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.