# [R] Linear Regression with Constraints

Tue May 26 20:54:59 CEST 2009

```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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Johns Hopkins University

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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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