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Create linear model

linearmodel.Rd

Use to create a constant, linear, or quadratic PD model

Usage

linearmodel(
  isPopulation = TRUE,
  type = "Constant",
  data = NULL,
  columnMap = TRUE,
  modelName = "",
  workingDir = "",
  ...
)

Arguments

isPopulation

Is this a population model TRUE or individual model FALSE?

type

Model type. Options are "Constant", "Linear", "Quadratic".

data

Input dataset

columnMap

If TRUE (default) column mapping arguments are required. Set to FALSE to manually map columns after defining model using colMapping.

modelName

Model name for subdirectory created for model output in current working directory.

workingDir

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).

Value

NlmePmlModel object

Column mapping

Note that quoted and unquoted column names are supported. Please see colMapping.

Examples

model <- linearmodel(type = "Linear", data = pkpdData, ID = "ID", C = "CObs", EObs = "EObs")

# View PML Code
print(model)
#> 
#>  Model Overview 
#>  ------------------------------------------- 
#> Model Name        :  Model_25_11_14_15_37
#> Working Directory :  C:/Repos/R-RsNLME/docs/reference/Model_25_11_14_15_37
#> 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
#>