[R-sig-ME] Extractor Functions
Martin Maechler
maechler at stat.math.ethz.ch
Thu Aug 11 16:11:01 CEST 2011
>>>>> 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:
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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
>>
>> _______________________________________________
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