Description Usage Arguments Value Author(s) References See Also Examples
Produces covariate plots for a multidimensional step function.
1 2 |
x |
A |
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. |
If grid
is TRUE, return the old par() values before function was called.
Zhou Fang
Zhou Fang and Nicolai Meinshausen (2009), Liso for High Dimensional Additive Isotonic Regression, available at http://blah.com
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)
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