[R-sig-ME] Extractor Functions
Murray Jorgensen
maj at waikato.ac.nz
Thu Aug 11 12:09:51 CEST 2011
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:
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> 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
>
>
>
>
>
>
>
>
>
>
>
>
>
> #---------------------------------------------------------------------------
> ------------
>
>
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>
> "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
>
>
>
> ------------------------------
>
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> 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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