linearmodel.Rd
Use to create a constant, linear, or quadratic PD model
linearmodel(
isPopulation = TRUE,
type = "Constant",
data = NULL,
columnMap = TRUE,
modelName = "",
workingDir = "",
...
)
Is this a population model TRUE
or individual model FALSE
?
Model type. Options are "Constant"
, "Linear"
, "Quadratic"
.
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 linearmodel_MappingParameters
ID
Column mapping argument for input dataset column(s)
that identify individual data profiles. Only applicable to population models
isPopulation = TRUE
.
C
Column mapping argument that represents the input dataset column for the independent variable that is treated as a covariate during the estimation/simulation process.
EObs
Column mapping argument that represents the input dataset column for the observed drug effect (i.e., the dependent variable).
Note that quoted and unquoted column names are supported. Please see colMapping
.
model <- linearmodel(type = "Linear", data = pkpdData, ID = "ID", C = "CObs", EObs = "EObs")
# View PML Code
print(model)
#>
#> Model Overview
#> -------------------------------------------
#> Model Name : Model_23_11_27_14_00
#> Working Directory : C:/Users/jcraig/Documents/GitHub/R-RsNLME/docs/reference/Model_23_11_27_14_00
#> Is population : TRUE
#> Model Type : LINEAR
#>
#> Linear
#> -------------------------------------------
#> Linear Type : E = EAlpha + EBeta*C
#> Linear Frozen : FALSE
#> Effect Compartment: FALSE
#>
#> PML
#> -------------------------------------------
#> test(){
#> covariate(C)
#> E = EAlpha + EBeta*C
#> error(EEps=1)
#> observe(EObs(C)=E + EEps)
#> stparm(EAlpha = tvEAlpha * exp(nEAlpha))
#> stparm(EBeta = tvEBeta * exp(nEBeta))
#> fixef( tvEAlpha = c(,1,))
#> fixef( tvEBeta = c(,1,))
#> ranef(diag(nEAlpha,nEBeta) = c(1,1))
#> }
#>
#> Structural Parameters
#> -------------------------------------------
#> EAlpha EBeta
#> -------------------------------------------
#> Observations:
#> Observation Name : EObs
#> Effect Name : E
#> Epsilon Name : EEps
#> Epsilon Type : Additive
#> Epsilon frozen : FALSE
#> is BQL : FALSE
#> -------------------------------------------
#> Column Mappings
#> -------------------------------------------
#> Model Variable Name : Data Column name
#> id : ID
#> C : CObs
#> EObs : EObs
#>