[R-sig-ME] Help establishing mixed model equation for split, plot design

Wed Apr 4 12:12:32 CEST 2018

Thanks everyone.

Samuel, indeed I was refering to missing observations, and due to the lmer considering automatically I won't worry about it.

Thanks Samuel and Paul for the links. I think I'll stay with the lmer model but in case of needing to use the lme I'll check them for help.

Best regards to everyone, you have been of great help.

Felipe Calleja Apéstegui

Predoctoral researcher

Instituto de Hidráulica Ambiental "IH Cantabria"

C/ Isabel Torres, Nº 15

Parque Científico y Tecnológico de Cantabria

39011 Santander (España)

www.ihcantabria.es<http://www.ihcantabria.es/>

Tel:  +34 942 20 16 16 Ext. 1153

Fax: +34 942 26 63 61

e-mail: felipe-francisco.calleja at alumnos.unican.es

________________________________
De: Paul Johnson <paul.johnson at glasgow.ac.uk>
Enviado: miércoles, 4 de abril de 2018 11:14:34
Para: Samuel Knapp
Cc: CALLEJA APESTEGUI, FELIPE FRANCISCO; r-sig-mixed-models at r-project.org
Asunto: Re: [R-sig-ME] Help establishing mixed model equation for split, plot design

Hi,

> Sorry, lme only takes nested random effects

Other designs are possible, e.g. fitting crossed random effects in lme is possible but fiddly. Pinheiro & Bates (Mixed Effects Models in S and S-Plus, 2000, p163-167) show how to do this using pdBlocked.

Best wishes,
Paul

> On 4 Apr 2018, at 11:34, Samuel Knapp <samuel.knapp at tum.de> wrote:
>
> Hi Felipe,
>
>
> please also respond to the mailing list.
>
>
> Sorry, lme only takes nested random effects (see
> https://stats.stackexchange.com/questions/228800/crossed-vs-nested-random-effects-how-do-they-differ-and-how-are-they-specified).
> In order to use the rep:salinity effect, you could add a column for the
> main plots in your data set:
>
>
> sp.datos$rep_salinityF <- paste(sp.datos$rep,sp.datos$salinityF,paste="_")
>
> lmemodel <- lme(Plantsurvival ~ salinityF*immersionF*SpecTF, random=
> ~1|rep_salinityF, data=sp.datos)
>
>
> I hope, it works like this.
>
>
> I'm afraid, I don't fully understand your question regarding equal
> number of samples. Do you mean missing observations? In lme, you need to
> add na.action="na.omit", while lmer is automatically omitting missing
> observations.
>
> You're right, that the ANOVA is doing type III SS. If you're interested
> in other SS type, you could look at the Anova() function from the car
> package. Apparently it can also deal with mixed models. Furthermore, the
> new version of lmerTest can also handle other SS type. See
> https://github.com/runehaubo/lmerTestR/blob/master/pkg_notes/new_lmerTest.pdf
>
>
> Best,
>
> Samuel
>
>
>
> On 04/04/18 10:15, CALLEJA APESTEGUI, FELIPE FRANCISCO wrote:
>>
>> Hi Samuel,
>>
>>
>> first of all thanks a lot for the quick and clear response.
>>
>>
>> As you state, all models give the same results once they are
>> established following your examples. Now I have a clear view about
>> what is really doing the command and how to continue.
>>
>>
>> Just one quick comment. The line:
>>
>> *lmemodel <- lme(Plantsurvival ~ salinityF*immersionF*SpecTF, random=
>> ~**1|rep:salinityF, data=sp.datos)*
>>
>>
>> Throws the following error:
>>
>> *Error in getGroups.data.frame(dataMix, groups) : **invalid formula
>> for groups*
>>
>> I leave it here in case is interesting for you. I've decided to use
>> the lmer package because I have other variables that are not balanced,
>> so I prefer to use the same model in all cases, and that package is
>> working fine.
>>
>> Just one final question. Is there any consideration I should have when
>> working with variables without equal number of samples? I mean,
>> something specific in the command of the lmer I should be careful with
>> or investigate further on. Or does the function itself has all
>> considerations included. i see from the *anova(fit.okay3,
>> ddf="Kenward-Roger") *that the anova is made using the type III SS so
>> I wouldn't have to change that part. *
>> *
>>
>> Also, fyi,� the aov function in the case of not grouping by rep throws
>> the same result as the lmer.
>>
>> Again, I really appreciate your help, it has been a lifesaver.
>>
>> Best regards,
>>
>> *Felipe Calleja Ap�stegui*
>>
>> *Predoctoral researcher*
>>
>> *
>> *
>>
>> *Instituto de Hidr�ulica Ambiental "IH Cantabria"*
>>
>> C/ Isabel Torres, N� 15
>>
>> Parque Cient�fico y Tecnol�gico de Cantabria
>>
>> 39011 Santander (Espa�a)
>>
>> www.ihcantabria.es<http://www.ihcantabria.es> <http://www.ihcantabria.es/>
>>
>> Tel:� +34 942 20 16 16 Ext. 1153
>>
>> Fax: +34 942 26 63 61
>>
>> e-mail: f*elipe-francisco.calleja at alumnos.unican.es*
>>
>>
>> ------------------------------------------------------------------------
>> *De:* Samuel Knapp <samuel.knapp at tum.de>
>> *Enviado:* martes, 3 de abril de 2018 21:26:31
>> *Para:* r-sig-mixed-models at r-project.org; CALLEJA APESTEGUI, FELIPE
>> FRANCISCO
>> *Asunto:* Re: Help establishing mixed model equation for split, plot
>> design
>> Hi Felipe,
>>
>> if I understand the design correctly, it is a split-plot design with
>> salinityF as the whole-plot. Only, if you have arranged the boxes in a
>> way that replicate blocks are formed, you should include the rep effect
>> in a model. These replicate blocks could be, that you always have three
>> boxes with the three different salinity levels grouped together, e.g. on
>> one shelf. If you have simply replicated each box 5 times and they are
>> set up fully randomly, you should not include a repliate block.
>>
>> If you have no missing values, and you specify the right model, both
>> aov() and lme() should give you the same results. I would suggest the
>> following models:
>>
>>
>> # with replicate blocks
>>
>> aovmodel <- aov(Plantsurvival ~ salinityF*immersionF*SpecTF +
>> Error(rep/salinityF), data=sp.datos)
>>
>> lmemodel <- lme(Plantsurvival ~ salinityF*immersionF*SpecTF, random=
>> ~1|rep/salinityF, data=sp.datos)
>>
>> # or with lmer from lme4 package (library (lme4)
>>
>> lmermodel <- lmer(Plantsurvival ~ salinityF*immersionF*SpecTF
>> +(1|rep/salinityF), data=sp.datos)
>>
>> # without replicate blocks (I'm not completely sure, if aov will report
>> the exact same results here)
>>
>> aovmodel <- aov(Plantsurvival ~ salinityF*immersionF*SpecTF +
>> Error(rep:salinityF), data=sp.datos)
>>
>> lmemodel <- lme(Plantsurvival ~ salinityF*immersionF*SpecTF, random=
>> ~1|rep:salinityF, data=sp.datos)
>>
>> lmermodel <- lmer(Plantsurvival ~ salinityF*immersionF*SpecTF
>> +(1|rep:salinityF), data=sp.datos)
>>
>>
>> As rep/salinityF is simply a short form for rep+rep:salinityF the
>> difference will be that you have no rep block effect in the models
>> without replicate blocks.
>>
>>
>> If you have no missing values, the point about using a mixed model here
>> is less about variance estimation through a random effect, but
>> automatically getting the right F-Test of the main-plot effect in ANOVA
>> (which is the main part of doing a proper split-plot analysis!!!).
>>
>> My experience is that the anova() function from the lmerTest package
>> will give you the right denominator df when using the Kenward-Roger
>> method. You need to use lmer for this:
>>
>> library(lmerTest)
>>
>> anova(lmermodel,ddf="Kenward-Roger")
>>
>> For proper mean comparisons, I suggest to use the emmeans package, with
>> the Tukey test and letter display from the cld() function:
>>
>> library(emmeans)
>>
>> cld(emmeans(lmermodel,"salinityF") #replace salinityF by the factor you
>> are interested
>>
>>
>> Finally, if you have one or more missing values, aov() will return
>> strange anova tables, thus better use a mixed model!
>>
>>
>> Best regards,
>>
>> Samuel
>>
>> --
>> Samuel Knapp
>>
>> Lehrstuhl f�r Pflanzenern�hrung
>> Technische Universit�t M�nchen
>> (Chair of Plant Nutrition
>> Technical University of Munich)
>>
>> Emil-Ramann-Strasse 2
>> D-85354 Freising
>>
>> Tel. +49 8161 71-3578
>> samuel.knapp at tum.de
>> www.researchgate.net/profile/Samuel_Knapp<http://www.researchgate.net/profile/Samuel_Knapp>
>> <http://www.researchgate.net/profile/Samuel_Knapp>
>>
>>
>>
>> On 03/04/18 19:16, r-sig-mixed-models-request at r-project.org wrote:
>>> Message: 1
>>> Date: Tue, 3 Apr 2018 07:53:15 +0000
>>> From: "CALLEJA APESTEGUI, FELIPE FRANCISCO"
>>> <felipe-francisco.calleja at alumnos.unican.es>
>>> To: "r-sig-mixed-models at r-project.org"
>>> ������� <r-sig-mixed-models at r-project.org>
>>> Subject: [R-sig-ME] Help establishing mixed model equation for split
>>> ������� plot design
>>> Message-ID:
>>>
>> <DB6P191MB0088D09AF911107B33C1DE5C87A50 at DB6P191MB0088.EURP191.PROD.OUTLOOK.COM>
>>>
>>> Content-Type: text/plain; charset="iso-8859-1"
>>>
>>> Hello,
>>>
>>>
>>> I'm looking for some help establishing a mixed model ANOVA using R,
>> for a split plot design I've made for an experiment of saltmarsh
>> germination. I'll explain as clear as possible the experimental design
>> and afterwards what I've done and my doubts. Hope you can help me. I
>> haven't worked very much with R so many of my doubts are about what
>> I'm "telling" it to do with one or other command.
>>>
>>>
>>> The experiment consists in meassuring the germination percentage of
>> one species in variable conditions of salinity, immersion time and
>> presence of other species. I sow seeds in soil core's, that are inside
>> plastic boxes that are filled periodically with saline water. There
>> are three factors: salinity (3 levels: 0, 5 and 18), immersion time (3
>> levels: 0, 20, 40%), species treatment (2 levels: Baccharis, Baccharis
>> + Juncus). Inside each box there are 6 cores that combine in a
>> complete random design the factors of immersion and species treatment.
>> The box is filled with water at one of the levels of salinity. Thus,
>> I'm using a split plot design with salinity as the whole plot factor,
>> and immersion and species treatment as the subplot factors. All
>> factors are considered fixed. Each box is repeated 5 times. Thus,
>> there are 15 boxes and 90 soil cores. The dependent variable is the
>> percentage of germination of the species Baccharis in each core.
>>>
>>>
>>> I have several doubts about how to analyze the array.
>>>
>>>
>>> 1 - As far as I understand, although I'm treating all my factors as
>> fixed, this is a mixed model because of the interaction between
>> subjects (the boxes I believe), and the "split plot nature" of the
>> array, right? In that sense, which function would be better to analyze
>> this, the aov of the stats package, the lme of the nlme package, the
>> lmer of lme4?
>>>
>>>
>>>
>>> �� 2 - I've had trouble calculating the degrees of freedom for the
>> residuals. The only reference I have is that the error of the whole
>> plot part should have 12 df's, and the within error should have 60
>> df's. With that reference I've established two possible R commands:
>>>
>>>
>>> fit.aov2 <- aov(Plantsurvival ~ salinityF*immersionF*SpecTF +
>> Error(rep:salinityF/immersionF:SpecTF), data=sp.datos)
>>>
>>>
>>> With rep being the number of repetition. This last one gives the 12
>> and 60 df's for the error terms.
>>>
>>>
>>> The other option is:
>>>
>>> fit.okay <- lme(Plantsurvival ~ salinityF*immersionF*SpecTF, random=
>> ~1|rep/salinityF, data=sp.datos)
>>>
>>> But in this last case, the df's are 8 and 60, which makes me suspect
>> maybe there is something wrong. But as I said, I haven't cleared my
>> head on which should be the correct df's.
>>>
>>>
>>> Questions: Is the aov line solving a mixed model adequate for my
>> design?,
>>>
>>>
>>>
>>> ���������������������� Is the lme line considering salinityF as a
>> random factor? If it is, how can I tell it to consider all factors as
>> fixed, but put salinity at the "higher level" of the whole plot and
>> the other ones in the "lower level" of the subplot?
>>>
>>>
>>> I hope the questions and the experimental array are clear. If there
>> is any doubt or need more information please let me know. I attach a
>> csv file with the data in case you want to see it.
>>>
>>>
>>> Finally, if is not too much to ask, I'm fairly new to the splitplot
>> anova's and R, so I would really appreciate if you could answer be
>> with as much detail as possible, to fully understand what's going on
>> and where to continue.
>>>
>>>
>>> Thanks a lot,
>>>
>>>
>>> Felipe Calleja Ap�stegui
>>>
>>> Predoctoral researcher
>>>
>>>
>>> Instituto de Hidr�ulica Ambiental "IH Cantabria"
>>>
>>> C/ Isabel Torres, N� 15
>>>
>>> Parque Cient�fico y Tecnol�gico de Cantabria
>>>
>>> 39011 Santander (Espa�a)
>>>
>>> www.ihcantabria.es<http://www.ihcantabria.es/>
>>>
>>> Tel:� +34 942 20 16 16 Ext. 1153
>>>
>>> Fax: +34 942 26 63 61
>>>
>>> e-mail: felipe-francisco.calleja at alumnos.unican.es
>>>
>>>
>>>
>>
>
>
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