# plot.multistep: Plot a multidimensional step function In liso: Fitting lasso penalised additive isotone models

## Description

Produces covariate plots for a multidimensional step function.

## Usage

 ```1 2``` ```## S3 method for class 'multistep' plot(x = NULL, xpoints=NULL, ypoints = NULL, dims = 1:max(nrow(x\$param), ncol(xpoints)) , ylimit = cbind(min(min(x),max(x)), max(max(x), min(x))), grid = TRUE, add = FALSE, titles = !add,...) ```

## Arguments

 `x` A `multistep` object. `xpoints` Covariate values of additional points to be plotted. `ypoints` Response values of additional points to be plotted. `dims` Dimensions to be shown. (Default is all) `ylimit` Y-axis limits to be used for all plots. `grid` If TRUE, construct a grid of plots to show all plotted components. Otherwise, plot each component after the other normally. `add` If TRUE, superimpose new plot on the old plot. This may false for more than one component. `titles` If TRUE, add names of covariates to plot. `...` Additional arguments to be passed to plot.

## Value

If `grid` is TRUE, return the old par() values before function was called.

Zhou Fang

## References

Zhou Fang and Nicolai Meinshausen (2009), Liso for High Dimensional Additive Isotonic Regression, available at http://blah.com

`multistep`, `plot`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```## Use the method on a simulated data set set.seed(79) n <- 100; p <- 50 ## Simulate design matrix and response x <- matrix(runif(n * p, min = -2.5, max = 2.5), nrow = n, ncol = p) y <- scale(3 * (x[,1]> 0), scale=FALSE) + x[,2]^3 + rnorm(n) ## try lambda = 2 fits <- liso.backfit(x,y, 2) fits2 <- liso.backfit(x,y, 4) ## Plot in some different ways plot(fits, dim=2) plot(fits2, dim=2, col=2, add=TRUE) plot(fits, grid=FALSE) plot(fits) ```