Prediction after a gnlmm fit

Generate predictions after a generalized non-linear mixed effect model fit

prediction(fit, pred, data = NULL, mc.cores = 1)

Arguments

fit

a gnlmm fit object

pred

prediction function

data

new data

mc.cores

number of cores (for Linux only)

Value

observed and predicted

Examples

# \donttest{
if (FALSE) {
ode <- "
d/dt(depot) =-KA*depot;
d/dt(centr) = KA*depot - KE*centr;
"
sys1 <- RxODE(ode)

pars <- function() {
  CL <- exp(THETA[1] + ETA[1]) # ; if (CL>100) CL=100
  KA <- exp(THETA[2] + ETA[2]) # ; if (KA>20) KA=20
  KE <- exp(THETA[3])
  V <- CL / KE
  sig2 <- exp(THETA[4])
}
llik <- function() {
  pred <- centr / V
  dnorm(DV, pred, sd = sqrt(sig2), log = TRUE)
}

inits <- list(THTA = c(-3.22, 0.47, -2.45, 0))

inits$OMGA <- list(ETA[1]+ETA[2]~c(.027, .01, .37))

theo <- theo_md

fit <- try(gnlmm(llik, theo, inits, pars, sys1,
  control = list(trace = TRUE, nAQD = 1)
))

if (!inherits(fit, "try-error")) {

pred <- function() {
  pred <- centr / V
}

s <- try(prediction(fit, pred))
if (!inherits(s, "try-error")) {
 plot(s$p, s$dv)
 abline(0, 1, col = "red")
}
}
}
# }