liso.maxlamb: Liso maximum lambda

In liso: Fitting lasso penalised additive isotone models

Description

Calculates maximum value of lambda for which Liso gives a non-zero fit

Usage

liso.maxlamb(x=NULL,y=NULL,monotone=TRUE, covweights=rep(1, ncol(x)), weights=rep(1, length(y)))

Arguments

x	Design matrix (without intercept).
y	Response value.
monotone	Monotonicity pattern. Can be a single value, or a vector of length equal to the number of covariates. Takes values -1, 0, 1, indicating monotonically decreasing, non-monotonic, monotonically increasing respectively.
covweights	Covariate weights. Should be a vector of length equal to the number of covariates, or more if different weights are to be applied to positive and negative fits of non-monotone components.
weights	Observation weights. Should be a vector of length equal to the number of observations.

Author(s)

Zhou Fang

References

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

See Also

plotCV

Examples

## 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)

liso.maxlamb(x,y)
liso.maxlamb(x,y,monotone = -1)

liso documentation built on May 29, 2017, 6:47 p.m.