Dtrace | R Documentation |
A helper function for writing output to the screen and files when optimizing.
Dtrace(
fn,
it,
ni,
xtopt,
xopt,
aopt,
gxt,
ga,
dmf,
diff,
ixt,
ia,
itvector,
dmfvector,
poped.db,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
opt_samps = poped.db$settings$optsw[1],
opt_inds = poped.db$settings$optsw[5],
rsit = poped.db$settings$rsit,
convergence_eps = poped.db$settings$convergence_eps
)
fn |
A file to output information to. Can also be the screen if |
it |
the iteration number. |
ni |
A vector of the number of samples in each group. |
xtopt |
The matrix defining current best sampling schedule. |
xopt |
The cell structure defining the current best discrete design variables. |
aopt |
The matrix defining the current best continuous design variables. |
gxt |
The matrix defining the current gradient of the xt vector. |
ga |
The matrix defining the current gradient for the continuous design variables. |
dmf |
The current OFV. |
diff |
The difference from the previous iteration. |
ixt |
If xt Gradient Inversion occurred or not. |
ia |
If a Gradient Inversion occurred or not. |
itvector |
The iteration vector. Not currently used. |
dmfvector |
The dmf vector. Not currently used. |
poped.db |
A PopED database. |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
opt_samps |
Are the number of sample times per group being optimized? |
opt_inds |
Are the number of individuals per group being optimized? |
rsit |
Number of Random search iterations |
convergence_eps |
Stochastic Gradient convergence value, (difference in OFV for D-optimal, difference in gradient for ED-optimal) |
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)
#####################################
FIM <- evaluate.fim(poped.db)
dmf <- det(FIM)
Dtrace(fn="",
it=1,
ni=poped.db$design$ni,
xtopt=poped.db$design$xt,
xopt=poped.db$design$x,
aopt=poped.db$design$a,
gxt=0,ga=0,
dmf=dmf,diff=3,
ixt=FALSE,
ia=FALSE,
itvector=NULL,
dmfvector=NULL,
poped.db,
opt_xt=poped.db$settings$optsw[2],
opt_a=poped.db$settings$optsw[4],opt_x=poped.db$settings$optsw[3],
opt_samps=poped.db$settings$optsw[1],opt_inds=poped.db$settings$optsw[5],
rsit=200)
Dtrace(fn="",
it=1,
ni=poped.db$design$ni,
xtopt=poped.db$design$xt,
xopt=poped.db$design$x,
aopt=poped.db$design$a,
gxt=0,ga=0,
dmf=dmf,diff=3,
ixt=FALSE,
ia=FALSE,
itvector=NULL,
dmfvector=NULL,
poped.db,
opt_xt=poped.db$settings$optsw[2],
opt_a=poped.db$settings$optsw[4],opt_x=poped.db$settings$optsw[3],
opt_samps=poped.db$settings$optsw[1],opt_inds=poped.db$settings$optsw[5],
rsit=0)
Dtrace(fn="",
it=1,
ni=poped.db$design$ni,
xtopt=poped.db$design$xt,
xopt=poped.db$design$x,
aopt=poped.db$design$a,
gxt=0,ga=0,
dmf=dmf,diff=3,
ixt=FALSE,
ia=FALSE,
itvector=NULL,
dmfvector=NULL,
poped.db,
opt_xt=poped.db$settings$optsw[2],
opt_a=poped.db$settings$optsw[4],opt_x=poped.db$settings$optsw[3],
opt_samps=poped.db$settings$optsw[1],opt_inds=poped.db$settings$optsw[5],
rsit=1)
Dtrace(fn="",
it=1,
ni=poped.db$design$ni,
xtopt=poped.db$design$xt,
xopt=poped.db$design$x,
aopt=poped.db$design$a,
gxt=0,ga=0,
dmf=dmf,
diff=0,
ixt=FALSE,
ia=FALSE,
itvector=NULL,
dmfvector=NULL,
poped.db,
opt_xt=poped.db$settings$optsw[2],
opt_a=poped.db$settings$optsw[4],opt_x=poped.db$settings$optsw[3],
opt_samps=poped.db$settings$optsw[1],opt_inds=poped.db$settings$optsw[5],
rsit=1)
Dtrace(fn="",
it=5,
ni=poped.db$design$ni,
xtopt=poped.db$design$xt,
xopt=poped.db$design$x,
aopt=poped.db$design$a,
gxt=0,ga=0,
dmf=dmf,
diff=0,
ixt=FALSE,
ia=FALSE,
itvector=NULL,
dmfvector=NULL,
poped.db,
opt_xt=poped.db$settings$optsw[2],
opt_a=poped.db$settings$optsw[4],opt_x=poped.db$settings$optsw[3],
opt_samps=poped.db$settings$optsw[1],opt_inds=poped.db$settings$optsw[5],
rsit=1)
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