[R-sig-ME] Extractor Functions
John Maindonald
john.maindonald at anu.edu.au
Thu Aug 11 12:54:26 CEST 2011
showMethods(class="mer")
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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