Prints information and diagnostic statistics for a particular Liso fit.

1 2 |

`x` |
A |

Dummy variables for compatibility:

`...` |
Unused. |

`print`

prints, in this case, n, p, Lambda for the fit, and then for each non-zero fitted variable, stepwise and total variation complexity statistics, as well as the apparent monotonicity of the fit if it was not pre-specified. Finally some residual statistics are printed.

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 | ```
## 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)
print(fits)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.