Introduction | Initial Setup | Setting up the data | Population Simulation Data | Time Profile Plots | Population Data Aggregation | Showing Individual Dataset Names | Custom Aesthetic Mappings | Predicted vs Observed Plots | Customizing Fold Distance | Swapping Axes | Scaling Options | Residuals vs Covariate Plots | Residuals vs Observed Values | Residuals vs Time | Residuals vs Predicted Values | Residual Scale Options | Residuals as Histogram | Quantile-Quantile (Q-Q) Plot | Automatic Unit Conversion | Specifying Units Directly | Handling Mixed Error Types | Using data.table Instead of DataCombined | Further Customization | Legend title | Legend position | Saving Plots

Calculating PK parameters | Extracting a dataframe | Import and export of PK-analyses | User-Defined PK Parameters | Examples | Calculate AUC between for a specific time interval. | Calculate CMax between the 4th and 5th application of a multiple applications simulation. | Calculate CMax between the 4th and 5th application of a multiple applications simulation and apply a time offset | Update Dimension of a standard parameter for correct visualization

Changing initial values | Formula types | Changing parameters and molecules initial values

Adding enzyme ontogenies

Unit conversion

Life cycle of a simulation run | Running a single simulation once | Running a single simulation multiple times and varying some parameters/initial values between each run | Optimized loading for running simulations with predefined set of variable parameters | Varying state variable parameters with SimulationBatch

Overview | Quick Start: Basic Simulation | Step-by-Step Tutorial | Loading Simulations | Exploring Model Structure | Modifying Parameters | Customizing Simulation Settings | Running Simulations | Visualizing Results | Option 1: Built-in Plotting Functions | Option 2: Custom Plotting with ggplot2 | Complete Workflow Example | Understanding ospsuite Objects | Object-Oriented Approach | Key Classes | General Workflow Summary | Next Steps | Essential Next Steps: | Population Studies: | Analysis and Optimization: | Data Integration: | Advanced Topics: | General Information

Loading a simulation | Accessing entities of the model and their properties - the path concept | Loading simulation tree

DataSet | Importing data

Not required for {ospsuite} version equal or above 12.1.0 | Versions below 12.1.0 | Install PK-Sim Globally | Install and use PK-Sim in a project

Loading population file | Creating populations | Running population simulation

Running individual simulation and retrieving the results | Running multiple individual simulations and retrieving the results | Adding new outputs | Changing simulation intervals | Adjusting solver settings

Performing a sensitivity analysis | Understanding numberOfSteps and variationRange | Customizing sensitivity analysis settings | Import and export of sensitivity analysis results

Changing table parameter values

Introduction | Time profile plots | Observed versus simulated scatter plot | Residuals versus time or vs simulated scatter plot | Customizing plots | Creating multi-panel plots | Saving plots | Implementation details

Introduction | Creating DataCombined object | Grouping | Transformations | Extracting a combined data frame | Further functionalities | Visualizations with DataCombined

Data type conversion | Basic data types | Object pointers and the NetObject class | Loading an assembly | Working with static members | Working with objects

Installation | Overview of the workflow | Loading PKML models | Specifying model parameters for analysis | Specifying model outputs for analysis | Running one-at-a-time analyses (local and uncertainty analysis) | Running the Morris (global) sensitivity method | Running variance-based Global Sensitivity Analyses (Sobol and EFAST) | Using the graphical user interface and R script generation | Next steps

Mathematical derivation | Computation of variance-based sensitivity indices | Homma & Saltelli (Sobol) | EFAST | References

Background | Parameters analyzed | Loading the package and simulations | Specifying the input parameters | Specifying the model output | Running the sensitivity analyses | Interpreting the results

Parameters analyzed | Loading the package and simulation | Specifying the input parameters | Specifying the model output | Running the sensitivity analyses | Interpreting the results

1. Introduction | 1.1 Setup | 1.2 Example Data | 2. Examples | 2.1 Examples for Aggregation of Categorical Data | 2.1.1 Minimal Example | 2.1.2 Stratification on X-axis | 2.1.3 Stratification by Color | 2.1.4 Stratification by Color and on X-axis | 2.1.5 Stratification by Column Combination | 2.1.6 Omit Data Points Flagged as Missing Dependent Variable (MDV) | 2.2 Examples for Box-Whisker vs Numeric Data | 2.2.1 Numeric Data as Factor | 2.2.2 Numeric Data as Distinct Numeric Values | 2.2.3 Map Continuous Column with Binning | 2.3 Aggregation Function | 2.3.1 Example for Customized Percentiles | 2.3.2 Example for Customized Aggregation Function | 2.3.3 Tables Corresponding to Plot | 2.4 Show Outliers | 2.4.1 Outliers with Default Settings | 2.4.2 Outliers with Customized Settings | 2.4.3 Retrieve Outliers as Table | 3. Plot Configuration of Boxplots

1. Introduction | 1.1 Setup | 1.2 Example Data | 2. Generating Forest Plots | 2.1 Basic Example | Explanation: | 2.2 Example Without Table | 2.3 Faceting by Data Type

1. Introduction | 1.1 Setup | 1.2 Example Data | 1.2.1 Dataset with Predicted and Observed Data | 1.2.2 Dataset for Examples with DDI Prediction | 1.2.3 Dataset for Ratio Comparison | 2. Predicted vs Observed (plotPredVsObs()) | 2.1 Basic Examples | 2.1.1 Default Settings | 2.1.2 Basic Example: Linear Scale | 2.1.3 Error Bars for Observed Data | 2.1.4 Error Bars for Observed and Predicted Data | 2.1.5 Example with LLOQ | 2.2 Adjust Comparison Lines | 2.2.1 Adjust Fold Distance | 2.2.2 Adjust Display of Lines | 2.3 Adding a Regression Line | 2.4 Add Guest Criteria Lines | 2.5 Use Non-Square Format | 3. Residuals vs Covariate (plotResVsCov()) | 3.1 Basic Examples | 3.1.1 Default Settings | 3.1.2 Linear Scale for Residuals | 3.2 Adjusting Comparison Lines | 3.3 Adding a Regression Line | 4. Ratio Plots (plotRatioVsCov()) | 4.1 Basic Examples | 4.1.1 Default Settings | 4.1.2 Compare Residuals as Ratio | 4.1.3 Using {ospsuite.plots} Specific Aesthetics like MDV and error_relative | 4.2 Qualification of Ratios | 4.3 Add Guest Criteria Lines | 5. Quantile Plot (plotQQ()) | 6 Residuals in Other Plot Functions | 6.1 Residuals as Histogram | 6.2 Stratify Residuals with a Box-Whisker Plot

1. Introduction | 1.1 Setup | 1.2 Example Data | 2. Examples | 2.1 Illustration of Basic Histograms | 2.1.1 Basic Example | 2.1.2 Basic Example: Change of Defaults | 2.1.3 Basic Example: Change of Position but Keep Number of Bins | 2.1.4 Basic Example: Overlay of Histograms | 2.1.5 Omit Data Points Flagged as Missing Dependent Variable (MDV) | 2.1.6 Stratified by a Combination of Columns | 2.1.7 Customization of Binning | 2.2 Frequency | 3. Distribution Fit | 3.1 Fit of a Normal Distribution with Mean as Vertical Line | 3.2 Fit of a Chi-Squared Distribution without Vertical Line | 3.3 Fit of Stacked Data | 3.4 Fit with Frequency TRUE | 3.5 Fit with Frequency TRUE and Stacked Data | 3.6 X-Axis on Log Scale for Distribution Fit | 4. Histogram for Categorical Data

1. Introduction | 2. Styling ospsuite.plots Plots | 2.1 OSP Theme and Custom Scales | 2.1.1 Theme: theme_osp() | 2.1.2 Color: scale_colour_osp() and scale_fill_osp() | 2.1.3 Shape: scale_shape_osp() | 2.1.4 Applying the constructors globally (deprecated) | 2.2 Session Options | 2.2.1 Geom Default Aesthetics | 2.2.2 Statistical and Plotting Behaviour | 2.2.3 Side Features: Watermark and Export | Watermark | Export | 3. Plot Functions | 3.1 plotTimeProfile() | 3.2 plotBoxWhisker() | 3.3 plotHistogram() | 3.4 plotPredVsObs() | 3.5 plotResVsCov() | 3.6 plotRatioVsCov() | 3.7 plotQQ() | 4. Additional Aesthetics | 5. Plot Export | 5.1 Basic Usage | 5.2 Advanced Usage | Adjusting Plot Dimensions Based on Content

1. Introduction | 1.1 Setup | 1.2 Example Data | 2. Binning Methods | 3. Continuous vs. Step Function Plot Types | 4. Generating Range Plots | 4.1 Basic Range Plot | Explanation: | 4.2 Range Plot with Custom Binning | 4.3 Example of Custom Aggregation Function | 4.4 Range Plot with Step Plot Option

1. Introduction | 1.1 Setup | 1.2 Example Data | 1.2.1 Simulated and Observed Data | 1.2.2 MetaData | 2 Examples | 2.1 Plot Simulated Data Only | 2.1.1 Basic Example with Multiple Simulations | 2.1.2 Multiple Simulations with Confidence Interval | 2.2 Plot Observed Data Only | 2.2.1 Basic Example with Multiple Observed Data Sets | 2.2.2 Observed Data Sets with Confidence Interval | 2.2.3 Usage of Aesthetic "Error" | 2.2.4 Observed Data with LLOQ | 2.2.5 Omit Data Points Flagged as Missing Dependent Variable (MDV) | 2.3 Plot Simulated and Observed Data | 2.3.1 Corresponding Simulated and Observed Datasets with Common Legend Entry | 2.3.2 Corresponding Simulated and Observed Datasets with Separate Legend Entries and Mapping Table | 2.3.3 Independent Simulated and Observed Datasets | 2.3.4 Multiple Simulations and Observed Data Sets without Legends | 2.3.5 Observed Data with Shape as Gender | 2.3.6 Data with Secondary Axis | 3. Plot Configuration | 3.1 Example for Changing Geom Attributes | 3.2 Example for Changing Color Scales | 3.2.1 Without Mapping Table | 3.2.2 With Mapping Table | 3.3 Example for Adjusting X and Y Scale | 3.4 Adjust Time Unit

Get started | Basic usage | Spec constructors | integerOption() | numericOption() | characterOption() | logicalOption() | Common parameters | nullAllowed | naAllowed | expectedLength | Error aggregation | Practical example

Get started | Taking advantage of glue and cli formatting | Catching messages | Masking messages

1. Introduction | 2. Label configuration: labels | 2.1. Label and Label configuration objects | 2.2. Label configuration in plots | 2.3. Changing plot labels | 2.4. Smart plot configurations | 3. Background configuration: background | 3.1. Background configuration properties | 3.2. Background configuration in plots | 3.3. Changing background properties | 3.3.1. Set Background | 3.3.2. Set Grid | 3.3.3. Set Watermark | 4. X/Y axes configurations: xAxis/yAxis | 4.1. Axis configuration in plots | Changing axis properties | 5. Legend configuration: legend | 5.1. Legend configuration in plots | 6. Default plot configuration: Theme objects | 7. Exporting/Saving a plot | Export code to re-create a PlotConfiguration object

Loading the workspace | Data type conversion

Introduction to atom plots | initializePlot | addScatter | addLine | addRibbon | addErrorbar

1. Introduction | 2. Definition of the boxplot functions and classes | 2.1. The plotBoxWhisker function | 2.2. The BoxWhiskerDataMapping class | 3. Examples | 3.1. Data | 3.2. Minimal example | 3.3. Difference x vs fill input | 3.4. Boxplot functions | 3.5. Outlier functions | 3.4. plotConfiguration of boxplots: BoxWhiskerPlotConfiguration | 4. Further utility of BoxWhiskerDataMapping

1. Introduction | 2. Illustration of basic histograms | 2.1. Data | 2.2. plotHistogram | 2.3. Minimal examples | 2.4. Examples using data and dataMapping | 2.5. Focus on binning | 2.6. Focus on distribution fit

1. Introduction | 1.1. Objectives | 1.2. Libraries | 1.3. tlf typical workflow | 1.4. Naming Conventions | 2. Data pre-processing: AggregationSummary class | 2.1. Data format | 2.2. MetaData | 2.3. Aggregation | 3. Mapping and grouping of data: DataMapping class | 3.1. GroupMapping | 3.1.1. Grouping class | 3.1.2. GroupMapping class | 3.2. DataMapping

1. Introduction | 2. Illustration of basic PK ratio plots | 2.1. Data | 2.2. plotPKRatio | 2.3. Minimal example | 2.4. Examples with dataMapping | 2.4.1 groupMapping | 2.4.2 PK values defined in lines | 2.5. Qualification of PK Ratios | 2.6. Plot Configuration

1. Introduction | 2. How to start | 3. How to update theme properties | 4. How to export and re-use a theme

1. Introduction | 2. Definition of the time profile functions and classes | 2.1. plotTimeProfile | 2.2. Data Mappings | 2.2.1. dataMapping argument | 2.2.2. observedDataMapping argument | 2.2.3. Remaining TODOs | 3. Examples | 3.1. Plot simulated data only | 3.1.1 Single simulation | 3.1.2. Single simulation with confidence interval | 3.1.3. Multiple simulations | 3.1.4. Multiple simulations with confidence interval | 3.2. Plot observed data only | 3.2.1. Single observed data set | 3.2.2. Single simulation with confidence interval | 3.2.3. Multiple observed data sets | 3.2.4. Multiple observed data sets with confidence interval | 3.2.4. Missing Dependent Variable (mdv) | 3.3. Plot simulated and observed data | 3.3.1. Single simulation and observed data set | 3.3.2. Single simulation and observed data set with their confidence intervals | 3.3.3. Multiple simulations and observed data sets | 3.3.4. Multiple simulations and observed data sets with their confidence intervals | 3.4. Plot Configuration

1. Introduction | 2. How to start | 3. How to import data | 4. How to plot the imported data | 4.1. Select the plot and its variables | 4.2. Update plot properties | 5. Save/export a plot | 6. Plot-specific properties | 6.1. Add a regression line | 6.1. Tornado plot