# liso.covweights: Covariate Weights for Adaptive Liso In liso: Fitting lasso penalised additive isotone models

## Description

Calculates covariate weights for the Adaptive Liso

## Usage

 `1` ```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.

Zhou Fang

## References

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```## 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.