I am new with R and I am trying to use multilevel modelling for my dataset using the function
glmer (for a binomial outcome variable) and
lmer for a continuous one.
I have 4 experimental groups (
Treatments) and in each group I am measuring the same outcome variables 4 time for each individual,
Conc is binomial (0/1) while
PosQ is continuous
I am treating the variables as:
Conc =Factor with 2 levels,
Treatment = Factor with 4 levels,
PosQ = Integer (no decimal values) Here is an example of my datafile
ID Time Conc Treatm PosQ 1 1 1 1 6 1 2 1 1 12 1 3 1 1 14 1 4 1 1 15 2 1 0 3 20 2 2 0 3 12 2 3 0 3 8 2 4 0 3 6
It is a 3 level repeated measure design and I want to test the effects of
Time on my outcome variables.
The variables are nested and not crossed, each individual belong only to 1 of the 4 experimental groups and I made 4 measurement on each individual.
So from the furthest to the nearest Treatment is nested with
ID that is nested with
Time (express the repeated measurement)
I want to measure the interaction effect of
Treatment (I Expect them to be better at the end of the 4 measurement according to the treatment group they belong to and to the pass of time).
I am using multilevel model because I want to take into consideration also individual differences into account
Here are the formulas I was using:
BinomialOutcomeVariable <- glmer(Conc ~ Treatment * Time + (1| Treatm) + (1|Treatm:ID) + (1|Treatm/ID/Time), data = analyses.4, family = binomial(link="logit")) ContinuousVariable <- lmer(PosQ ~ Treatm * Sequ + (1| Treatm) + (1|Treatm:ID) + (1|Treatm/ID/Time), data = analyses.4)
Are those formulas correct? Can be reduced? because when I run the analyses for continuous variable I receive this warning:
number of levels of each grouping factor must be < number of observations
grouping factors with < 5 sampled levels may give unreliable estimates
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?
Instead if I use this formula I have no problems for only 1 of the continuous variables
model <- lmer(PosQ ~ Treatm * Time + (1| Treatm/ID), data = analyses.4)
Is the same formula as before?
Does R understand that
Time is nested?
If I use
isNested function it says that they are not nested.
Any help, idea, suggestion is really appreciated.
Thanks in advance