Re: [R] Constraining coefficients

[Andrew Criswell]

Thank you all for the advice.

So, to confirm: If I have

   data.0 <- data.frame(y, x1, x2)
   levels(data.0$x1) <- c('one', 'two', 'three')  ### three levels
   fm1 <- glm(cbind(y, 20 - y) ~ x1 * x2, family = binomial, data = data.0)

then, what I need to do is merge the last two factors to one:

   data.0 <- data.1
   levels(data.1$x1) <- c('one', 'two', 'two')   ### two levels for factor
   fm2 <- glm(cbind(y, 20 - y) ~ x1 * x2, family = binomial, data = data.1)
   anova(fm2, fm1, test = 'Chisq')
Although I understand it achieves the same result, I am not sure what 
numbers to plug into the contrast. The constrat, as it currently stands, is

contrasts(data.0$x1) <- matrix(c(0, 0,
                                1, 0,
                                0, 1), ncol = 2, byrow = T)
Andrew



Prof Brian Ripley wrote:

On Fri, 5 Mar 2004, Andrew Criswell wrote:

 

I have a binomial model with one covariate, x1, treated as a factor with 
3 levels. The other covariate is measured x2 <- 1:30. The response, y, 
is the proportion of successes out of 20 trials.

glm(cbind(y, 20 - y) ~ x1 * x2, family = binomial)

Now, I would like to constrain the cofficients on 2 levels of the 
factor, x1, to be identical and test the difference between these models 
by a likelihood ratio test.

How can I get glm() to constrain the coefficients on 2 levels to be the 
same?
   

Merge the levels of the factor: see ?levels.

You could also set up a custom contrasts matrix: either way the natural S
approach is to reparametrize rather than constrain.
 

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