liso.backfit: Function to fit penalized additive isotonic models
In liso: Fitting lasso penalised additive isotone models

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/backfit.R

Fits penalized additive isotonic models using a total variation penalty.

1	liso.backfit(x, y, lambda=0, givebeta = FALSE, tol.target = 1e-04, weights= rep(1, length(y)), covweights=rep(1, ncol(x)), feed, trace=FALSE, monotone=TRUE, randomise=FALSE, huber=Inf)

`x`	Design matrix (without intercept).
`y`	Response value.
`lambda`	Value of the penalty parameter lambda. Can be either a single value or a vector, in which case the calculations are done sequentially, using the previous calculation as the `feed` input.
`givebeta`	If TRUE, output result as a vector instead of a `multistep` object.
`tol.target`	Threshold at which Liso loss change is considered small enough for convergence.
`weights`	Observation weights. Should be a vector of length equal to the number of observations.
`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.
`feed`	Initial values for backfitting calculation. By default, the zero fit is used. Any `multistep` fit can be used instead.
`trace`	If TRUE, print diagnostic information as calculation is done.
`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.
`randomise`	If TRUE, randomly permute the order of backfitting in each cycle. Usually slower, but possibly more stable.
`huber`	If less than Inf, huberization parameter for huberized liso. (Experimental)

With a single value of lambda, a lisofit object is returned, which inherits from class multistep. With more than one value, a list of lisofit values are generated. plot, summary, print, `*` and other methods exist.

Zhou Fang

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

cv.liso

## 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)

## Try lambda = 2, lambda = 1
fits <- liso.backfit(x,y, c(2,1), monotone=c(-1,rep(1, 49)))

## plot the result for lambda = 2
plot(fits[[2]])

## Plot y-yhat plot
plot(y,fits[[2]] * x)