• Modeling & Simulation in R
  • Preface
    • Who this book is for
    • How to read this book
  • I Introduction
  • 1 Quick Installation Guide
    • 1.1 Windows
    • 1.2 Linux specific: If you use Microsoft Open R
    • 1.3 Linux (not REDHAT6 and CENTOS6) / Mac
    • 1.4 Linux (REDHAT6 and CENTOS6)
    • 1.5 NONMEM and MONOLIX
      • 1.5.1 Windows
      • 1.5.2 Unix
  • 2 Installation
    • 2.1 Requirements
    • 2.2 Rtools on Windows
    • 2.3 IQR Tools
      • 2.3.1 Newest Version
      • 2.3.2 Redhat 6 / Centos 6
      • 2.3.3 Defined Version
    • 2.4 Setup after installation
    • 2.5 Activation
    • 2.6 IQReport
    • 2.7 IQRsbml
  • 3 Examples in this Book
  • 4 Reproducibility of Results
    • 4.1 The CRAN Nightmare
    • 4.2 MRAN Time Machine
    • 4.3 IQR Tools impact
    • 4.4 IQR Tools testing
      • 4.4.1 Defining R and IQR Tools version
    • 4.5 For the paranoid
  • 5 Validation
    • 5.1 Validation of IQR Tools
    • 5.2 Validation support
  • 6 Release Notes
    • 6.1 V1.5.0 October 23, 2020
    • 6.2 V1.4.0 August 21, 2020
    • 6.3 V1.3.2 July 5, 2020
    • 6.4 V1.3.1 May 20, 2020
    • 6.5 V1.3.0 May 01, 2020
    • 6.6 V1.2.1 March 08, 2020
    • 6.7 V1.2.0 March 02, 2020
    • 6.8 V1.1.1 January 31, 2020 (Brexit Day)
    • 6.9 V1.1.0 December 23, 2019
    • 6.10 V1.0.9 October 13, 2019
    • 6.11 V1.0.8 September 13, 2019
    • 6.12 V1.0.7 September 2, 2019
    • 6.13 V1.0.6 May 28, 2019
    • 6.14 V1.0.5 May 20, 2019
    • 6.15 V1.0.4 April 19, 2019
    • 6.16 V1.0.3 April 3, 2019
    • 6.17 V1.0.2 March 18, 2019
    • 6.18 V1.0.1 February 23, 2019
    • 6.19 V1.0.0 February 8, 2019
    • 6.20 V0.9.999 January 16, 2019
    • 6.21 V0.9.99 December 06, 2018
    • 6.22 V0.9.9 November 27, 2018
    • 6.23 V0.9.3 October 25, 2018
    • 6.24 V0.9.2 October 18, 2018
    • 6.25 V0.9.1 October 07, 2018
    • 6.26 V0.9.0 September 03, 2018
    • 6.27 V0.8.1 August 7, 2018
    • 6.28 V0.8.0 June 25, 2018
    • 6.29 V0.7.2 May 10, 2018
    • 6.30 V0.7.0 April 23, 2018
    • 6.31 V0.6.6 April 16, 2018
    • 6.32 V0.6.4 March 16, 2018
    • 6.33 V0.6.3 March 08, 2018
    • 6.34 V0.6.2 February 08, 2018
    • 6.35 V0.6.1 January 26, 2018
    • 6.36 V0.6.0 January 24, 2018
    • 6.37 V0.5.8 January 19, 2018
    • 6.38 V0.5.7 January 16, 2018
    • 6.39 V0.5.6 December 14, 2017
    • 6.40 V0.5.5 December 04, 2017
    • 6.41 V0.5.1 October 14, 2017
    • 6.42 V0.5.0 October 10, 2017
    • 6.43 V0.4.2 September 21, 2017
    • 6.44 V0.4.1 September 14, 2017
    • 6.45 V0.3.0 August 14, 2017
    • 6.46 V0.2.0 July 14, 2017
    • 6.47 V0.1.0 June 5, 2017
  • II Case Studies
  • 7 Analysis dataset preparation
    • 7.1 Example workflow
      • 7.1.1 Original dataset in general row-based format
      • 7.1.2 Import as IQRdataGENERAL format
      • 7.1.3 Source data exploration
      • 7.1.4 Cleaning to create an analysis dataset
      • 7.1.5 Export
    • 7.2 Workflow customization
      • 7.2.1 Dataset handling
      • 7.2.2 Import/export options
      • 7.2.3 Cleaning options
      • 7.2.4 Data exploration
  • 8 Model definition
    • 8.1 Model definition basics
      • 8.1.1 Biochemical reaction model
      • 8.1.2 One compartment linear PK
      • 8.1.3 PK/PD
    • 8.2 Dosing representation
    • 8.3 Advanced model definition
      • 8.3.1 Example
      • 8.3.2 Lagtimes
      • 8.3.3 Mathematical functions
      • 8.3.4 Implementing conditional statements (if-then-else)
      • 8.3.5 Interpolation functions
      • 8.3.6 MODEL FUNCTIONS section
      • 8.3.7 MODEL EVENTS section
    • 8.4 Handling of models in R
      • 8.4.1 Model import
      • 8.4.2 Support of SBML
      • 8.4.3 Basic model information
      • 8.4.4 Model export
    • 8.5 PK model library
    • 8.6 Example models
      • 8.6.1 PBPK
      • 8.6.2 Friberg neutropenia
      • 8.6.3 Novak-Tyson Cell-Cycle
      • 8.6.4 Parasitemia PK/PD
      • 8.6.5 Bouncing ball
      • 8.6.6 C-Functions
      • 8.6.7 Fantasy events
      • 8.6.8 Novak-Tyson biochemical
  • 9 Simulation of models
    • 9.1 Simulation
    • 9.2 Simulation settings
      • 9.2.1 Simulation time
      • 9.2.2 Initial conditions
      • 9.2.3 Parameters
      • 9.2.4 Output selection
    • 9.3 Dosing events
      • 9.3.1 Single dosing input
      • 9.3.2 Multiple dosing inputs
      • 9.3.3 Special dosing parameters
    • 9.4 Parameter sensitivities
    • 9.5 Integrator in C
  • 10 NLME Modeling
    • 10.1 Longitudinal Models
      • 10.1.1 Required data format
      • 10.1.2 Structural models
      • 10.1.3 Linear vs.nonlinear models
      • 10.1.4 Time varying covariates
      • 10.1.5 Basic PK model
      • 10.1.6 Tabular results
      • 10.1.7 General diagnostics
      • 10.1.8 Output diagnostics
      • 10.1.9 Lagtime and FOCEI
      • 10.1.10 Zero-order absorption
      • 10.1.11 NLME model settings
      • 10.1.12 Sequential PK/PD
      • 10.1.13 Regression parameters
      • 10.1.14 Error models
      • 10.1.15 Multiple outputs
      • 10.1.16 Basic covariate models
      • 10.1.17 Covariate plots
      • 10.1.18 Complex covariates
      • 10.1.19 Covariance
      • 10.1.20 BLOQ data
      • 10.1.21 IV/SC PK model
      • 10.1.22 NONMEM Bayes
      • 10.1.23 Other features
    • 10.2 Time-to-event models
      • 10.2.1 Data format
      • 10.2.2 Defining TTE NLME models
      • 10.2.3 Weibull
      • 10.2.4 Weibull with delay
      • 10.2.5 Exponential
      • 10.2.6 Exponential with delay
      • 10.2.7 Gompertz
      • 10.2.8 Gompertz with delay
      • 10.2.9 Log-logistic
      • 10.2.10 Diagnostics
    • 10.3 Joint models
      • 10.3.1 Longitudinal + TTE
      • 10.3.2 Data format
      • 10.3.3 RTTE & interval censoring
      • 10.3.4 NONMEM
  • 11 QSP Modeling
    • 11.1 Background
    • 11.2 Interface
      • 11.2.1 Data
      • 11.2.2 ModelSpec
    • 11.3 Systems Biology Example: Epo-Receptor
      • 11.3.1 Basic model simulation
      • 11.3.2 Manipulating parameters for simulations
      • 11.3.3 Defining experimental conditions
      • 11.3.4 Modeling data - exploration by manual parameter tweaking
      • 11.3.5 Modeling data - parameter estimation
      • 11.3.6 Modeling data - multistart optimization
      • 11.3.7 Modeling data - Profile Likelihood
      • 11.3.8 Modelling data - IIV and BLOQ (censored data)
  • 12 Model evaluation
    • 12.1 Goodness-of-fit
      • 12.1.1 Random effects
      • 12.1.2 Random effects / covariates
      • 12.1.3 GOF plots
      • 12.1.4 Individual plots
      • 12.1.5 Export to file
      • 12.1.6 Plot data
    • 12.2 VPC
      • 12.2.1 Generate VPC
      • 12.2.2 Prediction corrected VPC (pcVPC)
      • 12.2.3 VPC data
      • 12.2.4 VPC sequential modeling
      • 12.2.5 Additional settings
    • 12.3 Bootstrap
      • 12.3.1 Generate bootstrap
      • 12.3.2 Bootstrap results
      • 12.3.3 Stratification
      • 12.3.4 Large bootstraps
    • 12.4 Profile likelihood
  • 13 Advanced modeling workflows
    • 13.1 PopPK workflow
    • 13.2 Covariate selection
    • 13.3 Pop PK/PD workflow
  • 14 Population simulations
    • 14.1 Basic population simulation
      • 14.1.1 Basic example
      • 14.1.2 Event table generation
      • 14.1.3 Parameter sampling
      • 14.1.4 Customizing simulations
    • 14.2 Clinical trial simulation
      • 14.2.1 Parallel design example
      • 14.2.2 Adaptive design example
  • 15 Experimental design
    • 15.1 Use of PopED
      • 15.1.1 PopED / IQR Toolsinterface
      • 15.1.2 Same example in PopED
    • 15.2 Use of profile likelihood
  • 16 Exposure response analysis
    • 16.1 Logistic regression
      • 16.1.1 Single predictor
      • 16.1.2 Multiple predictors
    • 16.2 Kaplan-Meier
      • 16.2.1 Simple plot
      • 16.2.2 Stratified plot
      • 16.2.3 Style and annotation
      • 16.2.4 Risk table
      • 16.2.5 Confidence intervals
      • 16.2.6 Using the CENScol argument
    • 16.3 Cox Regression
  • 17 Reporting in Microsoft Word
    • 17.1 Example analysis report
    • 17.2 Applying styles when creating Word document
  • III Manuals
  • 18 General Dataset Format
    • 18.1 General columns
    • 18.2 Additional columns
    • 18.3 Deprecated columns
  • 19 Structural Model Syntax
    • 19.1 Model sections
      • 19.1.1 Model name
      • 19.1.2 Model notes
      • 19.1.3 Model states
      • 19.1.4 Model state information
      • 19.1.5 Model parameters
      • 19.1.6 Model variables
      • 19.1.7 Model reactions
      • 19.1.8 Model functions
      • 19.1.9 Model events
      • 19.1.10 Model C functions
    • 19.2 Pre-defined functions
    • 19.3 IQRmodel object
  • 20 Dosing definition
    • 20.1 IQRdosing object
  • 21 General Parameter Format (GPF)
    • 21.1 The GPF excel file
    • 21.2 Columns in the GPF estimates sheet
      • 21.2.1 Naming convention
      • 21.2.2 Example GPF file
    • 21.3 Parameter transformations
      • 21.3.1 Transformation between original and normal units
    • 21.4 Basic terms
  • 22 Random sampling of NLME model parameters
    • 22.1 Input
      • 22.1.1 Example GPF file
      • 22.1.2 Example patient data
    • 22.2 Calling the function sampleIndParamValues
      • 22.2.1 Output
      • 22.2.2 Example output
    • 22.3 Covariate adjustment formulae
    • 22.4 Possible values for FLAG_SAMPLE
    • 22.5 Sampling steps
      • 22.5.1 Step 1: Sampling of population parameter values
      • 22.5.2 Step 2: Sampling records from the patient data
      • 22.5.3 Step 3: Calculating typical individual parameter values
      • 22.5.4 Step 4: Sampling random effects
      • 22.5.5 Step 5: Calculating individual parameter values
    • 22.6 Testing for sampling discrepancies
      • 22.6.1 Detecting discrepancies in samples from the parameter uncertainty distribution
  • Appendix
  • A Function Reference
    • A.1 Allowed in IQRmodel
    • A.2 Auxiliary
    • A.3 Bootstrap
    • A.4 Covariate analysis
    • A.5 Data handling
    • A.6 GPF generating and I/O
    • A.7 GPF helpers
    • A.8 Help & Documentation
    • A.9 IQR Table
    • A.10 IQR plot
    • A.11 IQR report
    • A.12 IQR shiny
    • A.13 IQR slides
    • A.14 IQRdataER
    • A.15 IQRdataGENERAL
    • A.16 IQRdataGENERAL auxiliaries
    • A.17 IQRdataGeneral
    • A.18 IQRdataNLME
    • A.19 IQRmodel
    • A.20 IQRnlmeData
    • A.21 IQRnlmeProject
    • A.22 IQRtteProject
    • A.23 Installation
    • A.24 Logistic regression
    • A.25 MONOLIX
    • A.26 NCA
    • A.27 NLMIXR
    • A.28 Optimal Experiment Design
    • A.29 Output
    • A.30 Output & Compliance
    • A.31 QSP
    • A.32 SAS
    • A.33 Sensitivity Analysis
    • A.34 Simulation
    • A.35 Statistics
    • A.36 Submission package
    • A.37 Survival analysis
    • A.38 VPC
    • A.39 dMod interface
    • A.40 functions in the NLME parameter sampling API
    • A.41 stat functions
  • (c) 2018-2020 IntiQuan GmbH

Modeling & Simulation in R

aux_rmdir function

aux_rmdir.Rd

Removes a folder

aux_rmdir(pathdir)

Arguments

pathdir

path to folder to remove

See also

Other Auxiliary: IQRloadCSVdata(), IQRsaveCSVdata(), and(), aux_explodePC(), aux_explode(), aux_fileparts(), aux_fileread(), aux_filewrite(), aux_getRelPath(), aux_mkdir(), aux_na_locf(), aux_postFillChar(), aux_preFillChar(), aux_quantilenumber(), aux_simplifypath(), aux_splitVectorEqualPieces(), aux_strFindAll(), aux_strrep(), aux_strtrim(), aux_unlevel(), aux_version(), calcAICBIC(), clusterX(), compare_IQRmodel_IQRsysModel_simulation(), fit_EmaxModel(), format_GUM(), geocv(), geomean(), geosd(), ge(), ginv(), gt(), interp0(), interp1(), interpcs(), inv_logit(), le(), logit(), lt(), mod(), mvrnorm(), or(), piecewise(), progressBar(), run_silent_IQR(), stopIQR(), tempdirIQR(), tempfileIQR(), warningIQR()