[R-sig-ME] [R] glmer with non integer weights
ONKELINX, Thierry
Thierry.ONKELINX at inbo.be
Tue Apr 13 10:24:30 CEST 2010
Dear Kay,
There is a R list about mixed models. Which is a better place for your
questions.
The (quasi)binomial family is used with binary data or a ratio that
originates from binary data. In case of a ratio you need to provide the
number of trials through the weights argument.
Further I would suggest to drop stage from either the random effects or
the fixed effects. You are trying to estimate the same effect twice in a
model.
HTH,
Thierry
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium
Research Institute for Nature and Forest
team Biometrics & Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
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
> -----Oorspronkelijk bericht-----
> Van: r-help-bounces at r-project.org
> [mailto:r-help-bounces at r-project.org] Namens Kay Cichini
> Verzonden: maandag 12 april 2010 16:12
> Aan: r-help at r-project.org
> Onderwerp: [R] glmer with non integer weights
>
>
> hello,
>
> i'd appreciate help with my glmer.
> i have a dependent which is an index (MH.index) ranging from
> 0-1. this index can also be considered as a propability. as i
> have a fixed factor (stage) and a nested random factor (site)
> i tried to model with glmer. i read that it's possible to use
> a quasibinomial distribution, for this kind of data, which i
> than actually did - but firstly
>
> (1) i'm not quite sure if that's appropiate for my data, secondly
> (2) i wondered if the model can be correct when variance of
> then main and nested factor are zero.
> (3) also i could not yield p-values for that model.
>
> here's data, call and output:
>
> ##########################################################
> #call:
> ##########################################################
>
> glmer(MH~stage+(1|stage/site),family=quasibinomial)
>
> ##########################################################
> #output:
> ##########################################################
> #Generalized linear mixed model fit by the Laplace approximation
> #Formula: MH ~ stage + (1 | stage/site)
> # AIC BIC logLik deviance
> # 66.03 86.47 -26.01 52.03
> #Random effects:
> # Groups Name Variance Std.Dev.
> # site:stage (Intercept) 0.000000 0.000
> # stage (Intercept) 0.000000 0.000
> # Residual 0.076175 0.276
> # Number of obs: 137, groups: site:stage, 39; stage, 4
>
> #Fixed effects:
> # Estimate Std. Error t value
> #(Intercept) 0.39205 0.09009 4.352
> #stageB -0.87214 0.12498 -6.978
> #stageC -0.36153 0.12202 -2.963
> #stageD -0.09884 0.19811 -0.499
>
> #Correlation of Fixed Effects:
> # (Intr) stageB stageC
> #stageB -0.721
> #stageC -0.738 0.532
> #stageD -0.455 0.328 0.336
> ##########################################################
> #my data:
> ##########################################################
> similarity<-data.frame(list(structure(list(stage =
> structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L), .Label = c("A", "B", "C", "D"), class =
> "factor"), site = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
> 3L, 3L, 4L, 4L, 4L, 4L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 8L,
> 8L, 8L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 11L, 11L,
> 12L, 12L, 12L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 15L, 15L,
> 15L, 15L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 18L, 18L, 19L,
> 19L, 19L, 19L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 22L,
> 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 25L, 25L,
> 25L, 25L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 28L, 28L,
> 28L, 28L, 29L, 29L, 29L, 30L, 30L, 30L, 30L, 31L, 31L, 32L,
> 32L, 32L, 32L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 35L,
> 35L, 35L, 35L, 36L, 36L, 36L, 36L, 37L, 37L, 38L, 38L, 38L,
> 38L, 39L, 39L, 39L ), .Label = c("A11", "A12", "A14", "A15",
> "A16", "A17", "A18", "A19", "A20", "A5", "A7", "A8", "B1",
> "B12", "B13", "B14", "B15", "B17", "B18", "B2", "B4", "B7",
> "B8", "B9", "C1", "C10", "C11", "C15", "C17", "C18", "C19",
> "C2", "C20", "C3", "C4", "C6", "D1", "D4", "D7"), class =
> "factor"), MH.Index = c(0.392156863, 0.602434077,
> 0.576923077, 0.647482014, 0.989010989, 0.857142857, 1, 1, 1,
> 0, 1, 0.378378378, 0.839087948, 0.252915554, 1, 0.22556391,
> 0.510366826, 0.476190476, 0.555819477, 0.961538462,
> 0.666666667, 0.089285714, 0.923076923, 0.571428571, 0,
> 0.923076923, 0.617647059, 0.599423631, 0, 0.727272727,
> 0.998112812, 0, 0, 0, 1, 0.565656566, 0.75, 0.923076923,
> 0.654545455, 0.14084507, 0.617647059, 0.315789474,
> 0.179347826, 0.583468021, 0.165525114, 0.817438692,
> 0.455551457, 0.49548886, 0.556127703, 0.707431246,
> 0.506757551, 0.689655172, 0.241433511, 0.379232506,
> 0.241935484, 0, 0.30848329, 0.530973451, 0.148148148, 0,
> 0.976744186, 0.550218341, 0.542168675, 0.769230769,
> 0.153310105, 0, 0, 0.380569406, 0.742174733, 0.222222222,
> 0.046925432, 0, 0.068076328, 0.772727273, 0.830039526,
> 0.503458415, 0.863910822, 0.39401263, 0.081818182,
> 0.368421053, 0.088607595, 0, 0.575499851, 0.605657238,
> 0.714854232, 0.855881172, 0.815689401, 0.552207228,
> 0.81708081, 0.583228133, 0.334466349, 0.259477365,
> 0.194711538, 0.278916707, 0.636304805, 0.593715432,
> 0.661016949, 0.626865672, 0.420219245, 0.453535143,
> 0.471243706, 0.462427746, 0.56980057, 0.453821155,
> 0.052828527, 0.926829268, 0.51988266, 0.472200264,
> 0.351219512, 0.290030211, 0.765258974, 0.564894108,
> 0.789699571, 0.863378215, 0.525181559, 0.803061458,
> 0.260164645, 0.477265792, 0.265889379, 0.317791411,
> 0.107623318, 0.279181709, 0.471953363, 0.463724265,
> 0.241966696, 0.403647213, 0.693087992, 0.494259925,
> 0.68904453, 0.39329147, 0.498161213, 0.376225983,
> 0.407001046, 0.825016633, 0.718991658, 0.662995912)), .Names
> = c("stage", "site", "MH.Index"), class = "data.frame",
> row.names = c(NA,
> -136L))))
> ##########################################################
> --
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> 1837179.html
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