Nothing
require(TMB)
dyn.load(dynlib("randomregression"))
## Simulate random regression
set.seed(123)
n <- 100
ng <- 10
f <- gl(ng, n/ng)
t <- rnorm(n, mean=2, sd=5)
a <- rnorm(ng, mean=3, sd=4)
b <- rnorm(ng, mean=8, sd=7)
x <- a[f] * t + b[f] + rnorm(n, mean=0, sd=1)
if(FALSE){
library(lattice); xyplot(x ~ t, group = f, type="l")
}
## Construct AD function object
data <- list(group=f, x=x, t=t)
parameters <- list(
a = rep(1, nlevels(f)),
b = rep(1, nlevels(f)),
mu = rep(1, 2),
sigma = rep(1, 2),
sigma0 = 1
)
obj <- MakeADFun(data = data,
parameters = parameters,
random = c("a", "b"),
DLL = "randomregression"
)
## Test eval function and gradient
obj$fn()
obj$gr()
## Fit model
opt <- nlminb(obj$par, obj$fn, obj$gr)
opt$par
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