# scatter.smooth

##### Scatter Plot with Smooth Curve Fitted by Loess

Plot and add a smooth curve computed by `loess`

to a scatter plot.

- Keywords
- smooth

##### Usage

```
scatter.smooth(x, y = NULL, span = 2/3, degree = 1,
family = c("symmetric", "gaussian"),
xlab = NULL, ylab = NULL,
ylim = range(y, pred$y, na.rm = TRUE),
evaluation = 50, ..., lpars = list())
```loess.smooth(x, y, span = 2/3, degree = 1,
family = c("symmetric", "gaussian"), evaluation = 50, ...)

##### Arguments

- x, y
- the
`x`

and`y`

arguments provide the x and y coordinates for the plot. Any reasonable way of defining the coordinates is acceptable. See the function`xy.coords`

for details. - span
- smoothness parameter for
`loess`

. - degree
- degree of local polynomial used.
- family
- if
`"gaussian"`

fitting is by least-squares, and if`family = "symmetric"`

a re-descending M estimator is used. Can be abbreviated. - xlab
- label for x axis.
- ylab
- label for y axis.
- ylim
- the y limits of the plot.
- evaluation
- number of points at which to evaluate the smooth curve.
- ...
- For
`scatter.smooth()`

, graphical parameters, passed to`plot()`

only. For`loess.smooth`

, control parameters passed to`loess.control`

. - lpars
- a
`list`

of arguments to be passed to`lines()`

.

##### Details

`loess.smooth`

is an auxiliary function which evaluates the
`loess`

smooth at `evaluation`

equally spaced points
covering the range of `x`

.

##### Value

- For
`scatter.smooth`

, none.For

`loess.smooth`

, a list with two components,`x`

(the grid of evaluation points) and`y`

(the smoothed values at the grid points).

##### See Also

`loess`

; `smoothScatter`

for scatter plots
with smoothed *density* color representation.

##### Examples

`library(stats)`

```
require(graphics)
with(cars, scatter.smooth(speed, dist))
## or with dotted thick smoothed line results :
with(cars, scatter.smooth(speed, dist, lpars =
list(col = "red", lwd = 3, lty = 3)))
```

*Documentation reproduced from package stats, version 3.3, License: Part of R 3.3*

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