liso.covweights: Covariate Weights for Adaptive Liso

In liso: Fitting lasso penalised additive isotone models

Description

Calculates covariate weights for the Adaptive Liso

Usage

liso.covweights(obj, signfind = FALSE)

Arguments

obj	Initial fit to use, a multistep object.
signfind	If TRUE, conduct monotonicity detection procedure.

Details

This function calculates automatically weights for a second run of the Liso algorithm, in an adaptive liso scheme. See example for practical usage.

Value

Produces a vector of covariate weights to be supplied as the covweight argument in liso.backfit.

Author(s)

Zhou Fang

References

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

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)

## Adaptive liso
initialfit = liso.backfit(x,y, 4)
secondfit = liso.backfit(x,y, 4, covweights = liso.covweights(initialfit))

## Compare sparsity
which(dim(initialfit) != 0)
which(dim(secondfit) != 0)

set.seed(79)
y2 <- scale(3 * (x[,1]> 0), scale=FALSE)  + x[,2]^3-6*(abs(x[,2] - 1)< 0.1)  + rnorm(n)

## Sign finding
initialfit = liso.backfit(x,y2, 2, monotone=FALSE)
secondfit = liso.backfit(x,y2, 2, monotone=FALSE, covweights = liso.covweights(initialfit, signfind=TRUE))

## Compare monotonicity. Note near x=1
plot(secondfit, dim=2)
plot(initialfit, dim=2, add=TRUE, col=2)

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