LinMatrixL | R Documentation |
Function computes the derivative of the model with respect to the between subject variability terms in the model (b's and bocc's) evaluated at a defined point (b_ind and bocc_ind).
LinMatrixL(model_switch, xt_ind, x, a, bpop, b_ind, bocc_ind, poped.db)
model_switch |
A vector that is the same size as xt, specifying which model each sample belongs to. |
x |
A vector for the discrete design variables. |
a |
A vector of covariates. |
bpop |
The fixed effects parameter values. Supplied as a vector. |
b_ind |
The point at which to evaluate the derivative |
bocc_ind |
The point at which to evaluate the derivative |
poped.db |
A PopED database. |
As a list:
y |
A matrix of size (samples per individual x number of random effects) |
poped.db |
A PopED database |
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
#for the FO approximation
ind=1
LinMatrixL(model_switch=t(poped.db$design$model_switch[ind,,drop=FALSE]),
xt_ind=t(poped.db$design$xt[ind,,drop=FALSE]),
x=zeros(0,1),
a=t(poped.db$design$a[ind,,drop=FALSE]),
bpop=poped.db$parameters$bpop[,2,drop=FALSE],
b_ind=zeros(poped.db$parameters$NumRanEff,1),
bocc_ind=zeros(poped.db$parameters$NumDocc,1),
poped.db)["y"]
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