R^2 from lme function
On 5/14/07, Martin Henry H. Stevens <HStevens at muohio.edu> wrote:
Hi Cleber,
By "full" I simply meant "not REML." the function assumes that the fixed effects were estimated using REML criteria, and using update() simply changes that to ML. If the model was fit originally with ML, it shouldn't make any difference.
I am reasonably sure that it should not matter whether there is an intercept. ML estimates are invariant to fixed effects structure, whereas REML depends upon it.
I think the issue with presence or absence of an intercept is in how it affects the choice of the null model. Your function always uses y ~ 1 as the null model and that may not be appropriate if there is no intercept term in the original model.
On May 14, 2007, at 11:55 AM, Cleber Borges wrote:
Hi Martin,
many thanks for your tip!
but, { :-( }
what it 'full MLE' ? how to calculate? it is a saturated model???
and
it is valid for 'no-intercept model?
Many thanks again...
Cleber
Hi Cleber,
I have been using this function I wrote for lmer output. It should be
easy to convert to lme. As with everything, buyer beware. Note
that it
requires (full) maximum likelihood estimates.
Rsq <- function(reml.mod) {
## Based on
## N. J. D. Nagelkerke. A note on a general definition
## of the coefficient of determination. Biometrika, 78:691?692,
1991.
ml.mod <- update(reml.mod, method="ML")
l.B <- logLik(ml.mod)
l.0 <- logLik( lm(ml.mod at y ~ 1) )
Rsq <- 1 - exp( - ( 2/length(ml.mod at y) ) * (l.B - l.0) )
Rsq[1]
}
Hank
Hello allR How to access R^2 from lme object? or how to calculate it? ( one detail: my model do not have a intercept ) thanks in advanced Cleber
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