Model with variable number of arguments
On Thu, Jul 30, 2015 at 8:34 AM, Daniel Kaschek
<daniel.kaschek at physik.uni-freiburg.de> wrote:
Dear all, I have the following model: y_ij = x_i/s_j + (eps_ij)/s_j where y_ij are the responses, x_i and s_j are the fixed effects and the random effects follow a normal distribution
1. x_i and s_j are observed variables or parameters you need to estimate? Why you have no betas? 2. the formulation using division is unfamiliar to me, but when you get to this part
eps_ij ~ N(0, sigma0^2 + x_i^2 * sigmaR^2)
Can't answer because I can't tell if x_i is observed or not. If it is not, I don't know that lme4 will help. How did eps get this way in the first place. It appears it might be the sum of 2 separate random effects. If that's right, you are getting closer to the sort of model I would understand It makes me wonder why you don't have s_j inside the variance term there,, or why you don't have both x_ and s_ outside. Its pretty tough to read email with lots of x_i and such. That part is bad about plain text mailing lists
with error parameters sigma0 and sigmaR. In the end, I am interested in the parameters x_i, s_j, sigma0 and sigmaR. First of all, is lme4 the right package to solve this problem? When looking at nlmer(), I had problems to figure out what would be the correct structure of the function in the middle of the 3-part-formula. Any help is appreciated. Thanks a lot in advance, Daniel
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