- Operating System: Windows 10
- brms Version:
I am currently trying to fit a multilevel non-linear model, which uses paired, repeated measures data (data of patients before and after a medical procedure, in each case several measurements per patient are taken).
I read some of the docs provided on brms, but still have some doubts as to whether I am getting the parameter declaration right.
I am fitting the following model:
bf(LDL ~ 15 + (alpha-15) / (1 + exp ((gamma-Time) / delta)),
alpha ~ 1 + (1|Sample)+(1|Cat),
gamma ~ 1 + (1|Sample)+(1|Cat),
delta ~ 1 + (1|Sample)+(1|Cat),
nl = TRUE),
data = ldlData, family = gaussian(),
chains = 2,
warmup = 1000,
cores = getOption(“mc.cores”,1L),
control = list(adapt_delta = 0.999)
As seen from the code, the parameters are alpha, gamma, and delta.
Let us take alpha, for example:
alpha ~ 1 + (1|Sample)+(1|Cat) (1)
In this case, does this mean that alpha has a fixed effect, represented by the first 1 in the expression and two independent group effects (alpha varies per Sample and per Category - Before/After procedure).
In addition, if I want to write a model, in which Sample is influenced by Category,
I think I should use
alpha ~ 1 + (1|Cat/Sample) = 1 + (1|Cat) + (1|Cat:Sample) (2)
In case two is correct, can someone confirm what exactly the intuition behind it is -
the way I see it, it says that Cat contributes to a group effect for the whole population, and also influences the group effect per Sample in a specific Category.
Is this correct?