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Yes. There is something in R for such tasks. This requires a special package called mgcv, which should be installed in standard R configuration. See http://sekhon.berkeley.edu/library/mgcv/html/pcls.html. Especially, check out the option Ain and bin.
Here is an example I wrote:
(copy-paste these lines into R console)
## load the special package first.
library(mgcv)
options(digits=3)
## generate some fake data
x.1<-rnorm(100, 0, 1)
x.2<-rnorm(100, 0, 1)
x.3<-rnorm(100, 0, 1)
x.4<-rnorm(100, 0, 1)
y<-1+0.5*x.1-0.2*x.2+0.3*x.3+0.1*x.4+rnorm(100, 0, 0.01)
## make your own design matrix with one column corresponding to the intercept
x.mat<-cbind(rep(1, length(y)), x.1, x.2, x.3, x.4)
## this is the regular least-square regression
ls.print(lsfit(x.mat, y, intercept=FALSE))
## since you already have an X column for intercept, so no need for lsfit to assume another intercept term.
## the penalized constrained least square regression
M<-list(y=y,
w=rep(1, length(y)),
X=x.mat,
C=matrix(0,0,0),
p=rep(1, ncol(x.mat)),
off=array(0,0),
S=list(),
sp=array(0,0),
Ain=diag(ncol(x.mat)),
bin=rep(0, ncol(x.mat)) )
pcls(M)
4 comments:
Using the same R function, it can be done by specify the options of PCLS. You can follow the link in this post to read the help file of this function.
Tian,
Need to run a regression (y~ x) (just one independent variable) with constraints on the coefficient of x. y > x.
Constraint such that coefficient of x is integer between 1 and 10 (say). How can I do it in R ?. Any help greatly appreciated.
Thanks
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I am performing a linear regression and what I need is (1) to constrain the sum of the regression coefficients to 1, and (2) to constrain the sum of regression coefficients to 1 AND each regression coefficient to be non-negative. For each case, I need the standard error, t-stat and p values for each regression coefficient as it appears in an unconstrained linear regression.
I have tried the solve.QP command and I get correct results but I do not get the t-stat and p-values as in a linear regression
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