Power Analysis for Linear Mixed Model with Covariates

Ben,

Thank you for the link. It is very helpful as it can serve as a map for achieving my goal.
Based on my (limited) understanding of Linear Mixed Models, the core of your simulation is a model of that looks at fixed effects of two treatments while accounting for random effects at the individual level (intercept only) and at the observation level (intercept only). Random effects are not dependent and a slope is not included in the model.

I started modifying the code to tailor it to my design. The first modification I wanted to do was build a model for a continuous dependent variable which would be otherwise similar to the one you have. I changed and ran the following code:

expdat <- expand.grid(kid = factor(1:30), Time = factor(1:4), Treat = c("XTx", "BAU"))
expdat$obs <- factor(seq(nrow(expdat)))

set.seed(101)
nsim <- 20
beta <- c(100, -7)
theta <- c(15, 15)

ss <- simulate(~Treat + (1 | kid) + (1| Time), nsim = nsim, family = gaussian, weights = rep(25, nrow(expdat)), newdata = expdat, newparams = list(theta = theta, beta = beta))

I received the following error message:

In setParams(object, newparams) :
   some parameters not specified in setParams()

Assuming beta includes the intercept and fixed effect for treatment, it is not clear to me how theta should be specified and further what the implications are after choosing Family = gaussian (weights?).

Any thoughts?

Power Analysis for Linear Mixed Model with Covariates

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