Hi Emmanuel,
Thank you. That confirms my suspicions then. If no-one else has insights
in what method would be proper here, I will switch to phylolm which
provides bootstrapped CIs.
All the best,
Wouter
On 22-Sep-17 11:56, Emmanuel Paradis wrote:
Hi Wouter,
confint() uses the normal approximation of the likelihood function,
not profiling. In the example you give, confint.default() is used
because 'm' does not inherit the class "glm". As you wrote below, this
usually gives CIs too small (as explained in ?binaryPGLMM) and
symmetric whereas the likelihood function is often asymmetric.
profile(), a generic function, does profiling, but there's no method
for the class "binaryPGLMM". There is a profile.glm() in MASS.
binaryPGLMM() estimates the parameters by alternating between PQL and
REML, so I'm not sure it makes sense to do profiling here.
HTH
Best,
Emmanuel
Le 19/09/2017 à 10:46, Wouter van der Bijl a écrit :
Hi all,
I'm successfully using ape::binaryPGLMM to fit logistic regression
models while taking phylogeny into account. I can obtain the
estimates, standard errors and p-values, as they are directly
returned in the model object.
However, can I calculate confidence intervals for the regression
coefficients?
One way to get some numbers is to define the vcov and coef functions
for binaryPGLMM objects and use the standard confint function:
m <- binaryPGLMM(Y ~ X1, phy=phy, data=sim.dat) # from ?binaryPGLMM
vcov.binaryPGLMM <- function(object, ...) { object$B.cov }
coef.binaryPGLMM <- function(object, ...) { object$B[, 1] }
confint(m)
This gives us CI's, but given the fact that confint.glm uses
profiling it seems unlikely to me that these numbers are correct.
If I can't figure it out, I think I will change the phylopath package
to use phylolm::phyloglm instead.
Thanks for your help in advance,
Wouter
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