InitStep {nmw}

Initialization Step

Description

It receives parameters for the estimation and stores them into e environment.

Usage

InitStep(DataAll, THETAinit, OMinit, SGinit, LB, UB, Pred, METHOD)

Arguments

DataAll

Data for all subjects. It should contain columns which Pred function uses.

THETAinit

Theta initial values

OMinit

Omega matrix initial values

SGinit

Sigma matrix initial values

LB

Lower bounds for theta vector

UB

Upper bounds for theta vector

Pred

Prediction function name

METHOD

one of the estimation methods "ZERO", "COND", or "LAPL"

Details

Prediction function should return not only prediction values(F or IPRED) but also G (first derivative with respect to etas) and H (first derivative of Y with respect to epsilon). For the "LAPL", prediction function should return second derivative with respect to eta also. "INTERACTION" is TRUE for "COND" and "LAPL" option, and FALSE for "ZERO". Omega matrix should be full block one. Sigma matrix should be diagonal one.

Value

This does not return values, but stores necessary values into the environment e.

Author(s)

Kyun-Seop Bae <[e-mail]>

References

NONMEM Users Guide

Examples

DataAll = Theoph
colnames(DataAll) = c("ID", "BWT", "DOSE", "TIME", "DV")
DataAll[,"ID"] = as.numeric(as.character(DataAll[,"ID"]))

nTheta = 3
nEta = 3
nEps = 2

THETAinit = c(2, 50, 0.1) # Initial estimate
OMinit = matrix(c(0.2, 0.1, 0.1, 0.1, 0.2, 0.1, 0.1, 0.1, 0.2), nrow=nEta, ncol=nEta)
OMinit
SGinit = diag(c(0.1, 0.1))
SGinit

LB = rep(0, nTheta) # Lower bound
UB = rep(1000000, nTheta) # Upper bound

FGD = deriv(~DOSE/(TH2*exp(ETA2))*TH1*exp(ETA1)/(TH1*exp(ETA1) - TH3*exp(ETA3))*
             (exp(-TH3*exp(ETA3)*TIME)-exp(-TH1*exp(ETA1)*TIME)),
            c("ETA1","ETA2","ETA3"),
            function.arg=c("TH1", "TH2", "TH3", "ETA1", "ETA2", "ETA3", "DOSE", "TIME"),
            func=TRUE, hessian=TRUE)
H = deriv(~F + F*EPS1 + EPS2, c("EPS1", "EPS2"), function.arg=c("F", "EPS1", "EPS2"), func=TRUE)

PRED = function(THETA, ETA, DATAi)
{
  FGDres = FGD(THETA[1], THETA[2], THETA[3], ETA[1], ETA[2], ETA[3], DOSE=320, DATAi[,"TIME"])
  Gres = attr(FGDres, "gradient")
  Hres = attr(H(FGDres, 0, 0), "gradient")

  if (e$METHOD == "LAPL") {
    Dres = attr(FGDres, "hessian")
    Res = cbind(FGDres, Gres, Hres, Dres[,1,1], Dres[,2,1], Dres[,2,2], Dres[,3,])
    colnames(Res) = c("F", "G1", "G2", "G3", "H1", "H2", "D11", "D21", "D22", "D31", "D32", "D33")
  } else {
    Res = cbind(FGDres, Gres, Hres)
    colnames(Res) = c("F", "G1", "G2", "G3", "H1", "H2")
  }
  return(Res)
}

######### First Order Approximation Method
InitStep(DataAll, THETAinit=THETAinit, OMinit=OMinit, SGinit=SGinit, LB=LB, UB=UB,
         Pred=PRED, METHOD="ZERO")

######### First Order Conditional Estimation with Interaction Method
InitStep(DataAll, THETAinit=THETAinit, OMinit=OMinit, SGinit=SGinit, LB=LB, UB=UB,
         Pred=PRED, METHOD="COND")

######### Laplacian Approximation with Interacton Method
InitStep(DataAll, THETAinit=THETAinit, OMinit=OMinit, SGinit=SGinit, LB=LB, UB=UB,
         Pred=PRED, METHOD="LAPL")