danselechnik at gmail.com
Tue Jul 18 01:33:33 CEST 2017
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...?
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,
> (also, the * operator includes both the main effects of Population and
> Treatment and their interaction). But that should be tangential to your
> > 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
> 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:
> (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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