[R-sig-ME] Extractor Functions
Murray Jorgensen
maj at waikato.ac.nz
Fri Aug 12 01:36:14 CEST 2011
Thanks John for that great answer which I will also use with other packages.
Thanks Martin for your willingness to entertain new extractor methods
and for the flattery of being grouped with John and with somewhat
experienced *lmer() users!
Cheers, Murray
On 12/08/11 2:11 AM, Martin Maechler wrote:
>>>>>> John Maindonald<john.maindonald at anu.edu.au>
>>>>>> on Thu, 11 Aug 2011 20:54:26 +1000 writes:
>
> > showMethods(class="mer")
>
> yes, indeed, thank you, John.
>
> And...:
> Murray, John, ...<any somewhat experienced *lmer() user> ,
> please do ask for missing "extractor" methods!
> I use quotes because I mean not just extractors in the
> strict sense, but also other sensible (small) utilities.
>
> For *mer objects, I agree that a user should typically never
> have to use str(.) and then access the slots directly...
> (though its clearly necessary now, IIRC).
>
> Notably because current lme4 and future lme4
> result objects will partly have very different slots,
> such that only such "extractor" methods will continue to work
> when you switch from lme4 to "future-lme4"..
>
> Martin Maechler, ETH Zurich
> (digressing from finishing his slides for "useR!" next week...)
>
>
> > John Maindonald email: john.maindonald at anu.edu.au
> > phone : +61 2 (6125)3473 fax : +61 2(6125)5549
> > Centre for Mathematics& Its Applications, Room 1194,
> > John Dedman Mathematical Sciences Building (Building 27)
> > Australian National University, Canberra ACT 0200.
> > http://www.maths.anu.edu.au/~johnm
>
> > On 11/08/2011, at 8:09 PM, Murray Jorgensen wrote:
>
> >> Is there a convenient table or list of available extractor
> >> functions for mer and summary.mer objects in lme4 and lme4a?
> >>
> >> Murray Jorgensen
> >>
> >> On 11/08/11 10:00 PM, r-sig-mixed-models-request at r-project.org
> >> wrote:
> >>> Send R-sig-mixed-models mailing list submissions to
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> >>>
> >>>
> >>> Today's Topics:
> >>>
> >>> 1. mixed model (Ahmad Rabiee) 2. Re: mixed model (Ben Bolker)
> >>>
> >>>
> >>> ----------------------------------------------------------------------
> >>>
> >>> Message: 1 Date: Thu, 11 Aug 2011 11:53:20 +1000 From: "Ahmad
> >>> Rabiee"<ahmadr at sbscibus.com.au>
> >>> To:<r-sig-mixed-models at r-project.org> Subject: [R-sig-ME]
> >>> mixed model
> >>> Message-ID:<005b01cc57c9$724ca390$56e5eab0$@com.au>
> >>> Content-Type: text/plain
> >>>
> >>> Hi
> >>>
> >>>
> >>>
> >>> I have a binomial dataset (0, 1), and would like to run a
> >>> "mixed model" logistic regression and also a "nested mixed
> >>> model" logistic regression using glmer:
> >>>
> >>>
> >>>
> >>> ket.glm1<- glmer(z_ket_1.4 ~ bcs_pre + bhb_date + lact + (1 |
> >>> herdno) , family = binomial, data = ket)
> >>>
> >>> #-------------------------------
> >>>
> >>>
> >>>
> >>> To account for the overdispersion in the dataset, I used the
> >>> following codes (according to lme4 package), but the output is
> >>> identical to the first model (above= ket.hlm1). Comments
> >>> please?
> >>>
> >>>
> >>>
> >>> # Mixed model accounting for overdispersion
> >>>
> >>> ket$obs<- 1:nrow(ket)
> >>>
> >>> ket.glm2<- glmer(z_ket_1.4 ~ bcs_pre + bhb_date + lact + (1 |
> >>> herdno) + (1|obs), family = binomial, data = ket)
> >>>
> >>> #-------------------------------------------------
> >>>
> >>>
> >>>
> >>> #Nest random effect
> >>>
> >>> When I want to run a nested random effects using "glmer" I get
> >>> an error message (below);
> >>>
> >>>
> >>>
> >>> # herds nested within studies
> >>>
> >>> ket.glm43<- glmer(z_ket_1.4 ~ bcs_pre + bhb_date + lact +
> >>> (1|studyid:herdno) + (1|id), family = binomial, data = ket)
> >>>
> >>>
> >>>
> >>> #Error message (What does this mean?)
> >>>
> >>> Error: length(f1) == length(f2) is not TRUE
> >>>
> >>> In addition: Warning messages:
> >>>
> >>> 1: In study:herdno :
> >>>
> >>> numerical expression has 2695 elements: only the first used
> >>>
> >>> 2: In study:herdno :
> >>>
> >>> numerical expression has 2695 elements: only the first used
> >>>
> >>> #---------------------------------------------------------------------------
> >>> ----------
> >>>
> >>>
> >>>
> >>> #glmmadmb
> >>>
> >>> I believe my dataset (binomial) is zero-inflated- and Ben
> >>> suggested that I should use the "glmmadmb" package to count
> >>> for the zero-inflation (Please correct me if I am wrong). I
> >>> can run this model (below), when I don't have a random effects
> >>> term in the model. But I don't understand the outputs:
> >>>
> >>>
> >>>
> >>> # first model (without random effects term)
> >>>
> >>> ket.glmm1<- glmmadmb(z_ket_1.4 ~ bcs_pre + bhb_date + lact ,
> >>> family = "binomial", data = ket)
> >>>
> >>> summary(ket.glmm2)
> >>>
> >>>
> >>>
> >>> Initial statistics: 10 variables; iteration 0; function
> >>> evaluation 0; phase 1
> >>>
> >>> Function value 1.8680316e+03; maximum gradient component mag
> >>> 1.4283e+01
> >>>
> >>> Var Value Gradient |Var Value Gradient |Var Value Gradient
> >>>
> >>> 1 0.00000 1.42834e+01 | 2 0.00000 -1.25533e-01 | 3 0.00000
> >>> 7.40839e+00
> >>>
> >>> 4 0.00000 9.75790e-01 | 5 0.00000 -1.44553e+00 | 6 0.00000
> >>> -1.77029e+00
> >>>
> >>> 7 0.00000 -1.94537e+00 | 8 0.00000 -1.72752e+00 | 9 0.00000
> >>> -9.14276e-01
> >>>
> >>> 10 0.00000 -1.12861e+00 |
> >>>
> >>>
> >>>
> >>> Intermediate statistics: 10 variables; iteration 10; function
> >>> evaluation 14; phase 1
> >>>
> >>> Function value 1.2444800e+03; maximum gradient component mag
> >>> -4.4890e-02
> >>>
> >>> Var Value Gradient |Var Value Gradient |Var Value Gradient
> >>>
> >>> 1-74.45668 1.75306e-02 | 2 3.38165 1.96037e-02 | 3-39.88270
> >>> -8.55544e-03
> >>>
> >>> 4 -4.77457 -4.48896e-02 | 5 10.47568 -5.10712e-03 | 6 12.37466
> >>> -2.16758e-03
> >>>
> >>> 7 13.04031 3.49481e-03 | 8 10.98531 5.92878e-04 | 9 6.74107
> >>> -4.27903e-03
> >>>
> >>> 10 6.99462 -1.41183e-04 |
> >>>
> >>> 10 variables; iteration 20; function evaluation 24; phase 1
> >>>
> >>> Function value 1.2444697e+03; maximum gradient component mag
> >>> 1.2294e-04
> >>>
> >>> Var Value Gradient |Var Value Gradient |Var Value Gradient
> >>>
> >>> 1-74.57412 6.45335e-05 | 2 3.24452 3.17209e-05 | 3-39.84581
> >>> -2.93776e-05
> >>>
> >>> 4 -4.43904 1.22940e-04 | 5 10.55041 -6.38080e-05 | 6 12.43515
> >>> -6.09376e-05
> >>>
> >>> 7 13.07189 -2.38280e-05 | 8 11.01902 -1.81564e-05 | 9 6.79542
> >>> -2.85983e-05
> >>>
> >>> 10 7.02120 -6.17190e-06 |
> >>>
> >>>
> >>>
> >>> - final statistics:
> >>>
> >>> 10 variables; iteration 21; function evaluation 25
> >>>
> >>> Function value 1.2445e+03; maximum gradient component mag
> >>> 4.5702e-05
> >>>
> >>> Exit code = 1; converg criter 1.0000e-04
> >>>
> >>> Var Value Gradient |Var Value Gradient |Var Value Gradient
> >>>
> >>> 1-74.57453 9.86716e-06 | 2 3.24432 1.00312e-05 | 3-39.84559
> >>> 3.57290e-05
> >>>
> >>> 4 -4.43952 4.57024e-05 | 5 10.55069 -2.73803e-05 | 6 12.43544
> >>> -2.03976e-05
> >>>
> >>> 7 13.07205 -6.19842e-06 | 8 11.01915 -2.28111e-06 | 9 6.79553
> >>> -1.71599e-05
> >>>
> >>> 10 7.02125 -2.24921e-06 |
> >>>
> >>> Estimating row 1 out of 10 for hessian
> >>>
> >>> Estimating row 2 out of 10 for hessian
> >>>
> >>> Estimating row 3 out of 10 for hessian
> >>>
> >>> Estimating row 4 out of 10 for hessian
> >>>
> >>> Estimating row 5 out of 10 for hessian
> >>>
> >>> Estimating row 6 out of 10 for hessian
> >>>
> >>> Estimating row 7 out of 10 for hessian
> >>>
> >>> Estimating row 8 out of 10 for hessian
> >>>
> >>> Estimating row 9 out of 10 for hessian
> >>>
> >>> Estimating row 10 out of 10 for hessian
> >>>
> >>> Estimated covariance matrix may not be positive definite
> >>>
> >>> 4.44173 4.92261 5.06046 5.06419 5.35787 5.45402 5.62318
> >>> 6.84209 8.1491 11.1008
> >>>
> >>> Estimated covariance matrix may not be positive definite
> >>>
> >>> 4.44173 4.92261 5.06046 5.06419 5.35787 5.45402 5.62318
> >>> 6.84209 8.1491 11.1008
> >>>
> >>> #-------------------------------------------
> >>>
> >>>
> >>>
> >>> When I run "glmmadmb" with a random effects term in the model,
> >>> I get an error message. I don't know what I am doing wrong
> >>> here. Any help would be greatly appreciated.
> >>>
> >>>
> >>>
> >>> # Mixed model (herdno is the random effects term)
> >>>
> >>> ket.glmm2<- glmmadmb(z_ket_1.4 ~ bcs_pre + bhb_date + lact +
> >>> (1 | herdno), family = "binomial", data = ket)
> >>>
> >>> summary(ket.glmm2)
> >>>
> >>>
> >>>
> >>> #Error message
> >>>
> >>> Error in process_randformula(formula, random, data = data) :
> >>>
> >>> all grouping variables must be factors
> >>>
> >>>
> >>>
> >>>
> >>>
> >>> Thanks.
> >>>
> >>> Ahmad
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>> #---------------------------------------------------------------------------
> >>> ------------
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>> "Try not to become a man of success, but rather try to become
> >>> a man of value" Albert Einstein
> >>> <http://www.brainyquote.com/quotes/authors/a/albert_einstein.html>
> >>>
> >>>
> >>>
> >>> Please note my new email address is
> >>> mailto:ahmadr at sbscibus.com.au. Please update your records.
> >>>
> >>>
> >>>
> >>>
> >>> [[alternative HTML version deleted]]
> >>>
> >>>
> >>>
> >>> ------------------------------
> >>>
> >>> Message: 2 Date: Thu, 11 Aug 2011 02:34:52 +0000 (UTC) From:
> >>> Ben Bolker<bbolker at gmail.com> To:
> >>> r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] mixed
> >>> model Message-ID:<loom.20110811T042857-460 at post.gmane.org>
> >>> Content-Type: text/plain; charset=us-ascii
> >>>
> >>>> Ahmad Rabiee<ahmadr at ...> writes:
> >>>
> >>> # I have a binomial dataset (0, 1),
> >>>
> >>> this is a key piece of information not stated previously
> >>> (or I missed it ...)
> >>>
> >>>> and would like to run a "mixed model" # logistic regression
> >>> and also a "nested mixed model" logistic regression # using
> >>> glmer:
> >>> #
> >>> # ket.glm1<- glmer(z_ket_1.4 ~ bcs_pre + bhb_date + lact + (1
> >>> | herdno) , # family = binomial, data = ket)
> >>>
> >>> If your data are binomial with values 0/1 (i.e., "binary" or
> >>> "Bernoulli"), it makes sense to incorporate neither
> >>> overdispersion nor zero-inflation.
> >>>
> >>> # To account for the overdispersion in the dataset, I used the
> >>> following codes # (according to lme4 package), but the output
> >>> is identical to the first model # (above= ket.hlm1). Comments
> >>> please?
> >>> #
> >>> # # Mixed model accounting for overdispersion
> >>> #
> >>> # ket$obs<- 1:nrow(ket)
> >>> #
> >>> # ket.glm2<- glmer(z_ket_1.4 ~ bcs_pre + bhb_date + lact + (1
> >>> | herdno) + # (1|obs), family = binomial, data = ket)
> >>>
> >>> As stated above, overdispersion is unidentifiable with binary
> >>> data.
> >>>
> >>> # #Nest random effect # When I want to run a nested random
> >>> effects using "glmer" I get an error # message (below);
> >>> #
> >>> # # herds nested within studies
> >>> #
> >>> # ket.glm43<- glmer(z_ket_1.4 ~ bcs_pre + bhb_date + lact + #
> >>> (1|studyid:herdno) + (1|id), family = binomial, data = ket)
> >>> #
> >>> # #Error message (What does this mean?)
> >>> #
> >>> # Error: length(f1) == length(f2) is not TRUE
> >>> #
> >>> # In addition: Warning messages:
> >>> #
> >>> # 1: In study:herdno :
> >>> #
> >>> # numerical expression has 2695 elements: only the first used
> >>>
> >>> [snip]
> >>>
> >>> It means that you need studyid and herdno to be factors, not
> >>> numeric variables, in order for this to work.
> >>>
> >>> # I believe my dataset (binomial) is zero-inflated- and Ben
> >>> suggested that I # should use the "glmmadmb" package to count
> >>> for the zero-inflation (Please # correct me if I am wrong). I
> >>> can run this model (below), when I don't have a # random
> >>> effects term in the model. But I don't understand the outputs:
> >>>
> >>> When I suggested that, it was before I knew your data were
> >>> binary. Zero-inflation doesn't make sense for binary data.
> >>>
> >>> # # first model (without random effects term)
> >>> #
> >>> # ket.glmm1<- glmmadmb(z_ket_1.4 ~ bcs_pre + bhb_date + lact ,
> >>> family = # "binomial", data = ket)
> >>> #
> >>> # summary(ket.glmm2)
> >>> #
> >>> # Initial statistics: 10 variables; iteration 0; function
> >>> evaluation 0; phase # 1 [snip]
> >>>
> >>> # Estimated covariance matrix may not be positive definite
> >>> #
> >>> # 4.44173 4.92261 5.06046 5.06419 5.35787 5.45402 5.62318
> >>> 6.84209 8.1491 # 11.1008
> >>> #
> >>> # When I run "glmmadmb" with a random effects term in the
> >>> model, I get an # error message. I don't know what I am doing
> >>> wrong here. Any help would be # greatly appreciated.
> >>> #
> >>> # # Mixed model (herdno is the random effects term)
> >>> #
> >>> # ket.glmm2<- glmmadmb(z_ket_1.4 ~ bcs_pre + bhb_date + lact +
> >>> (1 | # herdno), family = "binomial", data = ket)
> >>> #
> >>> # summary(ket.glmm2)
> >>> #
> >>> # #Error message
> >>> #
> >>> # Error in process_randformula(formula, random, data = data) :
> >>> #
> >>> # all grouping variables must be factors
> >>>
> >>> What it says. herdno must be a factor.
> >>>
> >>> Ben Bolker
> >>>
> >>>
> >>>
> >>> ------------------------------
> >>>
> >>> _______________________________________________
> >>> R-sig-mixed-models mailing list
> >>> R-sig-mixed-models at r-project.org
> >>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>
> >>>
> >>> End of R-sig-mixed-models Digest, Vol 56, Issue 15
> >>> **************************************************
> >>
> >> --
> >> Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Staff/maj.html
> >> Department of Statistics, University of Waikato, Hamilton, New Zealand
> >> Email: maj at waikato.ac.nz majorgensen at ihug.co.nz Fax 7 838 4155
> >> Phone +64 7 838 4773 wk Home +64 7 825 0441 Mobile 021 0200 8350
> >>
> >> _______________________________________________
> >> R-sig-mixed-models at r-project.org mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
--
Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Staff/maj.html
Department of Statistics, University of Waikato, Hamilton, New Zealand
Email: maj at waikato.ac.nz majorgensen at ihug.co.nz Fax 7 838 4155
Phone +64 7 838 4773 wk Home +64 7 825 0441 Mobile 021 0200 8350
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