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
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)
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
5 matches
Mail list logo
