You could test the hypothesis:
Ho: b1 = -b2
Ha: b1 != -b2
through the use of contrasts. You could also do it through a likelihood ratio test with model 1 being the full model and model 2 being the reduced model.
If you got this model:
mode 1) y=a +b1x1 + b2x2 + u
and this:
mode 2) y=a + b1(x1-x2) + u
What do you call the restriction there has been put on model 1 in model 2?
Is it just b1=-b2 ?
if yes, how do you test this kind of restriction? I really dunno:S
You could test the hypothesis:
Ho: b1 = -b2
Ha: b1 != -b2
through the use of contrasts. You could also do it through a likelihood ratio test with model 1 being the full model and model 2 being the reduced model.
yes okay, but i really dont know how to test that kind of hypothesis.
Is it possible you can provide me with a example?
What software are you using? Most packages have an easy way to specify a contrast.
I use Stata. Really hope you can help. I need to deliver an answer tomo:S
I've never used Stata so I can't help there.
nop okay. But can you tell me the theory i should do, point for point?
please help im so lost
Okay friends, this is my exam. I did not think it was necessary to read up on the material because I thought I knew everything. I thought I got all knowledge from heaven
Well there is a package called "contrast" in R but I typically just do them by hand.
Code:test.contrast = function(lm.out, C, d = 0){ # Provides a test of Ho: C*b = d vs. Ha: C*b != d # lm.out: - is the linear model used # C: ----- is a matrix with the desired set of contrasts # which may contain more than one row # d: ----- a vector of values to test against b <- coef(lm.out) V <- vcov(lm.out) df.numerator <- nrow(C) df.denominator <- lm.out$df Cb.d <- (C %*% b) - d Fstat <- drop(t(Cb.d) %*% solve(C %*% V %*% t(C)) %*% Cb.d/df.numerator) pvalue <- 1 - pf(Fstat, df.numerator, df.denominator) ans <- list(Fstat = Fstat, pvalue = pvalue) return(ans) } # Generate some fake data n <- 20 sigma <- 1 betas <- c(7, 2, -2) x1 <- 1:n x2 <- runif(n) X <- matrix(c(rep(1,n),x1, x2), ncol = 3) y <- X %*% betas + rnorm(n, 0, sigma) # Fit the linear model o <- lm(y ~ x1 + x2) # Test the idea that b1 = -b2 ie b1 + b2 = 0 C <- matrix(c(0, 1, 1), nrow = 1) test.contrast(o, C)
Last edited by Dason; 08-14-2011 at 11:19 PM.
dudes, im not much cleaver here. Please introduce me to the rapidhole
What? You thought you didn't have to read up on anything and that knowledge just came from heaven? Well you're wrong and you should probably start reading up on things...
What are you trying to say here? Rapidhole? What?
Are you just asking for quick answers? You could try googling "linear contrast".
Dason :=) you are right. With a probability in 100%
Isn't there some way to deal with the contrasts in the lm parameter list?
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