Create some output to the screen and a text file that summarizes the problem you are tying to solve.
Usage
blockheader(
poped.db,
name = "Default",
iter = NULL,
e_flag = !(poped.db$settings$d_switch),
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],
fmf = 0,
dmf = 0,
bpop = NULL,
d = NULL,
docc = NULL,
sigma = NULL,
name_header = poped.db$settings$strOutputFileName,
file_path = poped.db$settings$strOutputFilePath,
out_file = NULL,
compute_inv = TRUE,
trflag = TRUE,
header_flag = TRUE,
...
)Arguments
- poped.db
A PopED database.
- name
The name used for the output file. Combined with
name_headeranditer. If""then output is to the screen.- iter
The last number in the name printed to the output file, combined with
name.- e_flag
Should output be with uncertainty around parameters?
- 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?
- fmf
The initial value of the FIM. If set to zero then it is computed.
- dmf
The initial OFV. If set to zero then it is computed.
- bpop
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
column 1 the type of the distribution for E-family designs (0 = Fixed, 1 = Normal, 2 = Uniform, 3 = User Defined Distribution, 4 = lognormal and 5 = truncated normal)
column 2 defines the mean.
column 3 defines the variance of the distribution (or length of uniform distribution).
Can also just supply the parameter values as a vector
c()if no uncertainty around the parameter value is to be used. The parameter order of 'bpop' is defined in the 'fg_fun' or 'fg_file'. If you use named arguments in 'bpop' then the order of this vector can be rearranged to match the 'fg_fun' or 'fg_file'. See `reorder_parameter_vectors`.- 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
c(). The parameter order of 'd' is defined in the 'fg_fun' or 'fg_file'. If you use named arguments in 'd' then the order of this vector can be rearranged to match the 'fg_fun' or 'fg_file'. See `reorder_parameter_vectors`.- docc
Matrix defining the IOV, the IOV variances and the IOV distribution as for d and bpop.
- 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
c().- name_header
The initial portion of the file name.
- file_path
The path to where the file should be created.
- out_file
Which file should the output be directed to? A string, a file handle using
fileor""will output to the screen.- compute_inv
should the inverse of the FIM be used to compute expected RSE values? Often not needed except for diagnostic purposes.
- trflag
Should the optimization be output to the screen and to a file?
- header_flag
Should the header text be printed out?
- ...
Additional arguments passed to further functions.
See also
Other Helper:
blockexp(),
blockfinal(),
blockopt()
Examples
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
#> function (model_switch, xt, parameters, poped.db)
#> {
#> with(as.list(parameters), {
#> y = xt
#> y = (DOSE * Favail * KA/(V * (KA - CL/V))) * (exp(-CL/V *
#> xt) - exp(-KA * xt))
#> return(list(y = y, poped.db = poped.db))
#> })
#> }
#> <bytecode: 0x557079188b38>
#> <environment: namespace:PopED>
## -- 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)
blockheader(poped.db,name="")
#> ==============================================================================
#> Optimization of design parameters
#>
#>
#> [1] ""
blockheader(name="",iter=1,poped.db)
#> ==============================================================================
#> Optimization of design parameters
#>
#>
#> [1] ""
blockheader(name='',
iter=1,
poped.db,
e_flag=FALSE,
opt_xt=TRUE,
opt_a=TRUE,opt_x=poped.db$settings$optsw[4],
opt_samps=poped.db$settings$optsw[1],opt_inds=poped.db$settings$optsw[5],
fmf=FIM,dmf=dmf,
bpop=poped.db$parameters$param.pt.val$bpop,
d=poped.db$parameters$param.pt.val$d,
docc=poped.db$parameters$docc,sigma=poped.db$parameters$param.pt.val$sigma)
#> ===============================================================================
#> Initial design evaluation
#>
#> Initial OFV = 1.14386e+24
#>
#> Initial design
#> expected relative standard error
#> (%RSE, rounded to nearest integer)
#> Parameter Values RSE_0
#> CL 0.15 5
#> V 8 3
#> KA 1 14
#> d_CL 0.07 30
#> d_V 0.02 37
#> d_KA 0.6 27
#> sig_prop 0.01 32
#> sig_add 0.25 26
#>
#> ==============================================================================
#> Optimization of design parameters
#>
#> * Optimize Sampling Schedule
#> * Optimize Covariates
#>
#> [1] ""
blockheader(name='',
iter=1,
poped.db,
e_flag=TRUE,
opt_xt=TRUE,
opt_a=TRUE,opt_x=poped.db$settings$optsw[4],
opt_samps=poped.db$settings$optsw[1],opt_inds=poped.db$settings$optsw[5],
fmf=FIM,dmf=dmf,
bpop=poped.db$parameters$param.pt.val$bpop,
d=poped.db$parameters$param.pt.val$d,
docc=poped.db$parameters$docc,sigma=poped.db$parameters$param.pt.val$sigma)
#> ===============================================================================
#> Initial design evaluation
#>
#> Initial OFV = 1.14386e+24
#>
#> Initial design
#> expected relative standard error
#> (%RSE, rounded to nearest integer)
#> Parameter Values RSE_0
#> CL 0.15 5
#> V 8 3
#> KA 1 14
#> d_CL 0.07 30
#> d_V 0.02 37
#> d_KA 0.6 27
#> sig_prop 0.01 32
#> sig_add 0.25 26
#>
#> ==============================================================================
#> Optimization of design parameters
#>
#> * Optimize Sampling Schedule
#> * Optimize Covariates
#>
#> [1] ""
poped.db.1 <- 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(0.01,0.25),
groupsize=32,
xt=rbind(c( 0.5,1,2,6,24,36,72,120),
c( 0.5,1.1,2,6,24,36,72,120)),
minxt=rbind(c(0,1,1.5,3,20,30,70,118),
c(0.1,1.1,1.6,3.1,20.1,30.1,70.1,118.1)),
maxxt=c(12,13,14,15,26,44,78,120),
a=70,
mina=0,
maxa=100)
blockheader(poped.db.1,name="",trflag=2,opt_xt=TRUE)
#> ==============================================================================
#> Model description : PopED model
#>
#> Model Sizes :
#> Number of individual model parameters g[j] : Ng = 5
#> Number of population model fixed parameters bpop[j] : Nbpop = 4
#> Number of population model random effects parameters b[j] : Nb = 3
#>
#> Typical Population Parameters:
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> bpop[1]: 0.15
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> bpop[2]: 8
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> bpop[3]: 1
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> bpop[4]: 1
#>
#> Between Subject Variability matrix D (variance units)
#> 0.07 0.00 0.00
#> 0.00 0.02 0.00
#> 0.00 0.00 0.60
#>
#> Diagonal Elements of D [sqrt(param)]:
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> D[1,1]: 0.07 [0.2646]
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> D[2,2]: 0.02 [0.1414]
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> D[3,3]: 0.6 [0.7746]
#>
#> Residual Unexplained Variability matrix SIGMA (variance units) :
#> 0.01 0.00
#> 0.00 0.25
#>
#> Diagonal Elements of SIGMA [sqrt(param)]:
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> SIGMA[1,1]: 0.01 [ 0.1]
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> SIGMA[2,2]: 0.25 [ 0.5]
#>
#> ==============================================================================
#> Experiment description (design and design space)
#>
#> Warning: 2 arguments not used by format 'Number of individuals: %g
#> '
#> Number of individuals: 64
#> Number of groups (individuals with same design): 2
#> Number of individuals per group:
#>
#> Warning: 2 arguments not used by format ' Group %g: %g
#> '
#> Group 1: 32
#> Group 2: 32
#> Number of samples per group:
#> Number of discrete experimental variables: 0
#> Number of model covariates: 1
#>
#> Initial Sampling Schedule
#> Group 1: 0.5 1 2 6 24 36 72 120
#> Group 2: 0.5 1.1 2 6 24 36 72 120
#>
#> Minimum allowed sampling values
#> Group 1: 1e-05 1 1.5 3 20 30 70 118
#> Group 2: 0.1 1.1 1.6 3.1 20.1 30.1 70.1 118.1
#>
#> Maximum allowed sampling values
#> Group 1: 12 13 14 15 26 44 78 120
#> Group 2: 12 13 14 15 26 44 78 120
#>
#> Covariates:
#> Group 1:
#> Warning: 2 arguments not used by format '%g'
#> 70
#> Group 2:
#> Warning: 2 arguments not used by format '%g'
#> 70
#>
#> ==============================================================================
#> Criterion Specification
#>
#> OFV calculation for FIM: 4
#> 1=Determinant of FIM,
#> 4=log determinant of FIM,
#> 6=determinant of interesting part of FIM (Ds)
#>
#> Approximation method: 0
#> 0=FO,
#> 1=FOCE,
#> 2=FOCEI,
#> 3=FOI
#>
#> Fisher Information Matrix type: 1
#> 0=Full FIM,
#> 1=Reduced FIM,
#> 2=weighted models,
#> 3=Loc models,
#> 4=reduced FIM with derivative of SD of sigma as pfim,
#> 5=FULL FIM parameterized with A,B,C matrices & derivative of variance,
#> 6=Calculate one model switch at a time, good for large matrices,
#> 7=Reduced FIM parameterized with A,B,C matrices & derivative of variance
#>
#> Design family: 1
#> D-family design (1) or
#> ED-family design (0)
#> (with or without parameter uncertainty)
#>
#> ==============================================================================
#> Optimization of design parameters
#>
#> * Optimize Sampling Schedule
#>
#> [1] ""