R-sig-mixed-models Digest, Vol 143, Issue 33

Error means squares in GLMER and LMER (Kornbrot, Diana)

Have now got lmer in R ad MIXED in SPSS to agree for my data in s4 a 2 between, 2 within anova, iwht pno as random subjects and freq as dependent
You are all correct do not nee ls means directly BUT fa values have numerators and denominators and would really like to see both
Rscript
a<- lmer(freq~b1*b2*w1*w2+(w1|pno) + (w2|pno), data=s4)
anova(a)for inferential and ls_mean(a) for descriptive means.

want to go on to ?correct' analysis which has a binomial proprtion of freq/Nmax
so tried

 b<- glmer(cbind(freq, Nmax-freq) ~ b1*b2*w1*w2 +(w1|pno)+(w2|pno), data= s4,family="binomial" )
Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model failed to converge with max|grad| = 0.00692938 (tol = 0.001, component 1)

So, what can I change so it does converge. Its by no means a big data set

it was happy with print(b), summary(b) and anova(b) but results do not agree with SPSS, which did converge
BUT would not give me mea

ls_means(b)

Error in UseMethod("ls_means") :
  no applicable method for 'ls_means' applied to an object of class "c('glmerMod', 'merMod')"

So how does one get means from objects  of class "c('glmerMod', 'merMod?)??

Also when I try to look up glmer  in packages, it says it ca?t be installed in R3.51. although it must be installed to obey the command. Mystified

All help gratefully received
best
Diana

Message: 3
Date: Thu, 22 Nov 2018 17:09:56 +0000
From: "Kornbrot, Diana" <d.e.kornbrot at herts.ac.uk<mailto:d.e.kornbrot at herts.ac.uk>>
To: roee maor <roeemaor at gmail.com<mailto:roeemaor at gmail.com>>
Cc: "r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>"
<r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>>
Subject: Re: [R-sig-ME] Error means squares in GLMER and LMER
Message-ID: <8D3FBC0E-8BFD-4AC9-89BF-D71F6A408873 at herts.ac.uk<mailto:8D3FBC0E-8BFD-4AC9-89BF-D71F6A408873 at herts.ac.uk>>
Content-Type: text/plain; charset="utf-8"

So I am comparing standard ANOVA on raw  frequencies (or equivalently probabilities) with GLMM for  binomial  proportion with both  logit and probit   link
ALL analyses have  been completed in SPSS using MIXED for: response   = identity ,  link = normal ; response =  proportion=freq/Nmax, link =  probit; response = proportion link  = logit)
I want   to show  how to  do identical  analyses in R using lmer  for are freq and  glmer for proportions
So I wasn?t SAME results  from R and SPSS (and a  diamond  necklace for Christmas,  celebrated as  an EU citizen in UK - I am a demanding woman)
Results are NOT quite  the same.
I am  checking using  the raw  probabilities as raw response with lmer before  moving  on to  glmer  for proportions
Check 1, in SPSS for raw  freq or  probability REPEATED  give  same  result   as  MIXED (response = identity, link = normal). where  there are differences it is REPEATED WITHIN  comparisons  not MULTIVARIATE.
Check 2. Compare R,  lmer with SPSS  mixed
if repeated groups are w1,  w2,  etc  and between groups are b1,  b2  etc, I use:
result <- lmer(freq~b1*b2*w1*w2 + (w1|subject)  + (w2|subject),  data  =  test)
anova(result)
Fand df from R  and SPSS do  not always match,  even  when  they do  match on sums  of squares
I am trying  to  work out WHY there is a mismatch
Thought that  knowing what was in the DENOMINATOR of the F values - which i perhaps  wrongly termed  error sums  of squares, might help

I  want F  for usual reasons:   to test significance  and estimate  effect  size.
I also want  all  my packages  to  give  me the SAME F and df2 and to  UNDERSTAND what is happening

Sorry this is  so long,  but  hope it is now clearer

best

Diana

and as  an extra  treat  would like marginal means from object  of type lmer

Dear Diana,
If indeed what you're looking for is what Rolf mentioned, you might find Nakagawa & Schielzeth (2013) helpful.
It's titled "A general and simple method for obtaining R2 from generalized linear mixed-effects models". There is no dispute about the method's generality, but simple is a relative term...
Here's the link: https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/j.2041-210x.2012.00261.x

Hope this helps,
--
Roi Maor
PhD candidate
School of Zoology, Tel Aviv University
Centre for Biodiversity and Environment Research, UCL

_____________________________________
Professor Diana Kornbrot
Mobile
+44 (0) 7403 18 16 12
Work
University of Hertfordshire
College Lane, Hatfield, Hertfordshire AL10 9AB, UK
+44 (0) 170 728 4626
d.e.kornbrot at herts.ac.uk<mailto:d.e.kornbrot at herts.ac.uk><mailto:d.e.kornbrot at herts.ac.uk>
http://dianakornbrot.wordpress.com/
http://go.herts.ac.uk/Diana_Kornbrot
skype:  kornbrotme
Home
19 Elmhurst Avenue
London N2 0LT, UK
+44 (0) 208 444 2081
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------------------------------

Message: 4
Date: Thu, 22 Nov 2018 14:44:27 -0500
From: Ben Bolker <bbolker at gmail.com>
To: "Kornbrot, Diana" <d.e.kornbrot at herts.ac.uk>
Cc: roee maor <roeemaor at gmail.com>,  R SIG Mixed Models
<r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] Error means squares in GLMER and LMER
Message-ID:
<CABghstStwFkNHw1SU59ZPRPxAYBPxf4061pixfuVKhFrNnp8iA at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

For marginal means, use the emmeans package.

If you use the lmerTest package, you can get Satterthwaite or
Kenward-Roger df: you can use lme (from the nlme package), or
<https://github.com/bbolker/mixedmodels-misc/blob/master/R/calcDenDF.R>,
to get df via a simple "parameter-counting" exercise.

The problem is that the "F statistics" are quite poorly defined for
GLMMs. Can you show us the contrasting results you're getting for SPSS
and glmer?  Do you know how SPSS is computing the F statistics?  (This
<https://www.ibm.com/support/knowledgecenter/en/SS3RA7_15.0.0/com.ibm.spss.modeler.help/idh_glmm_build_options.htm>
makes it seem like it might be using Satterthwaite approximations ...)
On Thu, Nov 22, 2018 at 12:09 PM Kornbrot, Diana

So I am comparing standard ANOVA on raw  frequencies (or equivalently probabilities) with GLMM for  binomial  proportion with both  logit and probit   link
ALL analyses have  been completed in SPSS using MIXED for: response   = identity ,  link = normal ; response =  proportion=freq/Nmax, link =  probit; response = proportion link  = logit)
I want   to show  how to  do identical  analyses in R using lmer  for are freq and  glmer for proportions
So I wasn?t SAME results  from R and SPSS (and a  diamond  necklace for Christmas,  celebrated as  an EU citizen in UK - I am a demanding woman)
Results are NOT quite  the same.
I am  checking using  the raw  probabilities as raw response with lmer before  moving  on to  glmer  for proportions
Check 1, in SPSS for raw  freq or  probability REPEATED  give  same  result   as  MIXED (response = identity, link = normal). where  there are differences it is REPEATED WITHIN  comparisons  not MULTIVARIATE.
Check 2. Compare R,  lmer with SPSS  mixed
if repeated groups are w1,  w2,  etc  and between groups are b1,  b2  etc, I use:
result <- lmer(freq~b1*b2*w1*w2 + (w1|subject)  + (w2|subject),  data  =  test)
anova(result)
Fand df from R  and SPSS do  not always match,  even  when  they do  match on sums  of squares
I am trying  to  work out WHY there is a mismatch
Thought that  knowing what was in the DENOMINATOR of the F values - which i perhaps  wrongly termed  error sums  of squares, might help

I  want F  for usual reasons:   to test significance  and estimate  effect  size.
I also want  all  my packages  to  give  me the SAME F and df2 and to  UNDERSTAND what is happening

Sorry this is  so long,  but  hope it is now clearer

best

Diana

and as  an extra  treat  would like marginal means from object  of type lmer

Dear Diana,
If indeed what you're looking for is what Rolf mentioned, you might find Nakagawa & Schielzeth (2013) helpful.
It's titled "A general and simple method for obtaining R2 from generalized linear mixed-effects models". There is no dispute about the method's generality, but simple is a relative term...
Here's the link: https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/j.2041-210x.2012.00261.x

Hope this helps,
--
Roi Maor
PhD candidate
School of Zoology, Tel Aviv University
Centre for Biodiversity and Environment Research, UCL

_____________________________________
Professor Diana Kornbrot
Mobile
+44 (0) 7403 18 16 12
Work
University of Hertfordshire
College Lane, Hatfield, Hertfordshire AL10 9AB, UK
+44 (0) 170 728 4626
d.e.kornbrot at herts.ac.uk<mailto:d.e.kornbrot at herts.ac.uk>
http://dianakornbrot.wordpress.com/
http://go.herts.ac.uk/Diana_Kornbrot
skype:  kornbrotme
Home
19 Elmhurst Avenue
London N2 0LT, UK
+44 (0) 208 444 2081
------------------------------------------------------------





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------------------------------

Message: 5
Date: Thu, 22 Nov 2018 22:59:33 +0000
From: roee maor <roeemaor at gmail.com>
To: j.hadfield at ed.ac.uk
Cc: R-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME]  Unclear output from MCMCglmm with categorical
predictors
Message-ID:
<CACxNx6uqb0C3HCEpvOfKDF=pOKTzXjgZStFS-kdCibf1fgYuig at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

Dear Jarrod and Ben,
Thank you both for the advice.
Apologies if I accidentally caused anyone to receive an unexpected email.

Running the model specified above returns the error: "non-reduced nodes do
not appear first". The error persists when using a fully-bifurcating tree
so the issue is not tree polytomies.
I tried to go through the source code line-by-line to locate the problem
but got lost in the many loops and conditional statements. This error
message is conditioned on a match() output that also returns a warning that
the two arguments matched are unequal in length: one corresponds to the
tips while the other includes the tree's internal nodes as well.

As always, any help is much appreciated.
Best,
Roi

Hi,

You could upload the tarball to winbuilder (
https://win-builder.r-project.org/) and build a Windows source package.

Cheers,

Jarrod

Hi Jarrod,
Many thanks for your reply.

I couldn't install the tarball on R v3.4.3 or v3.5.1 so sourced the files
directly to the workspace.
I tried to run this model as you suggested:
T1 <- MCMCglmm(Activity ~ -1 + log(Mass) + Max.Temp * Annual.Precip,
              random = ~ animal,
              prior = list(R = list(fix=1, V=1e-15), G = list(G1 =
list(V=1, nu=0.002))),
              pedigree = datatree,
              reduced = TRUE,
              burnin = 50000, nitt = 750001, thin = 700,
              family = "threshold",
              data = Rdata,
              pl = TRUE, saveX = TRUE, saveZ = TRUE,
              verbose = TRUE)

It returns some errors about missing functions "is.positive.definite" and
"Matrix", which I addressed with:
library("corpcor", lib.loc="~/R/win-library/3.5")
library("MatrixModels", lib.loc="~/R/win-library/3.5")
but I can't figure this one out:
'Error in .C("MCMCglmm", as.double(data$MCMC_y),
as.double(data$MCMC_y.additional),  :
 C symbol name "MCMCglmm" not in load table'
Detaching these packages doesn't necessarily cause that same error to
appear although I execute the exact same code.
Also, several attempts (same code again) caused a fatal error and
automatic session termination (info for a similar session below if
interesting).

I tried to use the fully bifurcating tree as an experiment but that made
no difference.

Any ideas what this last error means?

Thanks!
Roi


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

Matrix products: default

locale:
[1] LC_COLLATE=English_United Kingdom.1252  LC_CTYPE=English_United
Kingdom.1252    LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C                            LC_TIME=English_United
Kingdom.1252

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] corpcor_1.6.9   Matrix_1.2-14   phytools_0.6-60 maps_3.3.0
ape_5.2

loaded via a namespace (and not attached):
[1] igraph_1.2.2            Rcpp_0.12.19            magrittr_1.5
 MASS_7.3-50             mnormt_1.5-5
[6] scatterplot3d_0.3-41    lattice_0.20-35         quadprog_1.5-5
 fastmatch_1.1-0         tools_3.5.1
[11] parallel_3.5.1          grid_3.5.1              nlme_3.1-137
 clusterGeneration_1.3.4 phangorn_2.4.0
[16] plotrix_3.7-4           coda_0.19-2             yaml_2.2.0
 numDeriv_2016.8-1       animation_2.5
[21] compiler_3.5.1          combinat_0.0-8          expm_0.999-3
 pkgconfig_2.0.2

Hi,

Most likely the phylogenetic heritability (the phylogenetic variance /
the phylogenetic +residual variance) is approaching one resulting in
numerical difficulties. Probably the best thing is to assume that the
phylogenetic heritability equals 1 and use the reduced phylogenetic mixed
model implementation. This allows the phylogenetic heritability to be equal
to 1 without causing numerical issues. At some point I will integrate these
models into the main MCMCglmm package, but for now you can download it from
here: http://jarrod.bio.ed.ac.uk/MCMCglmmRAM_2.24.tar.gz.

Change the name of the ??Binomial? column to ?animal? and fit:

T1 <- MCMCglmm(Activity ~ -1 + log(Mass) + Max.Temp * Annual.Precip,
                              random = ~ animal
                              prior = list(R = list(fix=1, V=1e-15), G =
list(G1 = list(V=1, nu=0.002))),
                              pedigree = tree,
      reduced=TRUE,
                              burnin = 150000, nitt = 2650001, thin =
2500,
                              family = "threshold",  data = Tdata,
                              pl = TRUE, pr = TRUE, saveX = TRUE, saveZ
= TRUE,
                              verbose = FALSE)

You should need fewer iterations.

Cheers,

Jarrod

Dear Jarrod (and list),

Following your previous comment I added "random = ~ Binomial" to my model
to allow for a phylogenetic analysis.
This causes convergence problems: the trace plots show increasing
oscillations along each chain (although no directional trends, so it's not
a burn-in issue). Also, the posterior samples are highly correlated,
residual variance estimates are >10^3 and threshold estimates are high (>20
on the latent scale).
Surprisingly (to me), predictors that are strongly significant in the
non-phylogenetic model lose their effect in the phylogenetic model (I tried
several alternative parameter configurations).

It seems that this model attributes the explained variance to phylogeny
alone.
Can anyone explain what is going on here?  Am I specifying the model
poorly or just asking my data more than it can answer?

I tried to overcome this issue by using a fully resolved variant of the
phylogeny, which only improved things slightly.
I also changed the random effect to "random=~Family" or "random=~Order",
which reduced the variance and threshold estimates to more acceptable
levels (<10), but still no significant predictors (and I'm not sure how the
algorithm calculates covariance between higher taxa in the phylogeny).
Separately I tried parameter expanded prior: "prior = list(R = list(V=1,
fix=1), G = list(G1 = list(V=1, nu=1, alpha.mu=0, alpha.V=1000)))". That
didn't help, and messing with priors for this reason feels like poor
practice.

This is the model:
T1 <- MCMCglmm(Activity ~ -1 + log(Mass) + Max.Temp * Annual.Precip,
                              random = ~ Binomial,
                              prior = list(R = list(fix=1, V=1), G =
list(G1 = list(V=1, nu=0.002))),
                              ginverse = list(Binomial=INphylo$Ainv),
                              burnin = 150000, nitt = 2650001, thin =
2500,
                              family = "threshold",  data = Tdata,
                              pl = TRUE, pr = TRUE, saveX = TRUE, saveZ
= TRUE,
                              verbose = FALSE)

The data I use looks like this (not all variables appear in each model):

str(Tdata)
'data.frame': 1389 obs. of  10 variables:
$ Binomial           : Factor w/ 1421 levels "Abrocoma_bennettii",..: 1
2 3 4 5 6 7 8 9 10 ...
$ Order                : Factor w/ 27 levels "Afrosoricida",..: 24 24 24
24 24 3 24 24 2 2 ...
$ Family               : Factor w/ 126 levels "Abrocomidae",..: 1 26 26
26 26 46 74 87 10 10 ...
$ Activity              : Factor w/ 3 levels "1","2","3": 1 3 2 2 2 3 2
3 1 3 ...
$ Habitat              : Factor w/ 6 levels "Aqua","Arbo",..: 5 5 5 5 5
5 5 5 5 5 ...
$ Diet                   : Factor w/ 3 levels "Faun","Herb",..: 2 3 3 3
3 1 3 2 2 2 ...
$ Mass                 : num  250.5 24.9 34.5 38.9 24.5 ...
$ Max.Temp         : num  22 16.6 19.1 19.8 17.2 ...
$ Annual.Precip   : num  166 645 558 903 1665 ...

Any advice would be much appreciated!
Many thanks,


--
Roi Maor
PhD candidate
School of Zoology, Tel Aviv University
Centre for Biodiversity and Environment Research, UCL


The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.



--
Roi Maor
PhD candidate
School of Zoology, Tel Aviv University
Centre for Biodiversity and Environment Research, UCL

The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.



--
Roi Maor
PhD candidate
School of Zoology, Tel Aviv University
Centre for Biodiversity and Environment Research, UCL





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End of R-sig-mixed-models Digest, Vol 143, Issue 33
***************************************************

_____________________________________
Professor Diana Kornbrot
Mobile
+44 (0) 7403 18 16 12
Work
University of Hertfordshire
College Lane, Hatfield, Hertfordshire AL10 9AB, UK
+44 (0) 170 728 4626
d.e.kornbrot at herts.ac.uk<mailto:d.e.kornbrot at herts.ac.uk>
http://dianakornbrot.wordpress.com/
http://go.herts.ac.uk/Diana_Kornbrot
skype:  kornbrotme
Home
19 Elmhurst Avenue
London N2 0LT, UK
+44 (0) 208 444 2081
 ------------------------------------------------------------