[R-sig-ME] lme4

[Dan Selechnik]

Hi Ben,

Thank you very much for the reply, but I'm still not sure what exactly is
the fix here? Every sample has a single ID. Is this my issue...?

Cheers,
Dan

On Sat, Jul 15, 2017 at 12:10 PM, Ben Bolker <bbolker at gmail.com> wrote:

>
>
> On 17-07-14 09:26 PM, Dan Selechnik wrote:
> > Hello,
> >
> > My name is Dan and I'm a PhD student in Australia. I was hoping that I
> > could ask you for some help with using lme4. I have a dataset in which I
> > have PC1 as a response variable. Population, treatment, RBC, and
> > population*treatment are my explanatory variables. ID is a random factor.
> > (I have attached the CSV file here)...
> >
> > I am trying to run a power analysis, and first to fit my data using lmer.
> >
> > First I read my data into R:
> > pc1=read.csv("R-PowerAnalysis.csv", header=TRUE)
> >
> > Then I attempt to fit:
> > fm1=lmer(pc1$PC1 ~ pc1$RBC + pc1$Population + pc1$Treatment +
> > pc1$Population*pc1$Treatment + (1|pc1$ID), data=pc1, REML=FALSE)
>
>    A small point, but in general you should *not* use pc1$ in specifying
> your formula: instead,
>
> fm1=lmer(PC1 ~ RBC + Population*Treatment + (1|ID), data=pc1,
>     REML=FALSE)
>
> (also, the * operator includes both the main effects of Population and
> Treatment and their interaction). But that should be tangential to your
> problem.
>
> >
> > However, this fails, returning the message:
> > Error: number of levels of each grouping factor must be < number of
> > observations
> >
> > My number of populations and treatments is much less than my number of
> > observations, so I am not sure why I am getting this error...
>
>   That's not your problem.  lme4 is referring to the number of levels of
> the *grouping factor*, which is ID (not Population or Treatment).  Your
> ID variable must contain a single observation per group (cheating and
> looking at the data you sent me offline, I can see that's true).
>
> If you had sent the results of summary(pc1), we could have guessed this:
> ID is coded as an integer so we don't know for sure that it consists of
> the values 1..20, but since the min is 1 and the max is 20 and mean is
> 10.5, we can guess that that's the case ...
>
>        ID        Population         Treatment       RBC
>  Min.   : 1.00   QLD:10     LPS-Injection:10   Min.   :-113.00
>  1st Qu.: 5.75   WA :10     PBS-Injection:10   1st Qu.:  22.00
>  Median :10.50                                 Median :  49.00
>  Mean   :10.50                                 Mean   :  53.55
>  3rd Qu.:15.25                                 3rd Qu.: 107.00
>  Max.   :20.00                                 Max.   : 181.00
>       PC1
>  Min.   :-4.5411
>  1st Qu.: 0.1017
>  Median : 1.1470
>  Mean   : 0.6258
>  3rd Qu.: 1.9251
>  Max.   : 3.2004
>
>
> Also, when I
> > run this, it works fine:
> > fm1=lm(pc1$PC1 ~ pc1$RBC + pc1$Population + pc1$Treatment +
> > pc1$Population*pc1$Treatment + (1|pc1$ID), data=pc1)
>
> If you look at the results of this model:
>
> Coefficients:
>                         (Intercept)                                  RBC
>                             1.47759                              0.01318
>                        PopulationWA               TreatmentPBS-Injection
>                            -1.38234                             -1.85619
>                          1 | IDTRUE  PopulationWA:TreatmentPBS-Injection
>                                  NA                              0.24754
>
> you can see that something funny is happening to the (1|ID) term ...
>
> >
> > I was hoping I could ask for your assistance in figuring out what may be
> > the problem. Thank you very much.
> >
> > Cheers,
> > Dan
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>
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