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
have PC1 as a response variable. Population, treatment, RBC, and
population*treatment are my explanatory variables. ID is a random
(I have attached the CSV file here)...
I am trying to run a power analysis, and first to fit my data using
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
the problem. Thank you very much.
Cheers,
Dan