Reputation: 10619
I have $m=4$ group of mice (i.e group1, group2, group3, group4). Each group has a different number of mice. I measure a parameter $(y)$ on each mice of each group at $l=4$ different states (i.e state1, state2, state3, state4). I would like to build a mixed effect model to analyse the effect of group, state and group*state, allowing for the variability within each mouse and within each group.The mice within $group_{m}$ are labeled with an id (1,2,3...,number of mice of $group_{m}$)
$$y_{mln}=\mu +group_{m} +state_{l} +(group*state){ml}+b{ml}+\varepsilon_{mln} $$ with $b_{ml}$ the random effect for the nth mouse within $group_{m}$
My data frame has the following variables
value (num)
state (factor: 4 levels)
group (factor: 4 levels)
id (within group) (num)
Is the corresponding syntax correct?
lmer(value~group+state+group*state+(1|group))
Upvotes: 2
Views: 3292
Reputation: 18487
You want this
mouseID <- interaction(group, ID)
lmer(value ~ group * state + (1|mouseID))
The mouseID must be unique for each mouse.
Since group is a factor, you can't have it both in the fixed and the random part. That would lead to an unidentifiable model.
Upvotes: 3
Reputation: 5341
I think what you're looking for is
lmer(value ~ group*state + (1|group) + (1|id))
This model estimates the fixed effect of group and state as well as the interaction between them (R
automatically expands group*state
to group + state + group*state
) and estimates a random intercept for the effect of each group and for each mouse.
Upvotes: 0