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Type: Package
Title: Penalized Isotonic Regression in one and two dimensions
Version: 1.0
Date: 2014-04-04
Author: Mary C Meyer, Jiwen Wu, and Jean D. Opsomer
Maintainer: Mary Meyer <meyer@stat.colostate.edu>
Description: Given a response y and a one- or two-dimensional predictor, the isotonic regression estimator is calculated with the usual orderings.
License: GPL-2 | GPL-3
Depends: graphics, grDevices, stats, utils, coneproj, Matrix
Packaged: 2014-04-04 22:31:54 UTC; marycmeyer
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-04-05 19:08:34

Penalized Isotonic Regression in one and two dimensions

Description

Given a response y and a one- or two-dimensional predictor, the isotonic regression estimator is calculated with the usual orderings. The user can specify a penalty to tame spiking, or a default value can be used.

Details

Package: isotonic.pen
Type: Package
Version: 1.0
Date: 2014-04-04
License: GPL-2 | GPL-3

Author(s)

Mary C Meyer, Jiwen Wu, and Jean D Opsomer

Maintainer: Mary C Meyer <meyer@stat.colostate.edu>

References

Meyer, M.C. (2013) A Simple New Algorithm for Quadratic Programming with Applications in Statistics, Communications in Statistics, 42(5), 1126-1139.


Penalized Isotonic Regression in one and two dimensions

Description

Given a response vector y and a predictor matrix xmat with (one or two) columns, the isotonic regression estimator is returned, with the usual (complete or partial) ordering.

Usage

iso_pen(y, xmat, wt = 1, pen = TRUE, default = TRUE, lambda = 0, nsim = 0, alpha = 0.05)

Arguments

y

The response vector of length n

xmat

Either a one-dimensional predictor vector or an n by 2 matrix of two-dimensional predictor values.

wt

Optional weights – a positive vector of length n.

pen

If pen=FALSE, no penalty is applied to tame spiking. Default is pen=TRUE.

default

If default=FALSE, the user must specify a penalty value.

lambda

Optional penalty. If pen=0, an unpenalized isotonic regression is performed. If not supplied a default penalty is used.

nsim

The number of simulations used in the computation of approximate point-wise confidence intervals. The default is nsim=0, and no confidence intervals are returned.

alpha

The confidence level of the confidence intervals. Default is alpha=.05 (i.e., 95 percent confidence intervals)

Details

The least-squares isotonic regression is computed using the coneA function of the R package coneproj.

Value

fit

The fitted values; i.e., the estimated expected response

sighat

The estimated model standard deviation

upper

The upper points of the point-wise confidence intervals, returned if nsim>0

lower

The lower points of the point-wise confidence intervals, returned if nsim>0

Author(s)

Mary C Meyer, Professor, Department of Statistics, Colorado State University

References

Meyer, M.C. (2013) A Simple New Algorithm for Quadratic Programming with Applications in Statistics, Communications in Statistics, 42(5), 1126-1139.

Examples

### plot the estimated expected lung volume of children given age and height
data(FEV)
x1=FEV[,1]   ## age
x2=FEV[,3]   ## height
y=FEV[,2]
ans=iso_pen(y,cbind(x1,x2))
persp(ans$xg1,ans$xg2,ans$xgmat,th=-40,tick="detailed",xlab="age",ylab="height",zlab="FEV")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.