Hello R helpers, I am trying to do a linear OLS regression of y on two variables x1 and x2. I want to constrain the coefficients of x1 and x2 to sum up to 1. and therefore run a constrained OLS. Can anybody help with this? (I have seen some answers to similar questions but it was not clear to me what I need to do) - I have tried the lm function with offset but I must not have used it properly. Thanks, Spyros
Have a look at the linear.hypothesis function in the car package. For example:> mod.duncan <- lm(prestige ~ income + education, data=Duncan) > > linear.hypothesis(mod.duncan, "income + education = 1")Linear hypothesis test Hypothesis: income + education = 1 Model 1: prestige ~ income + education Model 2: restricted model Res.Df RSS Df Sum of Sq F Pr(>F) 1 42 7506.7 2 43 8045.2 -1 -538.5 3.0129 0.08994 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 On 27/09/06, Mesomeris, Spyros [CIR] <spyros.mesomeris at citigroup.com> wrote:> Hello R helpers, > > I am trying to do a linear OLS regression of y on two variables x1 and > x2. I want to constrain the coefficients of x1 and x2 to sum up to 1. > and therefore run a constrained OLS. Can anybody help with this? (I have > seen some answers to similar questions but it was not clear to me what I > need to do) - I have tried the lm function with offset but I must not > have used it properly. > > Thanks, > Spyros > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >-- ================================David Barron Said Business School University of Oxford Park End Street Oxford OX1 1HP
you could reparameterize, e.g.,
x1 <- runif(100, -4, 4)
x2 <- runif(100, -4, 4)
X <- cbind(1, x1 , x2)
y <- rnorm(100, as.vector(X %*% c(5, -3, 4)), 2)
######################
fn <- function(betas){
betas <- c(betas, 1 - betas[2])
crossprod(y - X %*% betas)[1, ]
}
opt <- optim(c(5, -3), fn, method = "BFGS")
c(opt$par, 1 - opt$par[2])
I hope it helps.
Best,
Dimitris
----
Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium
Tel: +32/(0)16/336899
Fax: +32/(0)16/337015
Web: http://med.kuleuven.be/biostat/
http://www.student.kuleuven.be/~m0390867/dimitris.htm
----- Original Message -----
From: "Mesomeris, Spyros [CIR]" <spyros.mesomeris at
citigroup.com>
To: <r-help at stat.math.ethz.ch>
Sent: Wednesday, September 27, 2006 12:51 PM
Subject: [R] Constrained OLS regression
> Hello R helpers,
>
> I am trying to do a linear OLS regression of y on two variables x1
> and
> x2. I want to constrain the coefficients of x1 and x2 to sum up to
> 1.
> and therefore run a constrained OLS. Can anybody help with this? (I
> have
> seen some answers to similar questions but it was not clear to me
> what I
> need to do) - I have tried the lm function with offset but I must
> not
> have used it properly.
>
> Thanks,
> Spyros
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm