| 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")