# lowess: Scatter Plot Smoothing In gplots: Various R Programming Tools for Plotting Data

## Description

The `lowess` function performs the computations for the LOWESS smoother (see the reference below). `lowess` returns a an object containing components `x` and `y` which give the coordinates of the smooth. The smooth can then be added to a plot of the original points with the function `lines`.

Alternatively, `plot` can be called directly on the object returned from `lowess` and the 'lowess' method for `plot` will generate a scatterplot of the original data with a `lowess` line superimposed.

Finally, the `plotLowess` function both calculates the `lowess` smooth and plots the original data with a `lowess` smooth.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```lowess(x, ...) ## Default S3 method: lowess(x, y=NULL, f=2/3, iter=3L, delta=0.01 * diff(range(x)), ...) ## S3 method for class 'formula' lowess(formula,data=parent.frame(), ..., subset, f=2/3, iter=3L, delta=.01*diff(range(mf[-response]))) ## S3 method for class 'lowess' plot(x, y, ..., col.lowess="red", lty.lowess=2) plotLowess(formula, data=parent.frame(), ..., subset=parent.frame(), col.lowess="red", lty.lowess=2 ) ```

## Arguments

 `formula` formula providing a single dependent variable (y) and an single independent variable (x) to use as coordinates in the scatter plot. `data` a data.frame (or list) from which the variables in ‘formula’ should be taken. `subset` an optional vector specifying a subset of observations to be used in the fitting process. `x, y` vectors giving the coordinates of the points in the scatter plot. Alternatively a single plotting structure can be specified. `f` the smoother span. This gives the proportion of points in the plot which influence the smooth at each value. Larger values give more smoothness. `iter` the number of robustifying iterations which should be performed. Using smaller values of `iter` will make `lowess` run faster. `delta` values of `x` which lie within `delta` of each other replaced by a single value in the output from `lowess`. `...` parameters for methods. `col.lowess, lty.lowess` color and line type for plotted line

## References

Cleveland, W. S. (1979) Robust locally weighted regression and smoothing scatterplots. J. Amer. Statist. Assoc. 74, 829–836.

Cleveland, W. S. (1981) LOWESS: A program for smoothing scatterplots by robust locally weighted regression. The American Statistician, 35, 54.

`loess` (in package `modreg`), a newer formula based version of `lowess` (with different defaults!).
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32``` ```data(cars) # # x,y method # plot(cars\$speed, cars\$dist, main="lowess(cars)") lines(lowess(cars\$speed, cars\$dist), col=2) lines(lowess(cars\$speed, cars\$dist, f=.2), col=3) legend(5, 120, c(paste("f=", c("2/3", ".2"))), lty=1, col=2:3) # # formula method: plot, then calculate the lowess smoother, # then add smooth to the plot # plot(dist ~ speed, data=cars, main="lowess(cars)") lines(lowess(dist ~ speed, data=cars), col=2, lty=2) lines(lowess(dist ~ speed, data=cars, f=.2), col=3) # smaller bandwith legend(5, 120, c(paste("f=", c("2/3", ".2"))), lty=1, col=2:3) # # formula method: calculate lowess() smoother, then call plot() # on the lowess object # lw <- lowess(dist ~ speed, data=cars) plot(lw, main="lowess(cars)" ) # # formula method: calculate and plot in a single command # plotLowess(dist ~ speed, data=cars, main="lowess(cars)") ```