Last data update: 2014.03.03
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R: Covariate Weights for Adaptive Liso
liso.covweights | R Documentation |
Covariate Weights for Adaptive Liso
Description
Calculates covariate weights for the Adaptive Liso
Usage
liso.covweights(obj, signfind = FALSE)
Arguments
obj |
Initial fit to use, a multistep object.
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signfind |
If TRUE, conduct monotonicity detection procedure.
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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)
Results
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