This particular case with a random intercept model can be handled by
glmmML, by bootstrapping the p-value.
Best, Göran
On Thu, Jul 17, 2008 at 1:29 PM, Douglas Bates <[EMAIL PROTECTED]> wrote:
> On Thu, Jul 17, 2008 at 2:50 AM, Rune Haubo <[EMAIL PROTECTED]> wrote:
>> 2008/7/16 Dimitris Rizopoulo
On Thu, Jul 17, 2008 at 2:50 AM, Rune Haubo <[EMAIL PROTECTED]> wrote:
> 2008/7/16 Dimitris Rizopoulos <[EMAIL PROTECTED]>:
>> well, for computing the p-value you need to use pchisq() and dchisq() (check
>> ?dchisq for more info). For model fits with a logLik method you can directly
>> use the foll
2008/7/16 Dimitris Rizopoulos <[EMAIL PROTECTED]>:
> well, for computing the p-value you need to use pchisq() and dchisq() (check
> ?dchisq for more info). For model fits with a logLik method you can directly
> use the following simple function:
>
> lrt <- function (obj1, obj2) {
>L0 <- logLik(
well, for computing the p-value you need to use pchisq() and dchisq()
(check ?dchisq for more info). For model fits with a logLik method you
can directly use the following simple function:
lrt <- function (obj1, obj2) {
L0 <- logLik(obj1)
L1 <- logLik(obj2)
L01 <- as.vector(- 2 *
Dear list,
I am fitting a logistic multi-level regression model and need to test the
difference between the ordinary logistic regression from a glm() fit and the
mixed effects fit from glmer(), basically I want to do a likelihood ratio test
between the two fits.
The data are like this:
My outc
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