Thanks Thierry,
Sorry about my question, I start to use the mixed models approach
recently. My structure of data was:
'data.frame': 288 obs. of 13 variables:
$ Transecto : int 2 2 2 2 2 2 3 3 3 3 ...
$ Ponto : int 1 2 3 4 5 6 1 2 3 4 ...
$ Distancia : int 160 120 80 40 20 0 160 120 80 40 ...
$ tipo_trat : Factor w/ 4 levels "","controle",..: 3 3 3 3 3 3 4 4 4
4 ...
$ umid_inici : num 81.3 84.1 81.3 83.9 81.9 ...
$ umid_final : num 63.7 68 66.2 66.8 66.4 ...
$ temp_inici : num 19.1 19.5 19.5 19.1 19.1 ...
$ temp_final : num 29.1 27.8 27.6 28 28.6 ...
$ abertu_dossel : num 35.6 20.8 28.9 30.6 27.1 ...
$ delta.umidade : num -17.6 -16.1 -15.2 -17.1 -15.5 ...
$ delta.temperatura: num 9.95 8.3 8.1 8.91 9.5 ...
$ remocao : num 0.02 0 0.1 0 0.08 0 1 0.04 0.08 0.42 ...
$ riqueza : int 3 9 3 3 4 5 4 3 2 5 ...
And the summary of model below. In my case is a adjustment problem or in
r.squaredGLMM() function?
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [
glmerMod]
Family: poisson ( log )
Formula: riqueza ~ tipo_trat + temp_final + temp_inici + umid_inici +
umid_final + (1 | Ponto)
Data: d1
Control:
glmerControl(check.conv.singular = "warning", optCtrl = list(maxfun =
1e+05))
AIC BIC logLik deviance df.resid
326.1 344.3 -155.0 310.1 64
Scaled residuals:
Min 1Q Median 3Q Max
-1.8416 -0.7947 -0.2221 0.7253 2.4622
Random effects:
Groups Name Variance Std.Dev.
Ponto (Intercept) 5.293e-17 7.275e-09
Number of obs: 72, groups: Ponto, 6
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.18941 3.18615 -1.629 0.1034
tipo_trattrat_euc -0.51209 0.29360 -1.744 0.0811 .
tipo_trattrat_mat_euc -0.43877 0.25986 -1.688 0.0913 .
temp_final 0.06914 0.04144 1.669 0.0952 .
temp_inici 0.07176 0.05479 1.310 0.1903
umid_inici 0.03270 0.02543 1.286 0.1985
umid_final 0.01774 0.01660 1.069 0.2850
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Correlation of Fixed Effects:
(Intr) tp_tr_ tp_t__ tmp_fn tmp_nc umd_nc
tp_trttrt_c 0.457
tp_trttrt__ 0.525 0.358
temp_final -0.546 -0.393 -0.143
temp_inici -0.750 -0.557 -0.696 0.037
umid_inici -0.755 0.028 -0.522 -0.020 0.667
umid_final -0.287 -0.661 0.236 0.651 0.023 -0.352
convergence code: 0
Model failed to converge with max|grad| = 0.00894145 (tol = 0.001, component
1)
singular fit
Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?
Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?
--
======================================================================
Alexandre dos Santos
Prote??o Florestal
IFMT - Instituto Federal de Educa??o, Ci?ncia e Tecnologia de Mato Grosso
Campus C?ceres
Caixa Postal 244
Avenida dos Ramires, s/n
Bairro: Distrito Industrial
C?ceres - MT CEP: 78.200-000
Fone: (+55) 65 99686-6970 (VIVO) (+55) 65 3221-2674 (FIXO)
e-mails:alexandresantosbr at yahoo.com.br
alexandre.santos at cas.ifmt.edu.br
Lattes: http://lattes.cnpq.br/1360403201088680
OrcID: orcid.org/0000-0001-8232-6722
Researchgate: www.researchgate.net/profile/Alexandre_Santos10
LinkedIn: br.linkedin.com/in/alexandre-dos-santos-87961635
Mendeley:www.mendeley.com/profiles/alexandre-dos-santos6/
======================================================================
Em 06/02/2018 06:15, Thierry Onkelinx escreveu:
Dear Alexandre,
First of all you need to get a stable model. Otherwise any number you
get from it is meaningless. Can you provide more detail on your model.
E.g. summary(mT), str(d1), ...
Best regards,
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
AND FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be
///////////////////////////////////////////////////////////////////////////////////////////
To call in the statistician after the experiment is done may be no
more than asking him to perform a post-mortem examination: he may be
able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does
not ensure that a reasonable answer can be extracted from a given body
of data. ~ John Tukey
///////////////////////////////////////////////////////////////////////////////////////////
2018-02-05 20:15 GMT+01:00 ASANTOS via R-sig-mixed-models
<r-sig-mixed-models at r-project.org>:
Dear Mix Models Members,
I try to extract R^2 for linear mixed effects models with
glmer() function with poisson distribution using r.squaredGLMM() in
MuMIn package, but doesn't work. My output always show:
#Model ajusted > mT <-glmer(riqueza ~tipo_trat+(1|Ponto),data=d1, +
family=poisson, control = glmerControl(check.conv.singular =
"warning",optCtrl = list(maxfun=100000))) Warning messages: 1: In
checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :Model
failed to converge with max|grad| = 0.00894145 (tol = 0.001, component
1) 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl =
control$checkConv, :singular fit 3: In checkConv(attr(opt, "derivs"),
opt$par, ctrl = control$checkConv, :Model is nearly unidentifiable: very
large eigenvalue - Rescale variables?;Model is nearly unidentifiable:
large eigenvalue ratio - Rescale variables? #R^2 conditional and
marginal > r.squaredGLMM(mT) Error in glmer(formula = riqueza ~
tipo_trat + temp_final + temp_inici + : fitting model with the
observation-level random effect term failed. Add the term manually In
addition: Warning message: In value[[3L]](cond) :(p <- ncol(X)) ==
ncol(Y) is not TRUE
I change almost all parameters indicating by web posts like
glmerControl, maxfun, etc. There are other approaches to calculate the
conditional and marginal R^2 for my model with lme4 package?
Thanks in advance,
Alexandre
--
======================================================================
Alexandre dos Santos
Prote??o Florestal
IFMT - Instituto Federal de Educa??o, Ci?ncia e Tecnologia de Mato Grosso
Campus C?ceres
Caixa Postal 244
Avenida dos Ramires, s/n
Bairro: Distrito Industrial
C?ceres - MT CEP: 78.200-000
Fone: (+55) 65 99686-6970 (VIVO) (+55) 65 3221-2674 (FIXO)
alexandre.santos at cas.ifmt.edu.br
Lattes:http://lattes.cnpq.br/1360403201088680
OrcID: orcid.org/0000-0001-8232-6722
Researchgate:www.researchgate.net/profile/Alexandre_Santos10
LinkedIn: br.linkedin.com/in/alexandre-dos-santos-87961635
Mendeley:www.mendeley.com/profiles/alexandre-dos-santos6/
======================================================================
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