Here is a demonstration of how to solve your problem :
n <- 30 # You might need more than 6 data points to get good estimates for
3 parameters
x1 <- rnorm(n)
x2 <- runif(n)
x3 <- rbinom(n, size=1, prob=0.4)
A <- cbind(x1, x2, x3) # 30 x 3 matrix of independent variables
b <- c(-1, 0.5, 0.2) # Note: the last component is out of bounds!
y <- A %*% b + rnorm(n, sd=0.1)
qr.solve(A, y) # unconstrained LS solution
# Implementing the bounds (there is probably a better way to do this)
#
nc <- ncol(A)
c1 <- matrix(0, nc, nc)
diag(c1) <- 1
c2 <- matrix(0, nc, nc)
diag(c2) <- -1
cmat <- rbind(c1, c2)
Cmat <- cmat[c(2,5,3,6), ] # Constraint matrix G
b0 <- c(0, -1, -1, 0)
require(limSolve)
ans <- lsei(A = A, B = y, G = Cmat, H = b0)
ans
While ans$X gives you the point estimates, it is a bit tricky to get
standard errors.
Hope this helps,
Ravi.
----------------------------------------------------------------------------
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Ravi Varadhan, Ph.D.
Assistant Professor, The Center on Aging and Health
Division of Geriatric Medicine and Gerontology
Johns Hopkins University
Ph: (410) 502-2619
Fax: (410) 614-9625
Email: rvaradhan at jhmi.edu
Webpage:
http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.h
tml
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-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On
Behalf Of Stu @ AGS
Sent: Tuesday, May 26, 2009 2:12 PM
To: r-help at r-project.org
Subject: [R] Linear Regression with Constraints
Hi!
I am a bit new to R.
I am looking for the right function to use for a multiple regression problem
of the form:
y = c1 + x1 + (c2 * x2) - (c3 * x3)
Where c1, c2, and c3 are the desired regression coefficients that are
subject to the following constraints:
0.0 < c2 < 1.0, and
0.0 < c3 < 1.0
y, x1, x2, and x3 are observed data.
I have a total of 6 rows of data in a data set.
Is "optim" in the stats package the right function to use?
Also, I can't quite figure out how to specify the constraints.
Thank you!
-Stu
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