) scatterplot(prestige ~ income, data=Prestige, smooth=list(smoother=quantregLine)) scatterplot(prestige ~ income | type, data=Prestige) scatterplot(prestige ~ income | type, data=Prestige, smooth=list(smoother=gamLine)) scatterplot(prestige ~ income | type, data=Prestige, smooth=list(smoother=quantregLine)) scatterplot(prestige ~ income | type, data=Prestige, smooth=FALSE) scatterplot(prestige ~ income | type, data=Prestige, smooth=list(spread=TRUE)) scatterplot(prestige ~ income | type, data=Prestige, smooth=list(smoother=gamLine, spread=TRUE)) scatterplot(prestige ~ income | type, data=Prestige, smooth=list(smoother=quantregLine, spread=TRUE)) scatterplot(weight ~ repwt | sex, data=Davis, smooth=list(smoother=loessLine, spread=TRUE, style="lines")) scatterplot(weight ~ repwt | sex, data=Davis, smooth=list(smoother=gamLine, spread=TRUE, style="lines")) # messes up scatterplot(weight ~ repwt | sex, data=Davis, smooth=list(smoother=quantregLine, spread=TRUE, style="lines")) # robust set.seed(12345) w <- 1 + rpois(100, 5) x <- rnorm(100) p <- 1/(1 + exp(-(x + 0.5*x^2))) y <- rbinom(100, w, p) scatterplot(y/w ~ x, smooth=list(smoother=gamLine, family="binomial", weights=w)) scatterplot(y/w ~ x, smooth=list(smoother=gamLine, family=binomial, link="probit", weights=w)) scatterplot(y/w ~ x, smooth=list(smoother=loessLine), reg=FALSE) y <- rbinom(100, 1, p) scatterplot(y ~ x, smooth=list(smoother=gamLine, family=binomial))

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