[R-sig-ME] Fwd: RE: gee, geese and glmer
Ben Bolker
bbolker at gmail.com
Tue Mar 18 03:28:45 CET 2014
[forwarding a conversation about lme4/lme4.0 incompatibilities. This
example looks pretty interesting, as it seems hard to prove that lme4
*isn't* giving the right answer/an answer that is numerically superior
to lme4.0, yet the lme4.0 answer is biologically preferable/more similar
to other estimation approaches. I don't know yet if we will eventually
find out that (1) the data are weird in a way that explains the
difference; (2) lme4 is actually misconverging, preliminary evidence to
the contrary; (3) ??? Enlightening comments are welcome.]
-------- Original Message --------
Subject: RE: [R-sig-ME] gee, geese and glmer
Date: Tue, 18 Mar 2014 00:18:01 +0000
From: Yang, Qiong <qyang at bu.edu>
To: Ben Bolker <bbolker at gmail.com>, "Chen, Ming-Huei" <mhchen at bu.edu>
CC: 'lme4-authors at lists.r-forge.r-project.org'
<lme4-authors at r-forge.wu-wien.ac.at>
Hi Ben,
Thanks for be mindful about our data confidentiality- what we
communicated in this email sequence so far can be copied to the general
mailing list.
Just to clarify further: All the cases are singletons(i.e. families of 1
member) while controls are from extended families (i.e. families of
multiple members).
The predictor variables do not identify the cases from controls.
We fitted reduce model where the only predictor is sex which does not
identify cases from controls however still observed reversed sign on
beta coefficients between lme4 and other software:
First, let's look at crude case by sex table
1 2
0 2554 3021
1 310 290
Ignoring family structure: Odds Ratio=0.79; beta=-0.23
Since all cases are singletons, I don't think including family-specific
random effects should change the direction of sex effects. Therefore I
tend to think the effect direction given by lme4 is not correct. Below
are the results with only sex as predictor.
> library(lme4)
> m1 <- glmer(case~sex+(1|famid),family=binomial,data=data)
> m1 at optinfo$warnings
list()
> coef(summary(m1))
Estimate Std. Error z value Pr(>|z|)
(Intercept) -10.2359223 0.4644516 -22.038725 1.225388e-107
sex 0.3531757 0.1837722 1.921813 5.462931e-02
> library(lme4.0)
> m0 <- lme4.0::glmer(case~sex+(1|famid),family=binomial,data=data)
Warning message:
In mer_finalize(ans) : false convergence (8)
> coef(summary(m0))
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.2807992 0.2868701 -11.436533 2.746424e-30
sex -0.2592505 0.1629382 -1.591097 1.115877e-01
> library(geepack)
> summary(geese(case~sex,id=famid,family=binomial,data=data))$mean
estimate san.se wald p
(Intercept) -1.8742252 0.16010676 137.03271 0.000000000
sex -0.2346185 0.09068416 6.69363 0.009675797
> library(gee)
> summary(gee(case~sex,id=famid,family=binomial,data=data))$coef
Estimate Naive S.E. Naive z Robust S.E. Robust z
(Intercept) -1.8742252 0.13510959 -13.871889 0.16010676 -11.706097
sex -0.2346185 0.08601766 -2.727562 0.09068416 -2.587205
Thanks a lot, Qiong
-----Original Message-----
From: Ben Bolker [mailto:bbolker at gmail.com]
Sent: Monday, March 17, 2014 3:51 PM
To: Chen, Ming-Huei; Yang, Qiong
Cc: 'lme4-authors at lists.r-forge.r-project.org'
Subject: Re: [R-sig-ME] gee, geese and glmer
[I'm not going to cc: to r-sig-mixed-models, as you may consider parts
of it to be sensitive, but I would like to ask permission to do so in
the future, as I think it would be useful to have these conversations in
public -- please let me know]
On 14-03-17 03:39 PM, Ming-Huei Chen wrote:
> Thanks, Ben!
>
> Our data was combined by two cohorts, one of them contains only cases
> and is an unrelated sample (249 male, 182 female). Do you think this
> may lead to what we observed here?
Possibly, although I would think the cohorts would have to be
identifiable somehow from the predictor variables.
>
> * I don't know why I got an error...
> Error: could not find function "slicetrans"
slicetrans is a function from the bbmle package, but it turns out to
be un-exported (this is still rather experimental functionality), so you
would need to use bbmle:::slicetrans
>
> * Do you mean using nAGQ? If so, I used nAGQ=5 an dgot a warning.
>> x <-
> summary(lme4::glmer(case~sex+PC1+PC2+PC3+PC4+(1|famid),family=binomial
> ,data=
> test.dat,nAGQ=5))
> Warning message:
> In checkConv(attr(opt, "derivs"), opt$par, checkCtrl = control$checkConv, :
> Model failed to converge with max|grad| = 0.00275074 (tol = 0.001)
> Fixed effects:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -2.675364 0.282841 -9.459 <2e-16 ***
> sex -0.053593 0.146444 -0.366 0.7144
> PC1 -0.205982 0.114760 -1.795 0.0727 .
> PC2 -0.161422 0.119219 -1.354 0.1757
> PC3 -0.191577 0.121107 -1.582 0.1137
> PC4 0.002723 0.122446 0.022 0.9823
This warning *might* be spurious (the lme4 maintainers have been
having a private conversation about the fact that one may get
false-positive warnings about gradients when the fit is singular):
>
> * Do you mean use fixef.prior? fixef.prior: a BLME prior of family
> normal, t, or NULL. So which one is weaker prior?
t is weaker than Normal (i.e., for the same mean (typically 0) and
variance it has fatter tails). However, there are two dimensions of
"weakness" -- to make a prior weaker you can make the scale (i.e.
variance) larger, and/or make the shape parameter more diffuse (i.e.,
make the tails fatter, in the sequence Gaussian (= t_\infty -> t_{large
n} -> t_{smaller n}). (You may end up discovering that the results
depend on the strength of the prior ... which will open up interesting
cans of worms in terms of what the data are actually telling you ...)
>
> Best,
>
> Ming-Huei
>
> -----Original Message-----
> From: Ben Bolker [mailto:bbolker at gmail.com]
> Sent: Friday, March 14, 2014 5:33 PM
> To: Ming-Huei Chen; 'Yang, Qiong'
> Cc: 'lme4-authors at lists.r-forge.r-project.org'
> Subject: Re: [R-sig-ME] gee, geese and glmer
>
> OK, this definitely looks well-behaved. I am leaning still more
> heavily toward the idea that new-lme4 is giving an answer that is
> technically more correct (the splom plot shows the focal parameters
> and the location of the minimum computed 'slice' value as closed and
> open circles (I forget which is which); if they don't coincide, that
> suggests a misconvergence. There are a few cases where there is a
> barely visible difference (i.e. you can see the open circle peeking
> out from around the edge of the closed circle), but they're generally
> very close. The deviance surface looks sensibly smooth/ellipsoidal, and none of the parameters appear to be on boundary.
> (There's a little problem with the slice2D code that limits the ranges
> sampled to be either all-positive or all-negative, I think.)
>
> If so, that makes the difference between GEE, Laplace
> approximation, and Bayesian GLMM as much of a statistical as a computational problem.
> A few more things I would try to follow up:
>
> * try s2 <- slicetrans(m0, m1, dd); plot(s2) to examine the
> cross-section of the deviance function between the new and old parameters
> * try using Gauss-Hermite quadrature to see if that makes a difference
> * try blme::bglmer to impose a weak prior on the fixed effects (see
> if that makes the results closer to MCMCglmm)
>
>
>
> On 14-03-14 02:15 PM, Ming-Huei Chen wrote:
>> Here it is....
>>
>> ###new
>>> library(lme4)
>>> m1 <-
>> glmer(case~sex+PC1+PC2+PC3+PC4+(1|famid),family=binomial,data=test.da
>> t
>> )
>>> m1 at optinfo$warnings
>> list()
>>> library(optimx)
>> Loading required package: numDeriv
>>> m1.1 <-
>> glmer(case~sex+PC1+PC2+PC3+PC4+(1|famid),family=binomial,data=test.da
>> t
>> ,contr
>> ol=glmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
>>> m1.1 at optinfo$warnings
>> list()
>>
>> ###old
>>> library(lme4.0)
>>> m0 <-
>> glmer(case~sex+PC1+PC2+PC3+PC4+(1|famid),family=binomial,data=test.da
>> t
>> )
>> Warning message:
>> In mer_finalize(ans) : false convergence (8)
>>> library(optimx)
>> Loading required package: numDeriv
>>> m0.0 <-
>> glmer(case~sex+PC1+PC2+PC3+PC4+(1|famid),family=binomial,data=test.da
>> t
>> ,contr
>> ol=glmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
>> Error in do.call(lmerControl, control) :
>> could not find function "glmerControl"
>>
>> Best,
>>
>> Ming-Huei
>> -----Original Message-----
>> From: Ben Bolker [mailto:bbolker at gmail.com]
>> Sent: Friday, March 14, 2014 11:08 AM
>> To: Ming-Huei Chen; 'Yang, Qiong'
>> Cc: lme4-authors at lists.r-forge.r-project.org
>> Subject: Re: [R-sig-ME] gee, geese and glmer
>>
>> The fact that the new deviance is lower suggests that the new
>> version of
>> lme4 is actually finding a _better_ fit to the data; so does the
>> existence of a convergence warning from old lme4 (it's interesting
>> that using
>> control=glmerControl(optimizer="optimx",optCtrl=list(method="nlminb")
>> ) doesn't give you a similar warning ...) . There is still some minor
>> possibility that there's a glitch in the deviance calculation -- a
>> good cross-check would be to evaluate the deviance of both sets of
>> parameters for old lme4 as well, but that's a bit harder to do
>> because
>> old-lme4 doesn't have the same level of flexibility).
>>
>> The parameter comparisons that new-lme4 is giving fairly extreme
>> estimates for one of the fixed parameters (old=-3, new=-10; looking
>> at the parameter tables below, it looks like this is the intercept)
>> and a very large estimate of the random-effects standard deviation.
>> (I could have figured this out from the parameter tables too, but I'm
>> lazy and graphical presentation makes it much more obvious.)
>>
>> This is looking like a case of (quasi-)complete separation, which
>> would explain why MCMCglmm is giving a much less extreme result (it
>> imposes weak priors on all of the parameters by default). It would
>> be interesting to try blme::bglmer on this case as well.
>>
>> Can you send the (graphical) results of
>>
>> dd <- lme4::glmer(...,devFunOnly=TRUE)
>> library(bbmle)
>> ss1 <- slice2D(fun = dd, params = m1parms, verbose = FALSE)
>> splom(ss1)
>>
>> ??
>>
>> On 14-03-14 09:39 AM, Ming-Huei Chen wrote:
>>> Thanks, Ben!
>>>
>>> The new deviance is lower than the old one. The plot of estimates is
>>> also attached.
>>>> dd(m0parms)
>>> [1] 3301.035
>>>> dd(m1parms)
>>> [1] 2991.881
>>>
>>> Best,
>>>
>>> Ming-Huei
>>> -----Original Message-----
>>> From: Ben Bolker [mailto:bbolker at gmail.com]
>>> Sent: Thursday, March 13, 2014 11:29 PM
>>> To: Ming-Huei Chen; 'Yang, Qiong'; 'Martin Maechler'
>>> Cc: r-sig-mixed-models at r-project.org
>>> Subject: Re: [R-sig-ME] gee, geese and glmer
>>>
>>> You should be able to install lme4.0 via:
>>>
>>> install.packages("lme4.0",
>>> repos="http://lme4.r-forge.r-project.org/repos")
>>>
>>> It should give identical results to lme4_0.999999-2.
>>>
>>> Also, if you can pull out the coefficients (both the theta and the
>>> fixed effect parameters) and save them somewhere, you don't need to
>>> run old and new lme4 in the same session. You just need to be able
>>> to pull both sets of parameters into an R session where you have
>>> lme4
>>>> =
>>> 1.0 loaded, use devFunOnly=TRUE to extract the deviance function,
>>> and apply it to both the old and the new sets of parameters.
>>>
>>> Ben Bolker
>>>
>>>
>>> On 14-03-13 08:54 PM, Ming-Huei Chen wrote:
>>>> Thanks Ben and Martin!
>>>>
>>>> I tried lme4 1.1-4 and the result was identical to lme4 1.0-6
>>>> without warning (by @optinfo$warnings). As you mentioned about
>>>> convergence warning, I redid the analysis with lme4_0.999999-2 and
>>>> saw a convergence warning. I also tried MCMCglmm and here is the
>>>> result, sex effect is insignificant but the estimate is negative as
>>>> in GEE and lme4_0.999999-2. But as you mentioned, it's different
> methodology..
>>>>
>>>> test <-
>>>> MCMCglmm(case~sex+PC1+PC2+PC3+PC4,random=~famid,family="categorical"
>>>> ,
>>>> d
>>>> ata=da
>>>> ta)
>>>> post.mean l-95% CI u-95% CI eff.samp pMCMC
>>>> (Intercept) -2.469391 -2.821757 -2.078372 4.290 <0.001 ***
>>>> sex -0.055439 -0.330237 0.111188 5.426 0.626
>>>> PC1 -0.197212 -0.332290 -0.018821 6.833 0.074 .
>>>> PC2 -0.158433 -0.364921 -0.003554 5.142 0.002 **
>>>> PC3 -0.130129 -0.320582 0.065973 4.094 0.320
>>>> PC4 0.002677 -0.306265 0.233621 2.060 0.812
>>>>
>>>> As for comparing deviance, because different versions of lme4 need
>>>> to be installed in different versions of R and there is actually no
>>>> lme4.0, so I don't know how to use two versions of lme4 in the same
>>>> R session... Since you planned to get lme4.0 on CRAN, so you should
>>>> have the package... in that case, can you please send it to me? Or?
>>>>
>>>> Our data contain pedigree information which cannot be shared (even
>>>> pedigree structure), so simulate may not help.
>>>>
>>>> Best,
>>>>
>>>> Ming-Huei
>>>>
>>>> -----Original Message-----
>>>> From: Ben Bolker [mailto:bbolker at gmail.com]
>>>> Sent: Thursday, March 13, 2014 4:09 PM
>>>> To: Yang, Qiong; Martin Maechler
>>>> Cc: r-sig-mixed-models at r-project.org; Chen, Ming-Huei
>>>> Subject: Re: [R-sig-ME] gee, geese and glmer
>>>>
>>>> On 14-03-13 02:01 PM, Yang, Qiong wrote:
>>>>> Hi Ben and Martin,
>>>>>
>>>>> Thanks you for your reply and taking time to explain the situation.
>>>>> Maybe I wasn't clear in my previous message or don't have a deeper
>>>>> understanding of the CRAN policy: Instead of separate packages for
>>>>> "old-CRAN" lme4 (lme4.0), and the current lme4, which is not
>>>>> allowed by CRAN maintainer, is it possible to have an option in
>>>>> current lme4 to call the algorithm in old lme4? I thought the new
>>>>> lme4 implements a different algorithm and why not make the old
>>>>> algorithm available in current lme4 lmer() as an option. It is
>>>>> like we can request pearson or spearman correlations in cor().
>>>>> Sorry if I simplified the problem too much.
>>>>
>>>> It's not so much CRAN policy as the architecture of the software.
>>>> A great deal of the internal structure has changed between versions
>>>> <1.0 and > 1.0, so putting both versions into one package would
>>>> basically mean having copies of all of the old and all of the new
>>>> code
>>>> -- and keeping it all safely distinguished and separate would
>>>> probably be substantially more work (we have about as much as we
>>>> can to do keep up with a single package ...) As we said before, we
>>>> were surprised by the rejection of lme4.0 by CRAN -- our previous
>>>> planning had assumed that we would be able to put lme4.0 on CRAN.
>>>> Somewhat to our surprise, there have been relatively few people
>>>> contacting us with troubles similar to yours -- for the most part
>>>> it seems that is
>>>> (thankfully) very rare that
>>>> lme4 gives significantly worse answers than lme4.0.
>>>>
>>>> So far it's not clear that lme4's answers are actually worse than
>>>> lme4.0's (I admit that all other things being equal, closer to GEE
>>>> is more likely to be correct -- on the other hand, we know that GEE
>>>> gives marginal estimates of fixed parameters while GLMM gives
>>>> conditional estimates, so we shouldn't expect them to be the same).
>>>> Can
>> I confirm:
>>>>
>>>> * you've tried this with the most recent development version of
>>>> lme4
>>>> (1.1-4) and you do *not* get any convergence warnings?
>>>>
>>>> * have you compared the deviances based on the old (lme4.0 / lme4 <
>>>> 1.0) and new packages? Here is the outline of how you would do this:
>>>>
>>>> library(lme4) ## load new package
>>>> m1 <- glmer(...) ## lme4 fit
>>>> library(lme4.0) ## load old package
>>>> m0 <- lme4.0::glmer(...) ## lme4.0 fit ## extract full parameter
>>>> set for lme4.0 and lme4 fits m0parms <-
>>>> c(getME(m0,"theta"),fixef(m0)) m1parms <-
>>>> c(lme4::getME(m1,"theta"),fixef(m1))
>>>> nparms <- length(m0parms)
>>>> ntheta <- length(getME(m0,"theta"))
>>>>
>>>> par(las=1,bty="l")
>>>> plot(m0parms,m1parms,col=rep(2:1,c(ntheta,nparms-ntheta)))
>>>> abline(a=0,b=1)
>>>>
>>>> ## set up deviance function
>>>> dd <- lme4::glmer(...,devFunOnly=TRUE) ## compare
>>>> dd(m0parms)
>>>> dd(m1parms)
>>>>
>>>> If the new deviance is lower than the old deviance, that's a
>>>> strong indication that the new version is actually getting a
>>>> _better_ fit than the old one.
>>>>
>>>> Another diagnostic tool is:
>>>>
>>>> library(bbmle)
>>>> ss1 <- slice2D(fun = dd, params = m1parms, verbose = FALSE)
>>>> splom(ss1)
>>>>
>>>> This will show you cross-sections of the deviance surface ...
>>>>
>>>>
>>>>> To Ben, Sorry that we cannot share data because of confidentiality
>>>>> policy. But you are able to debug our problem through PC screen
>>>>> sharing where you can gain control and run programs on our Linux
>>>>> session without possessing the data. If that sounds like a good
>>>>> idea, let us know when you might be available to do so.
>>>>
>>>> Can you generate a simulated example that replicates your problem?
>>>> simulate() is a useful tool for generating data that are similar,
>>>> but not identical to, your original data (you can also anonymize
>>>> factor labels etc.).
>>>>
>>>> PC screen sharing seems difficult -- I have easy access to MacOS
>>>> and Linux systems, but not Windows.
>>>>
>>>>>
>>>>> Thanks, Qiong -----Original Message----- From: Ben Bolker
>>>>> [mailto:bbolker at gmail.com] Sent: Tuesday, March 11, 2014 9:26 AM To:
>>>>> Martin Maechler; Yang, Qiong Cc: r-sig-mixed-models at r-project.org;
>>>>> Chen, Ming-Huei Subject: Re: [R-sig-ME] gee, geese and glmer
>>>>>
>>>>> I would also point out that we are indeed very interested, in the
>>>>> medium term (the short term is very very busy!), in making sure
>>>>> that your issues are resolved. We would like lme4 to dominate
>>>>> lme4.0 (i.e., to work better in all circumstances). So far it's
>>>>> been a bit difficult since we have been debugging remotely --
>>>>> short of the suggestions I gave below, and without access to a
>>>>> reproducible example, it's very hard indeed for me to say much more.
>>>>>
>>>>> sincerely Ben Bolker
>>>>>
>>>>> On 14-03-11 05:45 AM, Martin Maechler wrote:
>>>>>>>>>>> Yang, Qiong <qyang at bu.edu> on Mon, 10 Mar 2014 18:19:56
>>>>>>>>>>> +0000 writes:
>>>>>>
>>>>>>> Hi Ben, We wonder if you can add an option in lmer() of current
>>>>>>> lme4 version to call the algorithm used in lme4_0.999999-2?
>>>>>>
>>>>>> unfortunately not.
>>>>>>
>>>>>> For this reason, we had planned for many months, starting in
>>>>>> August
>>>>>> 2012 and announced on this mailing list at least a year ago that
>>>>>> we would provide the 'lme4.0' package (back-compatible as well
>>>>>> as possible in light of computer OS updates incl system
>>>>>> libraries, and R updates, ...) in order to provide useRs the
>>>>>> possibility of reproducible research and data analysis for their
>>>>>> analyses done with "old-CRAN" lme4 versions.
>>>>>>
>>>>>> Unfortunately (for us), the CRAN maintainers decided that
>>>>>> providing
>>>>>> lme4.0 in addition to lme4 was a bad idea, and explicit forbid
>>>>>> such actions in the (then new) CRAN policy document. I'm still
>>>>>> not at all happy with that decision.
>>>>>>
>>>>>>> For our package (analyze rare genetic variant) to be put on
>>>>>>> CRAN, we need to use current version of lme4. However, at this
>>>>>>> point, there are still issues that cannot be resolved with newer
>>>>>>> versions of lme4. It is very difficult resolved with newer
>>>>>>> versions
> of lme4.
>>>>>>> It is very difficult for us to keep waiting and testing the new
>>>>>>> release, and hope all the issues resolved and no new issues
>>>>>>> coming up. lme4_0.999999-2 has been used by us for a long time
>>>>>>> with little problem.
>>>>>>
>>>>>> Good to hear. Such cases were exactly the reason why we (lme4
>>>>>> authors) made such considerable effort to provide lme4.0 for
>>>>>> reproducible research and data analysis.
>>>>>>
>>>>>> at the bottom of
>>>>>> https://github.com/lme4/lme4/blob/master/README.md we mention the
>>>>>> state and give installation instruction of lme4.0, but as you
>>>>>> say, this does not solve the problem for other package
>>>>>> maintainers: If they want a CRAN package, they (currently? I'm
>>>>>> optimistic beyond reason :-) cannot have a 'Depends: lme4.0' (or
>>>>>> "Imports:..." or similar).
>>>>>>
>>>>>>> Your help on this is highly appreciated. Best, Qiong
>>>>>>
>>>>>> You're welcome; currentl there's not more we can do. Martin
>>>>>>
>>>>>> -- Martin Maechler, ETH Zurich, Switzerland
>>>>>>
>>>>>>
>>>>>>
>>>>>>> -----Original Message----- From: Ben Bolker
>>>>>>> [mailto:bbolker at gmail.com] Sent: Saturday, March 08, 2014 5:25
>>>>>>> PM
>>>>>>> To: r-sig-mixed-models at r-project.org Cc: Chen, Ming-Huei; Yang,
>>>>>>> Qiong Subject: Re: gee, geese and glmer
>>>>>>
>>>>>>> On 14-03-07 11:25 PM, Ming-Huei Chen wrote:
>>>>>>>> Hi Ben,
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> In an analysis we found that glmer in new lme4 gave result
>>>>>>>> different from old lme4, gee and geese, where old lme4 seems to
>>>>>>>> be closer to gee and geese.. Please see highlighted sex effect
> below.
>>>>>>>> Case by sex (2x2) table is also given. Can you please let us
>>>>>>>> know how would you look into the results? Thanks!
>>>>>>>>
>>>>>>
>>>>>>> [cc'ing to r-sig-mixed-models: **please** try r-sig-mixed-models
>>>>>>> first, not personal e-mail to me ...]
>>>>>>
>>>>>>> I can't say exactly what's going here; without having a
>>>>>>> reproducible example <http://tinyurl.com/reproducible-000> it's
>>>>>>> hard to say precisely. Thoughts:
>>>>>>
>>>>>>> * gee and geese are giving _exactly_ the same parameter
>>>>>>> estimates, to 8 significant digits, so I would guess they are
>>>>>>> wrapping identical underlying methods.
>>>>>>
>>>>>>> * As far as diagnosing the issue with lme4 1.0-6: * does
>>>>>>> changing the optimization method, i.e.
>>>>>>> glmerControl(optimizer="optimx",optCtrl=list(method="nlminb"))
>>>>>>> [must do library("optimx") first] or
>>>>>>> glmerControl(optimizer="bobyqa")
>>>>>>
>>>>>>> change the result?
>>>>>>
>>>>>>> * I would be curious whether the soon-to-be-released version
>>>>>>> 1.1-4 (which can be installed from github or
>>>>>>> lme4.r-forge.r-project.org/repos) gives either (1) convergence
>>>>>>> warnings or (2) different/better answers
>>>>>>
>>>>>>> * You can try specifying the starting values for lme4 to
>>>>>>> diagnose misconvergence; for example, start lme4 from the
>>>>>>> estimates given by old lme4/lme4.0 and see if it gives a similar answer.
>>>>>>
>>>>>>> * You can use the 'slice' and 'splom.slice' functions from bbmle
>>>>>>> to visualize the likelihood surfaces
>>>>>>
>>>>>>> good luck, Ben Bolker
>>>>>>
>>>>>>>> Ming-Huei
>>>>>>>>
>>>>>>
>>>>>>>> ###GEE
>>>>>>>>
>>>>>>>>> summary(gee(case~sex+PC1+PC2+PC3+PC4,id=famid,family=binomial,
>>>>>>>>> d
>>>>>>>>> a
>>>>>>>>> t
>>>>>>>>> a
>>>>>>>>> =da
>>>>>>
>>>>>>>>>
>>>>>>> ta))$coef
>>>>>>>> Estimate Naive S.E. Naive z Robust S.E. Robust z (Intercept)
>>>>>>>> -1.88047373 0.13532162 -13.8963286 0.15960440 -11.782092 sex
>>>>>>>> -0.23436854 0.08611269 -2.7216494 0.09050577 -2.589543 PC1
>>>>>>>> -0.05478639 0.06195318 -0.8843192 0.06822178 -0.803063 PC2
>>>>>>>> -0.09934572 0.06494563 -1.5296753 0.06520811 -1.523518 PC3
>>>>>>>> -0.07020391 0.06626875 -1.0593818 0.06962147 -1.008366 PC4
>>>>>>>> -0.13413097 0.06746716 -1.9880927 0.06979901 -1.921674
>>>>>>>>
>>>>>>
>>>>>>>> ###GEESE
>>>>>>>>
>>>>>>>>> summary(geese(case~sex+PC1+PC2+PC3+PC4,id=famid,family=binomia
>>>>>>>>> l
>>>>>>>>> ,
>>>>>>>>> d
>>>>>>>>> a
>>>>>>>>> ta=
>>>>>>
>>>>>>>>>
>>>>>>> data))$mean
>>>>>>>>
>>>>>>>> estimate san.se wald p
>>>>>>>>
>>>>>>>> (Intercept) -1.88047373 0.15960440 138.8176912 0.000000000 sex
>>>>>>>> -0.23436854 0.09050577 6.7057312 0.009610351 PC1 -0.05478639
>>>>>>>> 0.06822178 0.6449102 0.421938319 PC2 -0.09934572 0.06520811
>>>>>>>> 2.3211071 0.127629159 PC3 -0.07020391 0.06962147 1.0168016
>>>>>>>> 0.313278888 PC4 -0.13413097 0.06979901 3.6928324 0.054646745
>>>>>>>>
>>>>>>>> ### lme4_0.999999-2
>>>>>>>>
>>>>>>>>> summary(glmer(case~sex+PC1+PC2+PC3+PC4+(1|famid),family=binomi
>>>>>>>>> a
>>>>>>>>> l
>>>>>>>>> ,
>>>>>>>>> d
>>>>>>>>> ata
>>>>>>
>>>>>>>>>
>>>>>>> =data))
>>>>>>>> Estimate Std. Error z value Pr(>|z|) (Intercept) -3.01599
>>>>>>>> 0.28305 -10.655 <2e-16 *** sex -0.41056 0.16285 -2.521 0.0117 *
>>>>>>>> PC1 -0.17116 0.12903 -1.326 0.1847 PC2 -0.15510 0.13382 -1.159
>>>>>>>> 0.2465 PC3 -0.19044 0.13580 -1.402 0.1608 PC4 0.02532 0.13732
>>>>>>>> 0.184 0.8537
>>>>>>>>
>>>>>>>> ###lme4_1.0-6
>>>>>>>>
>>>>>>>>> summary(glmer(case~sex+PC1+PC2+PC3+PC4+(1|famid),family=binomi
>>>>>>>>> a
>>>>>>>>> l
>>>>>>>>> ,
>>>>>>>>> d
>>>>>>>>> ata
>>>>>>
>>>>>>>>>
>>>>>>> =data))
>>>>>>>>
>>>>>>>> Estimate Std. Error z value Pr(>|z|)
>>>>>>>>
>>>>>>>> (Intercept) -10.2784 0.8631 -11.909 <2e-16 *** sex 0.3497
>>>>>>>> 0.1975 1.770 0.0767 . PC1 -0.3555 0.1623 -2.190 0.0285 * PC2
>>>>>>>> -0.1087 0.1653 -0.657 0.5109 PC3 -0.2242 0.1652 -1.357 0.1748
>>>>>>>> PC4 0.1103 0.1671 0.660 0.5091
>>>>>>>>
>>>>>>>> Case by sex
>>>>>>>>
>>>>>>>> 1 2 0 2554 3021 1 310 290
>>>>>>>>
>>>>>>
>>>>>>> _______________________________________________
>>>>>>> R-sig-mixed-models at r-project.org mailing list
>>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>>>
>>>>>
>>>>
>
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