Penalized Isotonic Regression in one and two dimensions
Penalized Isotonic Regression in one and two dimensions
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.
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.
Returns
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
References
Meyer, M.C. (2013) A Simple New Algorithm for Quadratic Programming with Applications in Statistics, Communications in Statistics, 42(5) , 1126-1139.
Author(s)
Mary C Meyer, Professor, Department of Statistics, Colorado State University
Examples
### plot the estimated expected lung volume of children given age and heightdata(FEV)x1=FEV[,1]## agex2=FEV[,3]## heighty=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")