[R-sig-ME] Wald's test
Ben Bolker
bbolker at gmail.com
Wed Dec 5 02:36:09 CET 2012
It does appear however that (as Justin said in the first place) that
car::Anova.lme does only chi-squared and not F tests (car 2.0-15). I
haven't looked to see how hard it would be to extract the information
from the lme object to get the relevant denominator df (and I don't know
if there are theoretical complications ...)
Even the two-model anova.lme doesn't do F tests, only likelihood
ratio tests. Again, don't know whether this is an oversight or whether
there's some reason *not* to provide the option. (In contrast
anova.glm() *does* provide an F test, for testing nested models with
estimated scale parameters.)
On 12-12-04 08:19 PM, Justin Chase wrote:
> Thanks Dr. Fox.
>
> I only just figured out today that there were specific help files like
> ?anova.lme. I was trying to figure out the function using only ?anova,
> which says very little.
> That said, I did come across the package "pbkrtest" today and found that
> it provides a nice approximate type II F test (the Kenward-Roger
> approximation) for mixed effects models from lme4. I may wait for the
> Halekoh and Højsgaard paper to come out in the Journal of Statistical
> Software before I switch to this approach, but I would be interested to
> see what others in the R community think about the package and the K-R f
> test.
>
> Thanks again!
>
> Justin
>
> On Tue, Dec 4, 2012 at 8:32 PM, John Fox <jfox at mcmaster.ca
> <mailto:jfox at mcmaster.ca>> wrote:
>
> Dear Justin,
>
> The documentation in ?anova.lme seems very clear to me -- what is
> described for type="marginal" is what is often called type III
> tests, though for these to be sensible in models with interactions,
> careful attention has to be paid to contrast coding. The Anova()
> function in the car package has methods for type II tests for models
> fit by lme() in the nlme package and lmer() in the lme4 package.
>
> I hope this helps,
> John
>
> ------------------------------------------------
> John Fox
> Sen. William McMaster Prof. of Social Statistics
> Department of Sociology
> McMaster University
> Hamilton, Ontario, Canada
> http://socserv.mcmaster.ca/jfox/
>
> On Tue, 4 Dec 2012 11:15:56 -0400
> Justin Chase <justinwchase1 at gmail.com
> <mailto:justinwchase1 at gmail.com>> wrote:
> > Thanks Dr. Bolker! I have one issue though: I was able to
> generate F tests
> > for my lme models using your suggestion anova(fm1,type="marginal") ,
> > however, it is my understanding that "marginal" typically refers
> to Type 3
> > sums-of-squares, which is discouraged. I'm looking to generate
> Type 2 tests
> > (i.e., "conditional"). The help file for anova() does not explain
> what the
> > word "marginal" means in the context of the function, thus I've
> cc'd John
> > Fox, who may be able to confirm whether or not this is Type II SS.
> >
> > Thanks!
> >
> > Justin
> >
> > On Tue, Dec 4, 2012 at 10:12 AM, Ben Bolker <bbolker at gmail.com
> <mailto:bbolker at gmail.com>> wrote:
> >
> > >
> > > [I'm taking the liberty of cc'ing r-sig-mixed-models ... I strongly
> > > prefer *not* to answer mixed models questions offline, for a
> variety of
> > > reasons.]
> > >
> > > On 12-12-03 02:12 PM, Justin Chase wrote:
> > > > Hi there Dr. Bolker.
> > > >
> > > > I have a question regarding both your 2008 paper in TREE and your
> > > > response to Helios de Rosario's mixed model question on the
> > > > R-sig-mixed-models mailing list. I am analyzing count data
> (transformed
> > > > for normality) from a slightly unbalanced split-plot lab
> experiment. I
> > > > have two fixed factors, one whole-plot and one sub-plot, with
> "plot" as
> > > > random factor. the model looks like this:
> > > >
> > > > *y = µ + A_i + B_j + A*B_ij + C_k (B_i ) + A*C(B_i )_jk +
> e_l(ijk) *
> > > >
> > > > I have been using the nlme package in R to test the
> significance of
> > > > fixed factors and their interaction on my univariate data,
> following
> > > > chapter 19 in The R Book (Crawley 2007).
> > >
> > > I don't actually have Crawley's book (although I probably
> should, if
> > > only to find out what he's telling people to do).
> > >
> > > > However, I'm not interested in
> > > > analyzing coefficients of individual effects (treatment
> levels) like
> > > > Bates does, but just want a significance test for whole
> factors. Because
> > > > I don't want to prioritize either fixed factor, I prefer to
> generate
> > > > type II SS estimates. Like Helios de Rosario, I used the Anova
> function
> > > > in the car package to generate type II anova tables with
> Wald's Chi
> > > > Square test and P values (because the F test is not available
> for this
> > > > type of model). The results seem very reasonable and are fairly
> > > > consistent with some least squares tests I've done on the same
> data
> > > > using aov (reordering terms to get "type II" results). I've
> read your
> > > > response to Helios's inquiry but unfortunately it was a bit
> over my
> > > > head. According to your paper in TREE, my data should be
> analyzed with
> > > > REML and F tests, but only Wald's X2 is available in R. Do you
> think I'm
> > > > on the right track or should I just forget about using REML
> and analyze
> > > > with aov on least squares fits (laboriously rearranging model
> terms to
> > > > get type II tests)?
> > >
> > > Hmm. Obviously I don't have your exact model, but I'm trying to
> > > figure out what's wrong with, for example,
> > >
> > > library(nlme)
> > > fm1 <- lme(distance ~ age, data = Orthodont) # random is ~ age
> > > anova(fm1,type="marginal")
> > >
> > > ## numDF denDF F-value p-value
> > > ## (Intercept) 1 80 467.4406 <.0001
> > > ## age 1 80 85.8464 <.0001
> > >
> > > ? see anova.lme
> > >
> > > Some of the complexity of the 2008 paper is due to the fact
> > > that GLMMs are more challenging in several respects than LMMs.
> > >
> > > > Also, should I be testing for overdispersion when using the
> Wald test,
> > > > even though I'm just modelling Gaussian data with lme?
> > >
> > > No. The Gaussian family has an adjustable scale parameter (i.e.,
> > > the variance is estimated from the data, rather than [as in the
> > > binomial or Poisson families] fixed at a theoretical value)
> > >
> > >
> > >
> >
> >
> > --
> > *Justin W. Chase*
> > MSc Candidate
> > Canadian Rivers Institute
> > University of New Brunswick
> > 506-452-7474 <tel:506-452-7474> (home*)
> > 506-453-4845 <tel:506-453-4845> (office)
> > justinwchase1 at gmail.com <mailto:justinwchase1 at gmail.com>
> > LinkedIn Profile <http://www.linkedin.com/in/justinwchase>
> >
> > [[alternative HTML version deleted]]
> >
>
>
>
>
>
> --
> *Justin W. Chase*
> MSc Candidate
> Canadian Rivers Institute
> University of New Brunswick
> 506-452-7474(home*)
> 506-453-4845(office)
> justinwchase1 at gmail.com <mailto:justinwchase1 at gmail.com>
> LinkedIn Profile <http://www.linkedin.com/in/justinwchase>
>
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