[R-sig-ME] GLMM - Prediction

[Cleber Iack]

Thank you very much,

I tested, really is a bug.

God bless you.

2018-03-04 19:11 GMT+00:00 Ben Bolker <bbolker at gmail.com>:

> This looks like a bug in lme4: https://github.com/lme4/lme4/issues/457
> . A simple workaround is to specify some value for `ID`
> (which will be ignored).
>
> On Sat, Mar 3, 2018 at 7:06 PM, Cleber Iack <profiack at gmail.com> wrote:
> > Dear
> >
> > First, thank you very much for your time in answering.
> >
> > I'm sending a code that I tried not to inform the ID and I couldn't,
> >
> > Displays the following error at the end
> >
> > "Error in eval (predvars, date, env): object ID not found"
> >
> > I'd appreciate it if you could give me some guidance
> >
> > Thanks
> > ####
> >
> > library(plyr)
> >
> > library(dplyr)
> >
> > library(lme4)
> >
> > n = 300
> >
> > xx<-c("r1", "r2", "r3", "r4", "r5")
> >
> > xxx<-c("e1", "e2", "e3")
> >
> > p=0.3
> >
> > School = factor(sample(xxx, n, replace=TRUE), levels=xxx, ordered=FALSE)
> >
> > Rank = factor(sample(xx, n, replace=TRUE), levels=xx, ordered=FALSE)
> >
> >
> >
> > df1 <- data_frame(
> >
> >   ID = as.integer(runif(n, min = 1, max = n/7)),
> >
> >   xx1 = runif(n, min = 0, max = 10),
> >
> >   xx2 = runif(n, min = 0, max = 10),
> >
> >   xx3 = runif(n, min = 0, max = 10),
> >
> >   School = School,
> >
> >   Rank = Rank,
> >
> >   yx = as.factor(rbinom(n, size = 1, prob = p))
> >
> > )
> >
> > df1 = df1[order(df1$ID, decreasing=FALSE),]
> >
> > library(lme4)
> >
> > mm2 <- glmer(yx ~ xx1 + xx2 + xx3 + Rank +  (1 | ID) + (1 | School /
> Rank),
> > data = df1,
> >
> >              family = "binomial",control = glmerControl(calc.derivs =
> > FALSE))
> >
> >
> >
> > n11 <-  data.frame(School=factor("e1", levels =
> > levels(df1$School),ordered=FALSE),
> >
> >                    Rank=factor("r1", levels =
> > levels(df1$Rank),ordered=FALSE),
> >
> >                    xx1=8.58, xx2=8.75, xx3=7.92)
> >
> > predict(mm2, n11, type="response",re.form= ~(1 | School / Rank))
> >
> > ##
> >
> >
> > 2018-03-02 9:46 GMT+00:00 Thierry Onkelinx <thierry.onkelinx at inbo.be>:
> >
> >> Dear Iack,
> >>
> >> Look at the re.form argument of ?lme4::predict.merMod. This allows you
> the
> >> make predictions without the ID random effect.
> >>
> >> 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
> >> <https://maps.google.com/?q=Havenlaan+88&entry=gmail&source=g> 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
> >> ////////////////////////////////////////////////////////////
> >> ///////////////////////////////
> >>
> >> <https://www.inbo.be>
> >>
> >> 2018-03-02 1:09 GMT+01:00 Cleber Iack <profiack at gmail.com>:
> >>
> >>> Dear,
> >>>
> >>> I have n different companies with different characteristics and each
> >>> company of this has the same positions
> >>> I selected data from several members of each position of each Company,
> and
> >>> there were behavioral variables ranging from 0 to 10, which to
> facilitate
> >>> I
> >>> will call x1, x2, x3, these variables were collected more than once for
> >>> each person.
> >>>
> >>> The objective is to predict the probability of occurrence of a fact,
> and
> >>> each person was also noted if this fact occurred or not. (0 or 1)
> >>>
> >>> Using
> >>>
> >>> formMod1= fato~  x1+x2+x3+(1 | company / position)
> >>> Mod1 <- glmer( formMod1 , data = dadosord , family = binomial,
> >>>                      control = glmerControl(optimizer="bobyqa"))
> >>>
> >>> This model I can make a prediction without "problems"
> >>>
> >>> n11 <-  data.frame(company =factor("M1", levels =
> >>> levels(dadosord$company ),ordered=FALSE),
> >>>                    position=factor("P1", levels =
> >>> levels(dadosord$position),ordered=FALSE),
> >>>                    x1=1, x2=7,x3=7)
> >>> predict( Mod1 , n11, type="response")
> >>>
> >>> But I was worried because I have more than one observation of the
> >>> characteristics per individual, if I would not have to put it also as
> >>> random
> >>> or somehow analyze this structure of possible correlation
> >>>
> >>> Thinking about the first case I did
> >>>
> >>> formMod2= fato~  x1+x2+x3+(1 | company / position) + (1 | ID)
> >>> Mod2 <- glmer( formMod2 , data = dadosord , family = binomial,
> >>>                      control = glmerControl(optimizer="bobyqa"))
> >>>
> >>> But in this case when trying to predict the probability of the fact,
> the
> >>> function "predict" asks me who is the proposed id, which is out of
> >>> interest, since I must study for a random person who has the same
> features
> >>> x1, x2, x3, company and position.
> >>>
> >>> And the second form, if any, I do not know how I would do
> >>>
> >>> Thanks for the help of my friends.
> >>>
> >>> Iack
> >>>
> >>>         [[alternative HTML version deleted]]
> >>>
> >>> _______________________________________________
> >>> R-sig-mixed-models at r-project.org mailing list
> >>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>
> >>
> >>
> >
> >         [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

	[[alternative HTML version deleted]]