calc_autofocus | R Documentation |
Compute the autofocus portion of the stochastic gradient routine
calc_autofocus(
m,
ni_var,
dmf,
varopt,
varopto,
maxvar,
minvar,
gradvar,
normgvar,
avar,
model_switch,
groupsize,
xtopt,
xopt,
aopt,
ni,
bpop,
d,
sigma,
docc,
poped.db
)
m |
Number of groups in the study. Each individual in a group will have the same design. |
ni_var |
The ni_var. |
dmf |
The initial OFV. If set to zero then it is computed. |
varopt |
The varopt. |
varopto |
The varopto. |
maxvar |
The maxvar. |
minvar |
The minvar. |
gradvar |
The gradvar. |
normgvar |
The normgvar. |
avar |
The avar. |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
groupsize |
A vector of the number of individuals in each group. |
xtopt |
The optimal sampling times matrix. |
xopt |
The optimal discrete design variables matrix. |
aopt |
The optimal continuous design variables matrix. |
ni |
A vector of the number of samples in each group. |
bpop |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
Can also just supply the parameter values as a vector |
d |
Matrix defining the diagonals of the IIV (same logic as for the fixed effects
matrix bpop to define uncertainty). One can also just supply the parameter values as a |
sigma |
Matrix defining the variances can covariances of the residual variability terms of the model.
can also just supply the diagonal parameter values (variances) as a |
docc |
Matrix defining the IOV, the IOV variances and the IOV distribution as for d and bpop. |
poped.db |
A PopED database. |
A list containing:
navar |
The autofocus parameter. |
poped.db |
PopED database. |
Other Optimize:
Doptim()
,
LEDoptim()
,
RS_opt()
,
a_line_search()
,
bfgsb_min()
,
calc_ofv_and_grad()
,
mfea()
,
optim_ARS()
,
optim_LS()
,
poped_optim()
,
poped_optim_1()
,
poped_optim_2()
,
poped_optim_3()
,
poped_optimize()
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)
#####################################
## Not run:
# Stochastic gradient search, DOSE and sample time optimization
sg.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 1,
bUseBFGSMinimizer = 0,
bUseLineSearch = 0,
sgit=20)
## End(Not run)
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