| evaluate.e.ofv.fim {PopED} | R Documentation |
Evaluate the expectation of the Fisher Information Matrix (FIM) and the expectation of the OFV(FIM).
Description
Compute the expectation of the FIM and OFV(FIM) given the model, parameters, distributions of parameter uncertainty, design and methods defined in the
PopED database. Some of the arguments coming from the PopED database can be overwritten;
by default these arguments are NULL in the
function, if they are supplied then they are used instead of the arguments from the PopED database.
Usage
evaluate.e.ofv.fim(
poped.db,
fim.calc.type = NULL,
bpop = poped.db$parameters$bpop,
d = poped.db$parameters$d,
covd = poped.db$parameters$covd,
docc = poped.db$parameters$docc,
sigma = poped.db$parameters$sigma,
model_switch = NULL,
ni = NULL,
xt = NULL,
x = NULL,
a = NULL,
groupsize = poped.db$design$groupsize,
deriv.type = NULL,
bLHS = poped.db$settings$bLHS,
ofv_calc_type = poped.db$settings$ofv_calc_type,
ED_samp_size = poped.db$settings$ED_samp_size,
use_laplace = poped.db$settings$iEDCalculationType,
laplace.fim = FALSE,
...
)
Arguments
poped.db |
A PopED database. |
fim.calc.type |
The method used for calculating the FIM. Potential values:
|
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 |
covd |
Column major vector defining the covariances of the IIV variances.
That is, from your full IIV matrix |
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 |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
ni |
A vector of the number of samples in each group. |
xt |
A matrix of sample times. Each row is a vector of sample times for a group. |
x |
A matrix for the discrete design variables. Each row is a group. |
a |
A matrix of covariates. Each row is a group. |
groupsize |
A vector of the number of individuals in each group. |
deriv.type |
A number indicating the type of derivative to use:
|
bLHS |
How to sample from distributions in E-family calculations. 0=Random Sampling, 1=LatinHyperCube – |
ofv_calc_type |
OFV calculation type for FIM
|
ED_samp_size |
Sample size for E-family sampling |
use_laplace |
Should the Laplace method be used in calculating the expectation of the OFV? |
laplace.fim |
Should an E(FIM) be calculated when computing the Laplace approximated E(OFV). Typically the FIM does not need to be computed and, if desired, this calculation is done using the standard MC integration technique, so can be slow. |
... |
Other arguments passed to the function. |
Value
A list containing the E(FIM) and E(OFV(FIM)) and the a poped.db updated according to the function arguments.
See Also
Other FIM:
LinMatrixH(),
LinMatrixLH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.fim(),
gradf_eps(),
mf3(),
mf7(),
mftot(),
ofv_criterion(),
ofv_fim()
Other E-family:
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot()
Other evaluate_FIM:
calc_ofv_and_fim(),
evaluate.fim(),
ofv_fim()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization
## with parameter uncertainty)
#####################################
## 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 samoples).
## 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)
}
# Adding 10% log-normal Uncertainty to fixed effects (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_ln["Favail",] <- c(0,1,0)
bpop_vals_ed_ln
## -- 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=bpop_vals_ed_ln,
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=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70,
mina=0,
maxa=100)
############# END ###################
## Create PopED database
## (warfarin model for optimization
## with parameter uncertainty)
#####################################
## ED evaluate (with very few samples)
output <- evaluate.e.ofv.fim(poped.db,ED_samp_size=10)
output$E_ofv
## API evaluate (with very few samples)
output <- evaluate.e.ofv.fim(poped.db,ED_samp_size=10,ofv_calc_type=4)
output$E_ofv
## ED evaluate using Laplace approximation
tic()
output <- evaluate.e.ofv.fim(poped.db,use_laplace=TRUE)
toc()
output$E_ofv
## Not run:
## ED expected value with more precision.
## Compare time and value to Laplace approximation.
## Run a couple of times to see stochasticity of calculation.
tic()
e_ofv_mc <- evaluate.e.ofv.fim(poped.db,ED_samp_size=500)
toc()
e_ofv_mc$E_ofv
# If you want to get an E(FIM) from the laplace approximation you have to ask for it
# and it will take more time.
output <- evaluate.e.ofv.fim(poped.db,use_laplace=TRUE,laplace.fim=TRUE)
output$E_fim
## End(Not run)