Le mercredi 27 mai 2009 à 17:28 +1000, bill.venab...@csiro.au a écrit :
> You can accommodate the constraints by, e.g., putting
>
> c2 = pnorm(theta2)
> c3 = pnorm(theta3)
Nice. I'd have tried invlogit(), but I'm seriously biased...
> x1 has a known coefficient (unity) so it becomes an offset.
>
-project.org] On Behalf Of
Emmanuel Charpentier [charp...@bacbuc.dyndns.org]
Sent: 27 May 2009 17:05
To: r-h...@stat.math.ethz.ch
Subject: Re: [R] Linear Regression with Constraints
Le mardi 26 mai 2009 à 14:11 -0400, Stu @ AGS a écrit :
> Hi!
> I am a bit new to R.
> I am looking for the right
Le mardi 26 mai 2009 à 14:11 -0400, Stu @ AGS a écrit :
> 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
> su
roject.org] On
Behalf Of Stu @ AGS
Sent: Tuesday, May 26, 2009 2:12 PM
To: r-help@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)
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
Hi Gopi,
Simple linear regression minimizes sum of squares of
the residuals. So in your case you can use Quadratic
Programming (see quadprog package) to introduce linear
constraints.
Regards,
Moshe.
--- Gopi Goswami <[EMAIL PROTECTED]> wrote:
> Hi there,
>
>
> Is there an existing package in
Hi there,
Is there an existing package in R that does simple linear regression
with linear constraints on the parameters? Here is the set up:
y_i = \sum_{k = 1}^K \beta_k x_k + \epsilon_i
where
\sum_{k = 1}^K c_k \beta_k = c_0, for some known co
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