[R-sig-ME] Fwd: lmer2 slower?
Douglas Bates
bates at stat.wisc.edu
Sat Jan 27 18:50:16 CET 2007
I am forwarding (with permission) a conversation with Bill Auty who
discovered that in one of his examples lmer2 is considerably slower
than lmer, although they get to the same estimates. This can happen.
As I mentioned in one reply I hope that forcing a supernodal Cholesky
decomposition for models with crossed or partially crossed grouping
factors for the random effects will help but I haven't had time to
explore this yet.
I'll post some other timing results in another message.
---------- Forwarded message ----------
From: Douglas Bates <bates at stat.wisc.edu>
Date: Jan 26, 2007 3:38 PM
Subject: Re: lmer2 slower?
To: Bill Auty <bill at edmeasure.com>
On 1/26/07, Bill Auty <bill at edmeasure.com> wrote:
> > class(xq2 at L)
>
> [1] "dCHMsimpl"
>
> attr(,"package")
>
> [1] "Matrix"
Ah, good. That 's the answer that I wanted.
Briefly, the CHOLMOD sparse matrix code allows for two types of
Cholesky decompositions of sparse, symmetric matrices. These are
called "simplicial" and "supernodal". In the lmer code I forced the
supernodal decomposition. In the lmer2 code I allow the sparse matrix
manipulation code to choose one or the other based on some criteria
which I can "tune". I think that the default values that are
currently being used are too conservative about switching to the
supernodal decomposition so I may be able to gain back some of the
loss of speed.
Thanks again for checking this.
May I send a copy of this reply to the R-SIG-mixed-models list?
>
> >
>
> I don't have the math chops to stay with you on the theory of this, but
> I'm glad to help by trying things on my data.
>
> Douglas Bates wrote:
> > Thanks. That's valuable information.
> >
> > Could you check something for me? If you still have the object fit by
> > lmer2 could you send me the result of
> >
> > class(xq2 at L)
> >
> > The response should be either "dCHMsimpl" or "dCHMsuper"
> >
> > On 1/26/07, Bill Auty <bill at edmeasure.com> wrote:
> >> I've attached the output from running lmer and lmer2 on the same model.
> >> The variables are:
> >>
> >> rit = scale score on a math test
> >> gr = grade (range 0 - 2)
> >> stu = student ID
> >> sch = school ID
> >> dist = district ID
> >>
> >> The students are partially crossed in school and district.
> >>
> >>
> >> >
> >> system.time(xq2<-lmer2(rit~gr+I(gr^2)+(gr|stu)+(gr|sch)+(gr|dist),e2math,
> >>
> >> + control = list(niterEM =0, gradient = FALSE),method="ML"))
> >> [1] 428.223 30.569 458.986 0.000 0.000
> >> > xq2
> >> Linear mixed-effects model fit by maximum likelihood
> >> Formula: rit ~ gr + I(gr^2) + (gr | stu) + (gr | sch) + (gr | dist)
> >> Data: e2math
> >> AIC BIC logLik MLdeviance REMLdeviance
> >> 977133 977251 -488554 977109 977117
> >> Random effects:
> >> Groups Name Variance Std.Dev. Corr
> >> stu (Intercept) 78.60373 8.86587
> >> gr 1.88712 1.37373 -0.227
> >> sch (Intercept) 7.24479 2.69161
> >> gr 0.75162 0.86696 -0.512
> >> dist (Intercept) 4.30657 2.07523
> >> gr 0.37241 0.61026 0.139
> >> Residual 26.61534 5.15901
> >> Number of obs: 136484, groups: stu, 80224; sch, 182; dist, 63
> >>
> >> Fixed effects:
> >> Estimate Std. Error t value
> >> (Intercept) 209.65220 0.38078 550.6
> >> gr 7.67870 0.13085 58.7
> >> I(gr^2) -0.76376 0.01931 -39.6
> >>
> >> Correlation of Fixed Effects:
> >> (Intr) gr
> >> gr -0.188
> >> I(gr^2) 0.049 -0.411
> >> >
> >> system.time(q2<-lmer(rit~gr+I(gr^2)+(gr|stu)+(gr|sch)+(gr|dist),e2math,
> >> + control = list(niterEM =0, gradient = FALSE),method="ML"))
> >> [1] 241.323 24.070 265.412 0.000 0.000
> >> > q2
> >> Linear mixed-effects model fit by maximum likelihood
> >> Formula: rit ~ gr + I(gr^2) + (gr | stu) + (gr | sch) + (gr | dist)
> >> Data: e2math
> >> AIC BIC logLik MLdeviance REMLdeviance
> >> 977133 977251 -488554 977109 977117
> >> Random effects:
> >> Groups Name Variance Std.Dev. Corr
> >> stu (Intercept) 78.60255 8.86581
> >> gr 1.88767 1.37393 -0.227
> >> sch (Intercept) 7.24380 2.69143
> >> gr 0.75110 0.86666 -0.512
> >> dist (Intercept) 4.30710 2.07536
> >> gr 0.37265 0.61045 0.139
> >> Residual 26.61352 5.15883
> >> number of obs: 136484, groups: stu, 80224; sch, 182; dist, 63
> >>
> >> Fixed effects:
> >> Estimate Std. Error t value
> >> (Intercept) 209.65218 0.38079 550.6
> >> gr 7.67867 0.13086 58.7
> >> I(gr^2) -0.76376 0.01931 -39.6
> >>
> >> Correlation of Fixed Effects:
> >> (Intr) gr
> >> gr -0.188
> >> I(gr^2) 0.049 -0.411
> >> >
> >>
> >>
> >>
> >>
> >
>
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