pkindirectmodel.Rd
Use to create a PK/Indirect response model.
pkindirectmodel(
isPopulation = TRUE,
parameterization = "Clearance",
absorption = "Intravenous",
numCompartments = 1,
isClosedForm = TRUE,
isTlag = FALSE,
hasEliminationComp = FALSE,
isFractionExcreted = FALSE,
isSaturating = FALSE,
infusionAllowed = FALSE,
isDuration = FALSE,
isSequential = FALSE,
isPkFrozen = FALSE,
hasEffectsCompartment = FALSE,
indirectType = "LimitedStimulation",
isBuildup = TRUE,
isExponent = FALSE,
indirectFrozen = FALSE,
data = NULL,
columnMap = TRUE,
modelName = "",
workingDir = "",
...
)
Is this a population model TRUE
or individual model FALSE
?
Type of parameterization. Options are "Clearance"
, "Micro"
,
"Macro"
, or "Macro1"
.
Type of absorption. Options are "Intravenous"
, "FirstOrder"
,
"Gamma"
, "InverseGaussian"
, "Weibull"
.
Value of either 1
, 2
, or 3
.
Set to TRUE
to convert model from a differential equation to close form.
Set to TRUE
to add a lag time parameter to the model.
Set to TRUE
to add an elimination compartment to the model.
Set to TRUE
if elimination compartment (hasEliminationComp = TRUE
)
contains a fraction excreted parameter.
Set to TRUE
to use Michaelis-Menten kinetics for elimination.
Only applicable to models with paramteterization = "Clearance"
Set to TRUE
if infusions allowed.
Set to TRUE
if infusions use duration instead of rate
(must also set infusionAllowed = TRUE
).
Set to TRUE
to freeze PK fixed effects and convert
the corresponding random effects into covariates as well as remove the PK observed variable from the model.
Set to TRUE
to freeze PK fixed effects and remove
the corresponding random effects as well as the PK observed variable from the model.
Set to TRUE
to include an effect compartment into the model.
Type of drug actions for the indirect response model.
Options are "LimitedStimulation"
, "InfiniteStimulation"
, "LimitedInhibition"
,
"InverseInhibition"
, "LinearStimulation"
, or "LogLinearStimulation"
.
Set to FALSE
to have the drug actions affect
the loss/degradation instead of the production.
Set to TRUE
to add an exponent parameter to the drug action term.
Set to TRUE
to freeze PD fixed effects and remove
the corresponding random effects as well as the PD observed variable from the model.
Input dataset
If TRUE
(default) column mapping arguments are required.
Set to FALSE
to manually map columns after defining model using colMapping
.
Model name for subdirectory created for model output in current working directory.
Working directory to run the model. Current working directory will be used
if workingDir
not specified.
Arguments passed on to pkindirectmodel_MappingParameters
ID
Column mapping argument for input dataset column(s) that identify
individual data profiles. Only applicable to population models isPopulation = TRUE
.
Time
Column mapping argument that represents the input dataset column for the relative time used in a study and only applicable to time-based models.
A1
Column mapping argument that represents the input dataset column for the amount of drug administered. Only applicable to the following types of models:
Models with absorption = "Intravenous"
and parameterization set
to either "Clearance"
,"Micro"
, or "Macro"
Models with
absorption
set to either "Gamma"
, "InverseGaussian"
, or "Weibull"
Aa
Column mapping argument that represents the input dataset column
for the amount of drug administered and only applicable to models with absorption = "FirstOrder"
.
A
Column mapping argument that represents the input dataset column
for the amount of drug administered and only applicable to models with
absorption = "Intravenous"
and parameterization = "Macro1"
.
A1_Rate
Column mapping argument that represents the input dataset column for the rate of drug administered. Only applicable to the following types of models:
Models with absorption = "Intravenous"
, infusionAllowed = TRUE
and parameterization set to either "Clearance"
,"Micro"
or "Macro"
Models with absorption
set to either "Gamma"
, "InverseGaussian"
,
or "Weibull"
and infusionAllowed = TRUE
A1_Duration
Column mapping argument that represents the input dataset column for the duration of drug administered. Only applicable to the following types of models:
Models with absorption = "Intravenous"
,
infusionAllowed = TRUE
with isDuration = TRUE
and
parameterization set to either "Clearance"
,"Micro"
or "Macro"
Models with absorption
set to either "Gamma"
, "InverseGaussian"
, or "Weibull"
and infusionAllowed = TRUE
with isDuration = TRUE
Aa_Rate
Column mapping argument that represents the input dataset column
for the rate of drug administered and only applicable to models with absorption = "FirstOrder"
,
infusionAllowed = TRUE
.
Aa_Duration
Column mapping argument that represents the input dataset column
for the duration of drug administered and only applicable to models with absorption = "FirstOrder"
,
infusionAllowed = TRUE
, and isDuration = TRUE
.
A_Rate
Column mapping argument that represents the input dataset column
for the rate of drug administered and only applicable to models with absorption = "Intravenous"
,
infusionAllowed = TRUE
, and parameterization = "Macro1"
.
A_Duration
Column mapping argument that represents the input dataset column
for the duration of drug administered and only applicable to models with absorption = "Intravenous"
,
infusionAllowed = TRUE
, isDuration = TRUE
, and parameterization = "Macro1"
.
A1Strip
Column mapping argument that represents the input dataset column
for the stripping dose and only applicable to models with parameterization = "Macro"
.
CObs
Column mapping argument that represents the input dataset column
for the observations of drug concentration in the central compartment and only applicable
to models with parameterization
being either set to either "Clearance"
or "Micro"
.
C1Obs
Column mapping argument that represents the input dataset column
for the observations of drug concentration in the central compartment and only applicable
to models with parameterization
being either set to either "Macro"
or "Macro1"
.
A0Obs
Column mapping argument that represents the input dataset column
for the observed amount of drug in the elimination compartment. (hasEliminationComp = TRUE
).
EObs
Column mapping argument that represents the input dataset column for the observed drug effect.
nV
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nV
.
nV2
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nV2
.
nV3
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nV3
.
nCl
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nCl
.
nCl2
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nCl2
.
nCl3
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nCl3
.
nKa
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nKa
.
nA
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nA
.
nAlpha
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nAlpha
.
nB
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nB
.
nBeta
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nBeta
.
nC
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nC
.
nGamma
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nGamma
.
nKe
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nKe
.
nK12
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nK12
.
nK21
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nK21
.
nK13
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nK13
.
nK31
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nK31
.
nTlag
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nTlag
.
nKm
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nKm
.
nVmax
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nVmax
.
nFe
If isSequential = TRUE
and isFractionExcreted = TRUE
,
mapped to the input dataset column that lists the values for random effect nFe
.
nMeanDelayTime
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nMeanDelayTime
.
nShapeParam
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nShapeParam
.
nShapeParamMinusOne
If isSequential = TRUE
, mapped to the input dataset column
that lists the values for random effect nShapeParamMinusOne
.
Note that quoted and unquoted column names are supported. Please see colMapping
.
model <- pkindirectmodel(
parameterization = "Micro",
data = pkpdData,
ID = "ID",
Time = "Time",
A1 = "Dose",
CObs = "CObs",
EObs = "EObs"
)
# View PML Code
print(model)
#>
#> Model Overview
#> -------------------------------------------
#> Model Name : Model_23_01_29_13_48
#> Working Directory : C:/Users/jcraig/Documents/GitHub/R-RsNLME/docs/reference/Model_23_01_29_13_48
#> Is population : TRUE
#> Model Type : PK_INDIRECT
#>
#> PK
#> -------------------------------------------
#> Parameterization : Micro
#> Absorption : Intravenous
#> Num Compartments : 1
#> Dose Tlag? : FALSE
#> Elimination Comp ?: FALSE
#> Infusion Allowed ?: FALSE
#> Sequential : FALSE
#> Freeze PK : FALSE
#>
#> Indirect
#> -------------------------------------------
#> Indirect Type : LimitedStimulation
#> Effect Compartment: FALSE
#> Buildup : TRUE
#> Exponent : FALSE
#> Indirect Frozen : FALSE
#>
#> PML
#> -------------------------------------------
#> test(){
#> cfMicro(A1, Ke)
#> dosepoint(A1)
#> C = A1 / V
#> deriv(E = Kin * (1 + Emax * C / (C + EC50)) - Kout * E)
#> sequence{ E= Kin / Kout}
#> error(EEps=1)
#> observe(EObs=E + EEps)
#> error(CEps=0.1)
#> observe(CObs=C * ( 1 + CEps))
#> stparm(V = tvV * exp(nV))
#> stparm(Ke = tvKe * exp(nKe))
#> stparm(Kin = tvKin * exp(nKin))
#> stparm(Kout = tvKout * exp(nKout))
#> stparm(Emax = tvEmax * exp(nEmax))
#> stparm(EC50 = tvEC50 * exp(nEC50))
#> fixef( tvV = c(,1,))
#> fixef( tvKe = c(,1,))
#> fixef( tvKin = c(,1,))
#> fixef( tvKout = c(,1,))
#> fixef( tvEmax = c(,1,))
#> fixef( tvEC50 = c(,1,))
#> ranef(diag(nV,nKe,nKin,nKout,nEmax,nEC50) = c(1,1,1,1,1,1))
#> }
#>
#> Structural Parameters
#> -------------------------------------------
#> V Ke Kin Kout Emax EC50
#> -------------------------------------------
#> Observations:
#> Observation Name : EObs
#> Effect Name : E
#> Epsilon Name : EEps
#> Epsilon Type : Additive
#> Epsilon frozen : FALSE
#> is BQL : FALSE
#> -------------------------------------------
#> Observation Name : CObs
#> Effect Name : C
#> Epsilon Name : CEps
#> Epsilon Type : Multiplicative
#> Epsilon frozen : FALSE
#> is BQL : FALSE
#> -------------------------------------------
#> Column Mappings
#> -------------------------------------------
#> Model Variable Name : Data Column name
#> id : ID
#> time : Time
#> A1 : Dose
#> EObs : EObs
#> CObs : CObs
#>