Package 'ospsuite.reportingframework'

Description

Add biometrics information to config

Usage

addBiometricsToConfig(projectConfiguration, dataDT, overwrite = FALSE)

Arguments

See Also

Other observed data processing: aggregateObservedDataGroups(), convertDataCombinedToDataTable(), convertDataTableToDataCombined(), readObservedDataByDictionary(), updateDataGroupId(), updateOutputPathId(), validateObservedData()

Description

This function adds a default configuration sheet for distribution vs demographics plots to the plot configuration table. It can either create a new sheet or overwrite an existing one based on the specified parameters.

Usage

addDefaultConfigForDistributionsVsDemographics(
  projectConfiguration,
  pkParameterDT = NULL,
  sheetName = "DistributionVsRange",
  overwrite = FALSE
)

Arguments

Details

The function retrieves scenario definitions, output path IDs, and data groups from the project configuration. It checks if the specified sheet already exists and whether to overwrite it. If not, it creates a new header and fills in the default configuration values for the histograms.

Additionally, the function performs a validity check to ensure that it is not executed during a context where helper functions are prohibited (validRun). If such a context is detected, an error is raised to prevent execution.

Value

NULL This function updates the Excel workbook in place and does not return a value.

See Also

Other plot configuration helper function: addDefaultConfigForHistograms(), addDefaultConfigForPKBoxwhsikerPlots(), addDefaultConfigForPKForestPlots(), addDefaultConfigForTimeProfilePlots()

Other functions to generate histograms: plotHistograms(), validateHistogramsConfig()

Description

This function adds a default configuration sheet for histograms to the plot configuration table. It can either create a new sheet or overwrite an existing one based on the specified parameters.

Usage

addDefaultConfigForHistograms(
  projectConfiguration,
  pkParameterDT = NULL,
  sheetName = "Histograms",
  overwrite = FALSE
)

Arguments

Details

The function retrieves scenario definitions, output path IDs, and data groups from the project configuration. It checks if the specified sheet already exists and whether to overwrite it. If not, it creates a new header and fills in the default configuration values for the histograms.

Additionally, the function performs a validity check to ensure that it is not executed during a context where helper functions are prohibited (validRun). If such a context is detected, an error is raised to prevent execution.

Value

NULL This function updates the Excel workbook in place and does not return a value. It is called for its side effects. Add Default Configuration for Histograms

See Also

Other plot configuration helper function: addDefaultConfigForDistributionsVsDemographics(), addDefaultConfigForPKBoxwhsikerPlots(), addDefaultConfigForPKForestPlots(), addDefaultConfigForTimeProfilePlots()

Other functions to generate plots displaying distribution vs demographics: plotDistributionVsDemographics(), validateDistributionVsDemographicsConfig()

Description

This function adds a default configuration sheet for box-and-whisker plots to the plot configuration table. It can either create a new sheet or overwrite an existing one based on the specified parameters.

Usage

addDefaultConfigForPKBoxwhsikerPlots(
  projectConfiguration,
  pkParameterDT,
  sheetName = "PKParameter_Boxplot",
  overwrite = FALSE
)

Arguments

Details

The function retrieves scenario definitions, output path IDs, and data groups from the project configuration. It checks if the specified sheet already exists and whether to overwrite it. If not, it creates a new header and fills in the default configuration values for the time profile plots.

Additionally, the function performs a validity check to ensure that it is not executed during a context where helper functions are prohibited ('validRun'). If such a context is detected, an error is raised to prevent execution.

Value

NULL This function updates the Excel workbook in place and does not return a value. It is called for its side effects.

See Also

Other plot configuration helper function: addDefaultConfigForDistributionsVsDemographics(), addDefaultConfigForHistograms(), addDefaultConfigForPKForestPlots(), addDefaultConfigForTimeProfilePlots()

Other functions to generate box whisker plots: plotPKBoxwhisker(), validatePKBoxwhiskerConfig()

Description

Adds default configurations for forest plots to the 'Plots.xlsx' configuration file.

Usage

addDefaultConfigForPKForestPlots(
  projectConfiguration,
  pkParameterDT,
  sheetName = "PKParameter_Forest",
  overwrite = FALSE
)

Arguments

Value

NULL (invisible).

See Also

plotPKForestAggregatedAbsoluteValues, plotPKForestPointEstimateOfAbsoluteValues, plotPKForestAggregatedRatios, plotPKForestPointEstimateOfRatios,

Other plot functions: addDefaultConfigForTimeProfilePlots(), plotDistributionVsDemographics(), plotHistograms(), plotPKBoxwhisker(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), plotSensitivity(), plotTimeProfiles()

Other plot configuration helper function: addDefaultConfigForDistributionsVsDemographics(), addDefaultConfigForHistograms(), addDefaultConfigForPKBoxwhsikerPlots(), addDefaultConfigForTimeProfilePlots()

Other functions to generate forest plots: plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validatePKForestPointEstimateOfRatiosConfig()

Description

This function adds a default configuration sheet for time profile plots to the plot configuration table. It can either create a new sheet or overwrite an existing one based on the specified parameters.

Usage

addDefaultConfigForTimeProfilePlots(
  projectConfiguration,
  dataObserved = NULL,
  sheetName = "TimeProfiles",
  overwrite = FALSE
)

Arguments

Details

The function retrieves scenario definitions, output path IDs, and data groups from the project configuration. It checks if the specified sheet already exists and whether to overwrite it. If not, it creates a new header and fills in the default configuration values for the time profile plots.

Additionally, the function performs a validity check to ensure that it is not executed during a context where helper functions are prohibited ('validRun'). If such a context is detected, an error is raised to prevent execution.

Value

NULL This function updates the Excel workbook in place and does not return a value. It is called for its side effects.

See Also

Other plot functions: addDefaultConfigForPKForestPlots(), plotDistributionVsDemographics(), plotHistograms(), plotPKBoxwhisker(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), plotSensitivity(), plotTimeProfiles()

Other plot configuration helper function: addDefaultConfigForDistributionsVsDemographics(), addDefaultConfigForHistograms(), addDefaultConfigForPKBoxwhsikerPlots(), addDefaultConfigForPKForestPlots()

Description

Loop on figure keys, add figure or table

Usage

addFiguresAndTables(
  keyList,
  subfolder,
  numbersOf,
  customStyles = list(),
  digitsOfSignificance = 3
)

Arguments

Value

list of numbers and figures after loop

See Also

Other markdown helper function: mdBullet(), mdBullet0(), mdCaption(), mdFigure(), mdFootNote(), mdHeading(), mdLink(), mdNewline(), mdNewpage(), mdPaste(), mdPaste0(), mdTable()

Description

This function is for the usage outside a 'captureLog' bracket. Inside the bracket 'message("my message Text")' can be used

Usage

addMessageToLog(messageText)

Arguments

See Also

Other log file management: captureLog(), initLogfunction(), saveSessionInfo(), setShowLogMessages(), writeTableToLog(), writeToLog()

Description

This function performs linear extrapolation for increasing yValues and logarithmic interpolation for decreasing yValues based on the specified grouping identifiers. It modifies the input dtObserved data.table by adding a new column with the predicted values.

Usage

addPredictedValues(dtObserved, dtSimulated, identifier)

Arguments

Value

A data.table with an additional column named 'predicted' containing the extrapolated and interpolated values.

Description

This function adds a sensitivity table to the provided project configuration. It loads a template Excel file and populates it with parameter paths from the specified scenario.

Usage

addSensitivityTable(
  projectConfiguration,
  scenarioList = NULL,
  scenarioName,
  sheetName = scenarioName
)

Arguments

Value

The modified project configuration object.

Description

This function aggregates observed data based on specified groups and an aggregation method. It allows for different aggregation techniques, including geometric and arithmetic standard deviations, percentiles, or a user-defined custom function.

Usage

aggregateObservedDataGroups(
  dataObserved,
  groups = NULL,
  aggregationFlag = c("GeometricStdDev", "ArithmeticStdDev", "Percentiles", "Custom"),
  percentiles = getOspsuite.plots.option(optionKey = OptionKeys$Percentiles)[c(1, 3, 5)],
  groupSuffix = "aggregated",
  customFunction = NULL,
  lloqCheckColumns2of3 = NULL,
  lloqCheckColumns1of2 = NULL
)

Arguments

Details

The function also checks for values below the Lower Limit of Quantification (LLOQ) and adjusts the aggregated results accordingly. For aggregationFlag 'GeometricStdDev' and 'GeometricStdDev', the statistics at any time point will only be calculated if at least 2/3 of the individual data were measured and were above the lower limit of quantification (lloq). For aggregationFlag 'Percentile', the statistics will only be calculated if less than or equal to 1/2 of the data is above LLOQ. If you use aggregationFlag 'Custom', please set parameters lloqCheckColumns2of3 and lloqCheckColumns1of2 accordingly.

A custom function should take a numeric vector 'y' as input and return a list containing: - 'yValues': The aggregated value (e.g., mean). - 'yMin': The lower value of the aggregated data (e.g., mean - sd). - 'yMax': The upper value of the aggregated data (e.g., mean + sd). - 'yErrorType': A string indicating the type of error associated with the aggregation, it is used in plot legends and captions. It must be a concatenation of the descriptor of yValues and the descriptor of yMin - yMax range separated by "|" (e.g., "mean | standard deviation" or "median | 5th - 95th percentile").

Value

A 'data.table' containing aggregated observed data.

See Also

Other observed data processing: addBiometricsToConfig(), convertDataCombinedToDataTable(), convertDataTableToDataCombined(), readObservedDataByDictionary(), updateDataGroupId(), updateOutputPathId(), validateObservedData()

Description

enumeration biometric units

Usage

BIOMETRICUNITS

Format

An object of class list of length 3.

See Also

Other enumerations: DATACLASS, EXPORTDIR, TIMERANGE

Description

This function calculates pharmacokinetic (PK) parameters for specified scenarios based on project configuration and simulation results. It generates output files for each scenario in the designated output folder.

Usage

calculatePKParameterForScenarios(projectConfiguration, scenarioResults)

Arguments

Value

This function is called for its side effects and does not return a value.

See Also

Other scenario management: createScenarios.wrapped(), loadPKParameter(), loadScenarioResultsToFramework(), runAndSaveScenarios(), runOrLoadScenarios()

Description

function that catches messages, warnings, and errors. This function has to be initialized by 'initLogfunction' function

Usage

captureLog(expr, finallyExpression = invisible())

Arguments

See Also

Other log file management: addMessageToLog(), initLogfunction(), saveSessionInfo(), setShowLogMessages(), writeTableToLog(), writeToLog()

Description

Format corresponds to 'data.table' produced by 'readObservedDataByDictionary'.

Usage

convertDataCombinedToDataTable(datacombined)

Arguments

Value

A 'data.table' containing the converted data.

See Also

Other observed data processing: addBiometricsToConfig(), aggregateObservedDataGroups(), convertDataTableToDataCombined(), readObservedDataByDictionary(), updateDataGroupId(), updateOutputPathId(), validateObservedData()

Description

The 'data.table' must be formatted like a table produced by 'readObservedDataByDictionary'.

Usage

convertDataTableToDataCombined(dataDT)

Arguments

Value

An object of class 'DataCombined'.

See Also

Other observed data processing: addBiometricsToConfig(), aggregateObservedDataGroups(), convertDataCombinedToDataTable(), readObservedDataByDictionary(), updateDataGroupId(), updateOutputPathId(), validateObservedData()

Description

Opens a template file a new document

Usage

createDocumentFromTemplate(
  template = "template_workflow",
  templatePath = system.file("templates", package = "ospsuite.reportingframework")
)

Arguments

See Also

Other Addins Support: createWorkflowTemplate(), openEPackageTemplate(), openFigureTemplate(), openWorkflowTemplate()

Description

Create a 'ProjectConfigurationRF' based on the '"ProjectConfiguration.xlsx"'

based on esqlabsR::ProjectConfiguration but with additional file information for PK Parameter definitions

Usage

createProjectConfiguration(path = file.path("ProjectConfiguration.xlsx"))

Arguments

Value

Object of type 'ProjectConfigurationRF'

See Also

Other project initialization: ProjectConfigurationRF, getQCpassedEnvironmentVariable(), initProject(), setWorkflowOptions()

Description

wrap of 'esqlabsR::createDefaultProjectConfiguration()' with 'esqlabsR::createScenarios()' as input

Usage

createScenarios.wrapped(projectConfiguration, scenarioNames = NULL)

Arguments

Value

Named list of Scenario objects.

See Also

Other scenario management: calculatePKParameterForScenarios(), loadPKParameter(), loadScenarioResultsToFramework(), runAndSaveScenarios(), runOrLoadScenarios()

Description

Opens the workflow template as new document

Usage

createWorkflowTemplate()

See Also

Other Addins Support: createDocumentFromTemplate(), openEPackageTemplate(), openFigureTemplate(), openWorkflowTemplate()

Description

enumeration keys for 'DataClass'

Usage

DATACLASS

Format

An object of class list of length 5.

See Also

Other enumerations: BIOMETRICUNITS, EXPORTDIR, TIMERANGE

Description

enumeration keys for exportDirectories

Usage

EXPORTDIR

Format

An object of class list of length 3.

See Also

Other enumerations: BIOMETRICUNITS, DATACLASS, TIMERANGE

Description

This function generates virtual populations based on demographic data and exports them to CSV files.

Usage

exportRandomPopulations(
  projectConfiguration,
  populationNames = NULL,
  customParameters = NULL,
  overwrite = FALSE
)

Arguments

Value

This function does not return a value. It generates and exports CSV files for the specified populations.

See Also

Other population export: exportVirtualTwinPopulations(), setupVirtualTwinPopConfig()

Examples

## Not run: 
exportRandomPopulations(projectConfiguration,
  populationNames = c("Population1", "Population2"),
  customParameters = list(list(path = "param1", values = c(1, 2))),
  overwrite = TRUE
)

## End(Not run)

Description

This function facilitates the export of a simulation workflow to an electronic package (ePackage). It initializes a 'WorkflowScriptExporter' object and executes a series of operations to prepare the workflow for export, including generating workflow text, retrieving input files, configuring scenario details, and exporting necessary files.

Usage

exportSimulationWorkflowToEPackage(
  projectConfiguration,
  wfIdentifier,
  scenarioNames,
  fileNameReplacements = c()
)

Arguments

Value

Invisible NULL. The function does not return any value but performs a series of operations that result in the creation of exported files and configurations necessary for the ePackage. If successful, the function will complete without errors; otherwise, it will raise an informative error.

See Also

Other electronic package: exportTLFWorkflowToEPackage(), importWorkflow()

Examples

## Not run: 
# Example usage of exportSimulationWorkflow function
exportSimulationWorkflow(
  projectConfiguration = projectConfiguration,
  wfIdentifier = 1,
  scenarioNames = c("Scenario1", "Scenario2"),
  fileNameReplacements = c("oldName.pkml", "new_name.pkml")
)

## End(Not run)

Description

This function facilitates the export of a TLF (Table-Linked Format) workflow to an electronic package (ePackage). It initializes a 'WorkflowScriptExporter' object, extracts code chunks from the provided R Markdown file, evaluates the code chunks, and executes a series of operations to prepare the workflow for export. This includes generating workflow text, retrieving input files, configuring scenario details, and exporting necessary files.

Usage

exportTLFWorkflowToEPackage(
  projectConfiguration,
  wfIdentifier,
  workflowRmd,
  fileNameReplacements = c()
)

Arguments

Value

Invisible NULL. The function does not return any value but performs a series of operations that result in the creation of exported files and configurations necessary for the ePackage. If successful, the function will complete without errors; otherwise, it will raise an informative error.

See Also

Other electronic package: exportSimulationWorkflowToEPackage(), importWorkflow()

Examples

## Not run: 
# Example usage of exportTLFWorkflowToEPackage function
exportTLFWorkflowToEPackage(
  projectConfiguration = projectConfiguration,
  wfIdentifier = 1,
  workflowRmd = "path/to/workflow.Rmd",
  fileNameReplacements = c("oldName.pkml", "new_name.pkml")
)

## End(Not run)

Description

This function generates virtual twin populations based on the provided model and project configuration.

Usage

exportVirtualTwinPopulations(
  projectConfiguration,
  modelFile,
  overwrite = FALSE,
  populationNames = NULL
)

Arguments

See Also

Other population export: exportRandomPopulations(), setupVirtualTwinPopConfig()

Description

This function takes a numeric vector of percentiles and maps specific values to their corresponding labels. It also formats other numeric values based on whether they are integers or not.

Usage

formatPercentiles(percentiles, suffix = "", allAsPercentiles = FALSE)

Arguments

Value

A vector containing the mapped labels for specific percentiles and formatted strings for others.

Description

This function loads the properties for data groups from the specified workbook.

Usage

getDataGroups(wbPlots)

Arguments

Value

A 'data.table' with data group IDs.

See Also

Other get identifier: getModelParameterDefinitions(), getOutputPathIds(), getScenarioDefinitions(), getTimeRangeTags(), loadConfigTableEnvironment()

Description

This function loads the model parameter definitions from the specified workbook.

Usage

getModelParameterDefinitions(wbPlots)

Arguments

Value

A 'data.table' with model parameter definitions.

See Also

Other get identifier: getDataGroups(), getOutputPathIds(), getScenarioDefinitions(), getTimeRangeTags(), loadConfigTableEnvironment()

Description

This function loads the output configurations from the specified workbook.

Usage

getOutputPathIds(wbPlots)

Arguments

Value

A 'data.table' with output configurations.

See Also

Other get identifier: getDataGroups(), getModelParameterDefinitions(), getScenarioDefinitions(), getTimeRangeTags(), loadConfigTableEnvironment()

Description

This function retrieves the value of the environment variable 'QCpassed'. It attempts to convert the value to a logical type. If the environment variable is not set, is empty, or is non-logical, a warning is issued and the function defaults the value to 'FALSE'.

Usage

getQCpassedEnvironmentVariable()

Value

A logical value indicating whether the QC passed ('TRUE' or 'FALSE').

See Also

Other project initialization: ProjectConfigurationRF, createProjectConfiguration(), initProject(), setWorkflowOptions()

Examples

## Not run: 
# Set the environment variable for testing
Sys.setenv(QCpassed = "TRUE")
getQCpassedEnvironmentVariable() # Should return TRUE

# Unset the environment variable for testing
Sys.unsetenv("QCpassed")
getQCpassedEnvironmentVariable() # Should return FALSE and issue a warning

## End(Not run)

Description

This function loads the scenario definitions from the specified workbooks.

Usage

getScenarioDefinitions(wbScenarios, wbPlots = NULL)

Arguments

Value

A 'data.table' with scenario definitions.

See Also

Other get identifier: getDataGroups(), getModelParameterDefinitions(), getOutputPathIds(), getTimeRangeTags(), loadConfigTableEnvironment()

Description

This function loads the time range tags from the specified workbook.

Usage

getTimeRangeTags(wbPlots)

Arguments

Value

A 'data.table' with time range tags.

See Also

Other get identifier: getDataGroups(), getModelParameterDefinitions(), getOutputPathIds(), getScenarioDefinitions(), loadConfigTableEnvironment()

Description

This function imports a workflow from an electronic package (ePackage) into a project directory. It initializes the project configuration, retrieves relevant project files, and synchronizes scenarios with plots. The function ensures that the necessary directories exist and that the configuration is set up correctly for further processing.

Usage

importWorkflow(
  projectDirectory,
  wfIdentifier,
  ePackageFolder,
  configurationDirectory
)

Arguments

Value

An object containing the updated project configuration after the workflow has been imported.

See Also

Other electronic package: exportSimulationWorkflowToEPackage(), exportTLFWorkflowToEPackage()

Description

This function initialize the logging during a workflow. It is called at the start of the workflow script. It is used to configure options for the log file folder, warnings which should not logged and messages which should not logged.

Usage

initLogfunction(
  projectConfiguration,
  warningsNotDisplayed = c("introduced infinite values",
    "Each group consists of only one observation", "rows containing non-finite values",
    "rows containing missing values", "Ignoring unknown parameters",
    "was deprecated in ggplot2", "font family not found in Windows font database",
    "mbcsToSbcs"),
  messagesNotDisplayed = c("Each group consists of only one observation"),
  verbose = TRUE
)

Arguments

See Also

Other log file management: addMessageToLog(), captureLog(), saveSessionInfo(), setShowLogMessages(), writeTableToLog(), writeToLog()

Examples

## Not run: 
# Initialize the log function
logFunction <- initLogfunction(rootDirectory = "path/to/project")

## End(Not run)

Description

This function initializes a project directory by creating necessary subdirectories and copying configuration files from a source Excel file. It reads the directory structure and files specified in the provided Excel template and sets up the project environment accordingly.

Usage

initProject(
  configurationDirectory = ".",
  sourceConfigurationXlsx = system.file("templates", "ProjectConfiguration.xlsx", package
    = "ospsuite.reportingframework"),
  templatePath = system.file("templates", package = "ospsuite.reportingframework"),
  overwrite = FALSE
)

Arguments

Value

This function returns an invisible NULL. It is used for its side effects of creating directories and copying files rather than producing a value.

See Also

Other project initialization: ProjectConfigurationRF, createProjectConfiguration(), getQCpassedEnvironmentVariable(), setWorkflowOptions()

Description

This function loads various configuration tables from specified Excel files into a global environment.

Usage

loadConfigTableEnvironment(projectConfiguration)

Arguments

Value

An invisible value indicating that the loading was successful.

See Also

Other get identifier: getDataGroups(), getModelParameterDefinitions(), getOutputPathIds(), getScenarioDefinitions(), getTimeRangeTags()

Description

This function loads pharmacokinetic (PK) parameters for specified scenarios based on project configuration and a list of scenarios. It processes each scenario and returns the results as a data.table.

Usage

loadPKParameter(projectConfiguration, scenarioListOrResult)

Arguments

Value

A data.table containing the processed PK analyses for all specified scenarios.

A data.table containing the processed PK analyses.

See Also

Other scenario management: calculatePKParameterForScenarios(), createScenarios.wrapped(), loadScenarioResultsToFramework(), runAndSaveScenarios(), runOrLoadScenarios()

Description

This function loads the results of specified scenarios. If the results do not exist, it returns an error.

Usage

loadScenarioResultsToFramework(projectConfiguration, scenarioNames)

Arguments

Value

A list containing the loaded scenario results, including population data if available. throws Error if the scenario results do not exist.

See Also

Other scenario management: calculatePKParameterForScenarios(), createScenarios.wrapped(), loadPKParameter(), runAndSaveScenarios(), runOrLoadScenarios()

Description

Insert a bulleted item, making sure that there are sufficient newlines to build a bulleted list

Usage

mdBullet(..., bullet = "-", level = 1)

Arguments

See Also

Other markdown helper function: addFiguresAndTables(), mdBullet0(), mdCaption(), mdFigure(), mdFootNote(), mdHeading(), mdLink(), mdNewline(), mdNewpage(), mdPaste(), mdPaste0(), mdTable()

Description

Insert a bulleted item, making sure that there are sufficient newlines to build a bulleted list

Usage

mdBullet0(..., bullet = "-", level = 1)

Arguments

See Also

Other markdown helper function: addFiguresAndTables(), mdBullet(), mdCaption(), mdFigure(), mdFootNote(), mdHeading(), mdLink(), mdNewline(), mdNewpage(), mdPaste(), mdPaste0(), mdTable()

Description

add Caption to .Rmd

Usage

mdCaption(subfolder, captionFile, captionPrefix, captionStyle = NULL)

Arguments

See Also

Other markdown helper function: addFiguresAndTables(), mdBullet(), mdBullet0(), mdFigure(), mdFootNote(), mdHeading(), mdLink(), mdNewline(), mdNewpage(), mdPaste(), mdPaste0(), mdTable()

Description

Include a figure file with caption

Usage

mdFigure(
  figureNumber,
  figureFile,
  captionFile,
  footNoteFile = NULL,
  subfolder,
  addNewPage = TRUE,
  customStyles = list()
)

Arguments

See Also

Other markdown helper function: addFiguresAndTables(), mdBullet(), mdBullet0(), mdCaption(), mdFootNote(), mdHeading(), mdLink(), mdNewline(), mdNewpage(), mdPaste(), mdPaste0(), mdTable()

Description

add footnote lines

Usage

mdFootNote(subfolder, footNoteFile, footNoteCustomStyle = NULL)

Arguments

See Also

Other markdown helper function: addFiguresAndTables(), mdBullet(), mdBullet0(), mdCaption(), mdFigure(), mdHeading(), mdLink(), mdNewline(), mdNewpage(), mdPaste(), mdPaste0(), mdTable()

Description

Insert a heading with a specified level.

Usage

mdHeading(..., level = 1, newlines = 2)

Arguments

See Also

Other markdown helper function: addFiguresAndTables(), mdBullet(), mdBullet0(), mdCaption(), mdFigure(), mdFootNote(), mdLink(), mdNewline(), mdNewpage(), mdPaste(), mdPaste0(), mdTable()

Description

Insert a link to another file, and a newline.

Usage

mdLink(label, filename, folder, prefix = "")

Arguments

See Also

Other markdown helper function: addFiguresAndTables(), mdBullet(), mdBullet0(), mdCaption(), mdFigure(), mdFootNote(), mdHeading(), mdNewline(), mdNewpage(), mdPaste(), mdPaste0(), mdTable()

Description

'cat' a line end and start a new line.

Usage

mdNewline(n = 1)

Arguments

See Also

Other markdown helper function: addFiguresAndTables(), mdBullet(), mdBullet0(), mdCaption(), mdFigure(), mdFootNote(), mdHeading(), mdLink(), mdNewpage(), mdPaste(), mdPaste0(), mdTable()

Description

Insert a page break and a newline.

Usage

mdNewpage()

See Also

Other markdown helper function: addFiguresAndTables(), mdBullet(), mdBullet0(), mdCaption(), mdFigure(), mdFootNote(), mdHeading(), mdLink(), mdNewline(), mdPaste(), mdPaste0(), mdTable()

Description

'cat' text using 'paste' and optionally end with newlines.

Usage

mdPaste(..., sep = " ", collapse = NULL, newlines = 1)

Arguments

See Also

Other markdown helper function: addFiguresAndTables(), mdBullet(), mdBullet0(), mdCaption(), mdFigure(), mdFootNote(), mdHeading(), mdLink(), mdNewline(), mdNewpage(), mdPaste0(), mdTable()

Description

'cat' text using 'paste0' and optionally end with newlines.

Usage

mdPaste0(..., collapse = NULL, newlines = 1)

Arguments

See Also

Other markdown helper function: addFiguresAndTables(), mdBullet(), mdBullet0(), mdCaption(), mdFigure(), mdFootNote(), mdHeading(), mdLink(), mdNewline(), mdNewpage(), mdPaste(), mdTable()

Description

Insert a table into a markdown document. Essentially a wrapper for 'knitr::kable' with 'format = "markdown"' and newlines.

Usage

mdTable(
  tableNumber,
  tableCsv,
  captionFile,
  footNoteFile,
  subfolder,
  customStyles,
  digitsOfSignificance = 3,
  addNewPage = TRUE,
  ...
)

Arguments

Value

'x', invisibly

See Also

Other markdown helper function: addFiguresAndTables(), mdBullet(), mdBullet0(), mdCaption(), mdFigure(), mdFootNote(), mdHeading(), mdLink(), mdNewline(), mdNewpage(), mdPaste(), mdPaste0()

Description

Creates Rmd file which include specified .Rmd s

Usage

mergeRmds(
  newName = "appendix",
  title = "Appendix",
  sourceRmds = c("Demographics", "TimeProfile", "PKParameter", "DDIRatio", "myFigures"),
  projectConfiguration
)

Arguments

Description

Opens the template for figure creation as new document

Usage

openEPackageTemplate()

See Also

Other Addins Support: createDocumentFromTemplate(), createWorkflowTemplate(), openFigureTemplate(), openWorkflowTemplate()

Description

Opens the template for figure creation as new document

Usage

openFigureTemplate()

See Also

Other Addins Support: createDocumentFromTemplate(), createWorkflowTemplate(), openEPackageTemplate(), openWorkflowTemplate()

Description

Per default the workflow template of the package is loaded. To customize it save the file with name "template_workflow.R" in your template directory and set the path to the template via option ‘options(OSPSuite.RF.PathForWorkflowTemplate = ’myTemplateDirectory')'

Usage

openWorkflowTemplate()

See Also

Other Addins Support: createDocumentFromTemplate(), createWorkflowTemplate(), openEPackageTemplate(), openFigureTemplate()

Description

This function visualizes the relationship between predicted and observed values, allowing for easy identification of discrepancies.

Usage

ospsuite_plotPredictedVsObserved(
  plotData,
  metaData = NULL,
  mapping = ggplot2::aes(),
  ...
)

Arguments

Value

A 'ggplot2' plot object representing predicted vs observed values.

Examples

## Not run: 
# Generate a predicted vs observed plot for the provided data
plotPredictedVsObserved(convertUnits(
  myDataCombined,
  xUnit = ospUnits$Time$h,
  yUnit = ospUnits$`Concentration [mass]`$`µg/l`
))

## End(Not run)

Description

This function visualizes the distribution of predicted vs observed values using a Q-Q plot.

Usage

ospsuite_plotQuantileQuantilePlot(
  plotData,
  metaData = NULL,
  mapping = ggplot2::aes(),
  ...
)

Arguments

Value

A 'ggplot2' plot object representing the Q-Q plot.

Examples

## Not run: 
# Generate a Q-Q plot for the provided data
plotQuantileQuantilePlot(convertUnits(
  myDataCombined,
  xUnit = ospUnits$Time$h,
  yUnit = ospUnits$`Concentration [mass]`$`µg/l`
))

## End(Not run)

Description

This function generates a histogram of the residuals, providing a visual representation of their distribution.

Usage

ospsuite_plotResidualsAsHistogram(
  plotData,
  metaData = NULL,
  mapping = ggplot2::aes(),
  ...
)

Arguments

Value

A 'ggplot2' plot object representing the histogram of residuals.

Examples

## Not run: 
# Generate a histogram of residuals for the provided data
plotResidualsAsHistogram(convertUnits(
  myDataCombined,
  xUnit = ospUnits$Time$h,
  yUnit = ospUnits$`Concentration [mass]`$`µg/l`
))

## End(Not run)

Description

This function visualizes the residuals against observed values, helping to assess model performance.

Usage

ospsuite_plotResidualsVsObserved(
  plotData,
  metaData = NULL,
  mapping = ggplot2::aes(),
  ...
)

Arguments

Value

A 'ggplot2' plot object representing residuals vs observed values.

Examples

## Not run: 
# Generate a residuals vs observed plot for the provided data
plotResidualsVsObserved(convertUnits(
  myDataCombined,
  xUnit = ospUnits$Time$h,
  yUnit = ospUnits$`Concentration [mass]`$`µg/l`
))

## End(Not run)

Description

This function visualizes the residuals over time, providing insights into the accuracy of the model predictions.

Usage

ospsuite_plotResidualsVsTime(
  plotData,
  metaData = NULL,
  mapping = ggplot2::aes(),
  ...
)

Arguments

Value

A 'ggplot2' plot object representing residuals vs time.

Examples

## Not run: 
# Generate a residuals vs time plot for the provided data
plotResidualsVsTime(convertUnits(
  myDataCombined,
  xUnit = ospUnits$Time$h,
  yUnit = ospUnits$`Concentration [mass]`$`µg/l`
))

## End(Not run)

Description

This function generates a time profile plot using ggplot2, where the data is grouped by a column named "group".

Usage

ospsuite_plotTimeProfile(
  plotData,
  metaData = NULL,
  mapping = ggplot2::aes(),
  observedMapping = mapping,
  ...
)

Arguments

Value

A 'ggplot2' plot object representing the time profile.

Examples

## Not run: 
# Generate a time profile plot for the provided data
plotTimeProfile(convertUnits(
  myDataCombined,
  xUnit = ospUnits$Time$h,
  yUnit = ospUnits$`Concentration [mass]`$`µg/l`
))

## End(Not run)

Description

Generates plots comparing distributions against demographic data based on the provided configuration and data.

Usage

plotDistributionVsDemographics(
  projectConfiguration,
  onePlotConfig,
  pkParameterDT = NULL,
  scenarioList = NULL,
  asStepPlot = TRUE,
  aggregationFlag = c("Percentiles", "GeometricStdDev", "ArithmeticStdDev", "Custom"),
  customFunction = NULL,
  percentiles = ospsuite.plots::getOspsuite.plots.option(optionKey =
    OptionKeys$Percentiles)[c(1, 3, 5)],
  facetAspectRatio = 0.5,
  colorVector = c(scenario = NA, referenceScenario = NA),
  ...
)

Arguments

Details

This function is intended to be used in combination with the 'runPlot' function. Refer to the vignette 'Plot and Report Generation' for more details. It can be configured to plot PK parameters or population parameters, with the X-axis always displaying the population parameter.

A helper function, 'addDefaultConfigForDistributionsVsDemographics', is available to streamline the setup of your Excel configuration sheet for histograms. This function automatically populates a new sheet with default values that can be customized as needed.

Value

A list of plot objects generated based on the input configuration.

See Also

Other plot functions: addDefaultConfigForPKForestPlots(), addDefaultConfigForTimeProfilePlots(), plotHistograms(), plotPKBoxwhisker(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), plotSensitivity(), plotTimeProfiles()

Other functions to generate plots displaying distribution vs demographics: addDefaultConfigForHistograms(), validateDistributionVsDemographicsConfig()

Examples

## Not run: 
# Example for plotting a histogram of PK-parameters of a scenario compared to a reference scenario:
runPlot(
  nameOfplotFunction = "plotDistributionVsDemographics",
  projectConfiguration = projectConfiguration,
  configTableSheet = "DistributionVsRange",
  inputs = list(
    scenarioList = scenarioList,
    pkParameterDT = pkParameterDT,
    aggregationFlag = "GeometricStdDev"
  )
)

## End(Not run)

Description

Generates histogram plots based on the provided configuration and data.

Usage

plotHistograms(
  projectConfiguration,
  onePlotConfig,
  pkParameterDT = NULL,
  scenarioList = NULL,
  facetAspectRatio = 0.5,
  colorVector = c(scenario = NA, referenceScenario = NA),
  nMaxFacetRows = 2,
  ...
)

Arguments

Details

This function is intended to be used in combination with the 'runPlot' function. Refer to the vignette 'Plot and Report Generation' for more details. It can be configured to plot PK parameters or model parameters.

A helper function, 'addDefaultConfigForHistograms', is available to streamline the setup of your Excel configuration sheet for histograms. This function automatically populates a new sheet with default values that can be customized as needed.

Value

A list of histogram plot objects generated based on the input configuration.

See Also

Other plot functions: addDefaultConfigForPKForestPlots(), addDefaultConfigForTimeProfilePlots(), plotDistributionVsDemographics(), plotPKBoxwhisker(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), plotSensitivity(), plotTimeProfiles()

Other functions to generate histograms: addDefaultConfigForDistributionsVsDemographics(), validateHistogramsConfig()

Examples

## Not run: 
# Example for plotting a histogram of PK-parameters of a scenario compared to a reference scenario:
runPlot(
  nameOfplotFunction = "plotHistograms",
  projectConfiguration = projectConfiguration,
  configTableSheet = "HistogramTest",
  inputs = list(
    scenarioList = scenarioList,
    pkParameterDT = pkParameterDT,
    colorVector = c(
      "PO application" = "red",
      "IV application" = "green"
    )
  )
)

## End(Not run)

Description

Generates box-and-whisker plots for pharmacokinetic (PK) parameters based on a provided project configuration and plot configuration. This function creates visual representations of the distribution of PK parameters across different scenarios, allowing for comparison of absolute values and ratios.

Usage

plotPKBoxwhisker(
  projectConfiguration,
  onePlotConfig,
  pkParameterDT,
  percentiles = getOspsuite.plots.option(optionKey = OptionKeys$Percentiles),
  xAxisTextAngle = 0,
  colorVector = c(scenario = NA, referenceScenario = NA),
  facetAspectRatio = 0.5,
  ...
)

Arguments

Details

The function is intended to be used in combination with the 'runPlot' function. See the vignette 'Plot and Report Generation' for more details.

There exists a helper function 'addDefaultConfigForPKBoxwhsikerPlots' which is a helpful utility designed to streamline the process of setting up your Excel configuration sheet for box whisker plots. This function automatically populates a new sheet with default values that you can customize according to your needs.

Value

A list of ggplot objects representing the generated plots. Each plot can be rendered using ggplot2 or similar plotting systems. The list may include both absolute and ratio plots depending on the configuration.

See Also

Other plot functions: addDefaultConfigForPKForestPlots(), addDefaultConfigForTimeProfilePlots(), plotDistributionVsDemographics(), plotHistograms(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), plotSensitivity(), plotTimeProfiles()

Other functions to generate box whisker plots: addDefaultConfigForPKBoxwhsikerPlots(), validatePKBoxwhiskerConfig()

Examples

# Example usage of plotPKBoxwhisker
## Not run: 
plotList <- plotPKBoxwhisker(
  projectConfiguration = myProjectConfig,
  onePlotConfig = myPlotConfig,
  pkParameterDT = myPKData,
  percentiles = c(0.05, 0.25, 0.5, 0.75, 0.95),
  xAxisTextAngle = 45,
  colorVector = c(DDI = "red", control = "blue"),
  facetAspectRatio = 1,
)

## End(Not run)

Description

This is one of a group of functions which generates PK forest plots for pharmacokinetic (PK) parameters or population parameter, providing visualizations that can include absolute values or ratios, along with options for displaying variance or point estimates. These functions allow for the comparison of simulated and observed PK parameter data across different scenarios, aiding in the interpretation of variability and uncertainty in the estimates.

The aggregation of data can be customized using the 'aggregationFlag' parameter, which allows for options such as "GeometricStdDev", "ArithmeticStdDev", "Percentiles", and "Custom" functions for calculating summary statistics.

Available functions are:

plotPKForestAggregatedAbsoluteValues

Generates a PK forest plot displaying absolute values along with variance.

plotPKForestPointEstimateOfAbsoluteValues

Generates a PK forest plot displaying point estimates.

plotPKForestAggregatedRatios

Generates a PK forest plot displaying ratios of PK parameters along with variance.

plotPKForestPointEstimateOfRatios

Generates a PK forest plot displaying point estimates of ratios of PK parameters.

Usage

plotPKForestAggregatedAbsoluteValues(
  projectConfiguration,
  onePlotConfig,
  pkParameterDT,
  dataObservedPK = NULL,
  aggregationFlag = c("GeometricStdDev", "ArithmeticStdDev", "Percentiles", "Custom"),
  percentiles = getOspsuite.plots.option(optionKey = OptionKeys$Percentiles)[c(1, 3, 5)],
  customFunction = NULL,
  scaleVectors = list(),
  labelWrapWidth = 10,
  vlineIntercept = NULL,
  withTable = TRUE,
  relWidths = NULL,
  digitsToRound = 2,
  digitsToShow = 2
)

Arguments

Details

The function distinguishes between two cases for ratio calculations:

Case 1:

If the scenarios being compared are based on the same populations (same 'PopulationId' in scenario configuration), the plots will show summary statistics of individual ratios.

Case 2:

If the scenarios are based on different populations, the plots will display the ratio of summary statistics. This ratio is only available for plotPKForestPointEstimateOfRatios

The uncertainty in estimates is calculated using a bootstrapping approach. A unique seed is set for each combination of scenario, reference scenario (if available), output path ID, and PK parameter identifier to ensure reproducibility. The number of bootstrap samples is defined by the 'nBootstrap' parameter, and the confidence intervals are determined using the 'confLevel' parameter (e.g., 0.9 for 90

For the bootstrapping process, a unique seed is set for each combination of scenario, 'referenceScenario' (if available), 'outputPathId', and 'PKParameter' identifier. This ensures that results are consistent when a specific combination is used in different plots and that they are reproducible.

In addition to the point estimates and their confidence intervals, the precision watermark is an important feature of these plots. The precision watermark indicates whether the precision requirements for the displayed estimates have been met. The precision is calculated based on the relationship between the estimated values ('xValues') and their corresponding minimum ('xMin') and maximum ('xMax') confidence interval bounds. If the calculated precision falls below a predefined threshold (e.g., 0.01), the watermark is activated, displaying a warning label such as "Outside precision requirement" on the plot. The threshold is set by the option 'OSPSuite.RF.RequiredPrecisison' This alert serves to inform users that the estimates may not be reliable due to insufficient sample sizes or high variability in the data, prompting them to consider increasing the sample size for improved precision. If the precision is sufficient, point estimates are displayed without confidence interval bounds.

By including the precision watermark, users are better equipped to assess the reliability of the point estimates and make informed decisions based on the visualized data.

To support the creation of the configuration table a support function addDefaultConfigForPKForestPlots is available

Value

A list containing the generated plot objects.

See Also

Other plot functions: addDefaultConfigForPKForestPlots(), addDefaultConfigForTimeProfilePlots(), plotDistributionVsDemographics(), plotHistograms(), plotPKBoxwhisker(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), plotSensitivity(), plotTimeProfiles()

Other functions to generate forest plots: addDefaultConfigForPKForestPlots(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validatePKForestPointEstimateOfRatiosConfig()

Description

This is one of a group of functions which generates PK forest plots for pharmacokinetic (PK) parameters or population parameter, providing visualizations that can include absolute values or ratios, along with options for displaying variance or point estimates. These functions allow for the comparison of simulated and observed PK parameter data across different scenarios, aiding in the interpretation of variability and uncertainty in the estimates.

The aggregation of data can be customized using the 'aggregationFlag' parameter, which allows for options such as "GeometricStdDev", "ArithmeticStdDev", "Percentiles", and "Custom" functions for calculating summary statistics.

Available functions are:

plotPKForestAggregatedAbsoluteValues

Generates a PK forest plot displaying absolute values along with variance.

plotPKForestPointEstimateOfAbsoluteValues

Generates a PK forest plot displaying point estimates.

plotPKForestAggregatedRatios

Generates a PK forest plot displaying ratios of PK parameters along with variance.

plotPKForestPointEstimateOfRatios

Generates a PK forest plot displaying point estimates of ratios of PK parameters.

Usage

plotPKForestAggregatedRatios(
  projectConfiguration,
  onePlotConfig,
  pkParameterDT,
  dataObservedPK = NULL,
  aggregationFlag = c("GeometricStdDev", "ArithmeticStdDev", "Percentiles", "Custom"),
  percentiles = getOspsuite.plots.option(optionKey = OptionKeys$Percentiles)[c(1, 3, 5)],
  customFunction = NULL,
  scaleVectors = list(),
  labelWrapWidth = 10,
  vlineIntercept = NULL,
  withTable = TRUE,
  relWidths = NULL,
  digitsToRound = 2,
  digitsToShow = 2
)

Arguments

Details

The function distinguishes between two cases for ratio calculations:

Case 1:

If the scenarios being compared are based on the same populations (same 'PopulationId' in scenario configuration), the plots will show summary statistics of individual ratios.

Case 2:

If the scenarios are based on different populations, the plots will display the ratio of summary statistics. This ratio is only available for plotPKForestPointEstimateOfRatios

The uncertainty in estimates is calculated using a bootstrapping approach. A unique seed is set for each combination of scenario, reference scenario (if available), output path ID, and PK parameter identifier to ensure reproducibility. The number of bootstrap samples is defined by the 'nBootstrap' parameter, and the confidence intervals are determined using the 'confLevel' parameter (e.g., 0.9 for 90

For the bootstrapping process, a unique seed is set for each combination of scenario, 'referenceScenario' (if available), 'outputPathId', and 'PKParameter' identifier. This ensures that results are consistent when a specific combination is used in different plots and that they are reproducible.

In addition to the point estimates and their confidence intervals, the precision watermark is an important feature of these plots. The precision watermark indicates whether the precision requirements for the displayed estimates have been met. The precision is calculated based on the relationship between the estimated values ('xValues') and their corresponding minimum ('xMin') and maximum ('xMax') confidence interval bounds. If the calculated precision falls below a predefined threshold (e.g., 0.01), the watermark is activated, displaying a warning label such as "Outside precision requirement" on the plot. The threshold is set by the option 'OSPSuite.RF.RequiredPrecisison' This alert serves to inform users that the estimates may not be reliable due to insufficient sample sizes or high variability in the data, prompting them to consider increasing the sample size for improved precision. If the precision is sufficient, point estimates are displayed without confidence interval bounds.

By including the precision watermark, users are better equipped to assess the reliability of the point estimates and make informed decisions based on the visualized data.

To support the creation of the configuration table a support function addDefaultConfigForPKForestPlots is available

Value

A list containing the generated plot objects.

See Also

Other plot functions: addDefaultConfigForPKForestPlots(), addDefaultConfigForTimeProfilePlots(), plotDistributionVsDemographics(), plotHistograms(), plotPKBoxwhisker(), plotPKForestAggregatedAbsoluteValues(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), plotSensitivity(), plotTimeProfiles()

Other functions to generate forest plots: addDefaultConfigForPKForestPlots(), plotPKForestAggregatedAbsoluteValues(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validatePKForestPointEstimateOfRatiosConfig()

Description

This is one of a group of functions which generates PK forest plots for pharmacokinetic (PK) parameters or population parameter, providing visualizations that can include absolute values or ratios, along with options for displaying variance or point estimates. These functions allow for the comparison of simulated and observed PK parameter data across different scenarios, aiding in the interpretation of variability and uncertainty in the estimates.

The aggregation of data can be customized using the 'aggregationFlag' parameter, which allows for options such as "GeometricStdDev", "ArithmeticStdDev", "Percentiles", and "Custom" functions for calculating summary statistics.

Available functions are:

plotPKForestAggregatedAbsoluteValues

Generates a PK forest plot displaying absolute values along with variance.

plotPKForestPointEstimateOfAbsoluteValues

Generates a PK forest plot displaying point estimates.

plotPKForestAggregatedRatios

Generates a PK forest plot displaying ratios of PK parameters along with variance.

plotPKForestPointEstimateOfRatios

Generates a PK forest plot displaying point estimates of ratios of PK parameters.

Usage

plotPKForestPointEstimateOfAbsoluteValues(
  projectConfiguration,
  onePlotConfig,
  pkParameterDT,
  dataObservedPK = NULL,
  statFun = c(`geometric mean` = function(y) exp(mean(log(y[y > 0])))),
  confLevel = 0.9,
  nBootstrap = 100,
  scaleVectors = list(),
  labelWrapWidth = 10,
  vlineIntercept = NULL,
  withTable = TRUE,
  relWidths = NULL,
  digitsToRound = 2,
  digitsToShow = 2
)

Arguments

Details

The function distinguishes between two cases for ratio calculations:

Case 1:

If the scenarios being compared are based on the same populations (same 'PopulationId' in scenario configuration), the plots will show summary statistics of individual ratios.

Case 2:

If the scenarios are based on different populations, the plots will display the ratio of summary statistics. This ratio is only available for plotPKForestPointEstimateOfRatios

The uncertainty in estimates is calculated using a bootstrapping approach. A unique seed is set for each combination of scenario, reference scenario (if available), output path ID, and PK parameter identifier to ensure reproducibility. The number of bootstrap samples is defined by the 'nBootstrap' parameter, and the confidence intervals are determined using the 'confLevel' parameter (e.g., 0.9 for 90

For the bootstrapping process, a unique seed is set for each combination of scenario, 'referenceScenario' (if available), 'outputPathId', and 'PKParameter' identifier. This ensures that results are consistent when a specific combination is used in different plots and that they are reproducible.

In addition to the point estimates and their confidence intervals, the precision watermark is an important feature of these plots. The precision watermark indicates whether the precision requirements for the displayed estimates have been met. The precision is calculated based on the relationship between the estimated values ('xValues') and their corresponding minimum ('xMin') and maximum ('xMax') confidence interval bounds. If the calculated precision falls below a predefined threshold (e.g., 0.01), the watermark is activated, displaying a warning label such as "Outside precision requirement" on the plot. The threshold is set by the option 'OSPSuite.RF.RequiredPrecisison' This alert serves to inform users that the estimates may not be reliable due to insufficient sample sizes or high variability in the data, prompting them to consider increasing the sample size for improved precision. If the precision is sufficient, point estimates are displayed without confidence interval bounds.

By including the precision watermark, users are better equipped to assess the reliability of the point estimates and make informed decisions based on the visualized data.

To support the creation of the configuration table a support function addDefaultConfigForPKForestPlots is available

Value

A list containing the generated plot objects.

See Also

Other plot functions: addDefaultConfigForPKForestPlots(), addDefaultConfigForTimeProfilePlots(), plotDistributionVsDemographics(), plotHistograms(), plotPKBoxwhisker(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfRatios(), plotSensitivity(), plotTimeProfiles()

Other functions to generate forest plots: addDefaultConfigForPKForestPlots(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfRatios(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validatePKForestPointEstimateOfRatiosConfig()

Description

This is one of a group of functions which generates PK forest plots for pharmacokinetic (PK) parameters or population parameter, providing visualizations that can include absolute values or ratios, along with options for displaying variance or point estimates. These functions allow for the comparison of simulated and observed PK parameter data across different scenarios, aiding in the interpretation of variability and uncertainty in the estimates.

The aggregation of data can be customized using the 'aggregationFlag' parameter, which allows for options such as "GeometricStdDev", "ArithmeticStdDev", "Percentiles", and "Custom" functions for calculating summary statistics.

Available functions are:

plotPKForestAggregatedAbsoluteValues

Generates a PK forest plot displaying absolute values along with variance.

plotPKForestPointEstimateOfAbsoluteValues

Generates a PK forest plot displaying point estimates.

plotPKForestAggregatedRatios

Generates a PK forest plot displaying ratios of PK parameters along with variance.

plotPKForestPointEstimateOfRatios

Generates a PK forest plot displaying point estimates of ratios of PK parameters.

Usage

plotPKForestPointEstimateOfRatios(
  projectConfiguration,
  onePlotConfig,
  pkParameterDT,
  dataObservedPK = NULL,
  nBootstrap = 1000,
  confLevel = 0.9,
  statFun = c(`geometric mean` = function(y) exp(mean(log(y[y > 0])))),
  scaleVectors = list(),
  labelWrapWidth = 10,
  vlineIntercept = c(1),
  withTable = TRUE,
  relWidths = NULL,
  digitsToRound = 2,
  digitsToShow = 2
)

Arguments

Details

The function distinguishes between two cases for ratio calculations:

Case 1:

If the scenarios being compared are based on the same populations (same 'PopulationId' in scenario configuration), the plots will show summary statistics of individual ratios.

Case 2:

If the scenarios are based on different populations, the plots will display the ratio of summary statistics. This ratio is only available for plotPKForestPointEstimateOfRatios

The uncertainty in estimates is calculated using a bootstrapping approach. A unique seed is set for each combination of scenario, reference scenario (if available), output path ID, and PK parameter identifier to ensure reproducibility. The number of bootstrap samples is defined by the 'nBootstrap' parameter, and the confidence intervals are determined using the 'confLevel' parameter (e.g., 0.9 for 90

For the bootstrapping process, a unique seed is set for each combination of scenario, 'referenceScenario' (if available), 'outputPathId', and 'PKParameter' identifier. This ensures that results are consistent when a specific combination is used in different plots and that they are reproducible.

In addition to the point estimates and their confidence intervals, the precision watermark is an important feature of these plots. The precision watermark indicates whether the precision requirements for the displayed estimates have been met. The precision is calculated based on the relationship between the estimated values ('xValues') and their corresponding minimum ('xMin') and maximum ('xMax') confidence interval bounds. If the calculated precision falls below a predefined threshold (e.g., 0.01), the watermark is activated, displaying a warning label such as "Outside precision requirement" on the plot. The threshold is set by the option 'OSPSuite.RF.RequiredPrecisison' This alert serves to inform users that the estimates may not be reliable due to insufficient sample sizes or high variability in the data, prompting them to consider increasing the sample size for improved precision. If the precision is sufficient, point estimates are displayed without confidence interval bounds.

By including the precision watermark, users are better equipped to assess the reliability of the point estimates and make informed decisions based on the visualized data.

To support the creation of the configuration table a support function addDefaultConfigForPKForestPlots is available

Value

A list containing the generated plot objects.

See Also

Other plot functions: addDefaultConfigForPKForestPlots(), addDefaultConfigForTimeProfilePlots(), plotDistributionVsDemographics(), plotHistograms(), plotPKBoxwhisker(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotSensitivity(), plotTimeProfiles()

Other functions to generate forest plots: addDefaultConfigForPKForestPlots(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validatePKForestPointEstimateOfRatiosConfig()

Description

Generates sensitivity plots based on the provided project configuration and scenario list.

Usage

plotSensitivity(projectConfiguration, onePlotConfig, scenarioList)

Arguments

Value

A list of plots and tables, where each entry corresponds to a generated sensitivity plot or its data.

See Also

Other plot functions: addDefaultConfigForPKForestPlots(), addDefaultConfigForTimeProfilePlots(), plotDistributionVsDemographics(), plotHistograms(), plotPKBoxwhisker(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), plotTimeProfiles()

Description

The 'plotTimeProfiles' function creates a series of time profile plots as facet panels from the provided simulated and observed data. This function is designed to visualize complex time-dependent data by allowing for multiple plots organized in a user-defined layout.

Usage

plotTimeProfiles(
  projectConfiguration,
  onePlotConfig,
  dataObserved = NULL,
  scenarioResults,
  nMaxFacetRows = 4,
  facetAspectRatio = 0.5,
  aggregationFlag = c("GeometricStdDev", "ArithmeticStdDev", "Percentiles", "Custom"),
  percentiles = getOspsuite.plots.option(optionKey = OptionKeys$Percentiles)[c(1, 3, 5)],
  customFunction = NULL,
  checkForUnusedData = FALSE,
  referenceScaleVector = list(),
  ...
)

Arguments

Details

The function primarily focuses on simulated scenarios, with each panel displaying the result of one scenario, or one scenario versus a reference scenario. Plots containing more than one scenario or only observed data are not supported.

The function is intended to be used in combination with the 'runPlot' function. See the vignette 'Plot and Report Generation' for more details.

There exists a helper function 'addDefaultConfigForTimeProfilePlots' which is a helpful utility designed to streamline the process of setting up your Excel configuration sheet for time profile plots. This function automatically populates a new sheet with default values that you can customize according to your needs.

There is a tutorial for this function with many examples in the accompanying vignette: 'Time Profile Plotting Tutorial'.

Value

A list of generated time profile plots

See Also

Other plot functions: addDefaultConfigForPKForestPlots(), addDefaultConfigForTimeProfilePlots(), plotDistributionVsDemographics(), plotHistograms(), plotPKBoxwhisker(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), plotSensitivity()

Examples

## Not run: 
# Example of using the referenceScaleVector parameter for a DDI Analysis
referenceScaleVector <- list(
  DDI = c("#843C39FF", "#AD494AFF"),
  Control = c("#3182BDFF", "#6BAED6FF")
)
# The scenario would be labeled as 'DDI', with aesthetic color as dark red ("#843C39FF") and
# aesthetic fill as light red ("#AD494AFF").
# The reference scenario would be labeled 'Control',
# with aesthetic color as dark blue ("#3182BDFF") and
# aesthetic fill as light blue ("#6BAED6FF").

# Example of using the referenceScaleVector parameter for a comparison between a pediatric
# and an adult simulation using the default colors
referenceScaleVector <- list(
  "Pediatric population" = c(NA, NA),
  "Adult population" = c(NA, NA)
)

# The scenario would be labeled as 'Pediatric population', using the default colors with aesthetic
# color dark blue ("#3182BDFF") and aesthetic fill as light blue ("#6BAED6FF").
# The reference scenario would be labeled 'Adult population', with both aesthetic color and fill
# as 'grey'.

# Example of using the aggregationFlag parameter "Custom"
plotList <- runPlot(
  nameOfplotFunction = "plotTimeProfiles",
  configTableSheet = "myTimeProfile",
  projectConfiguration = projectConfiguration,
  suppressExport = TRUE,
  inputs = list(
    scenarioResults = scenarioResults,
    dataObserved = dataObserved,
    aggregationFlag = c("Custom"),
    customFunction = function(y) {
      list(
        yValues = mean(y),
        yMin = mean(y) - sd(y),
        yMax = mean(y) + sd(y),
        yErrorType = "mean | standard deviation"
      )
    }
  )
)

# Example of using the aggregationFlag parameter "Percentiles"
plotList <- runPlot(
  nameOfplotFunction = "plotTimeProfiles",
  configTableSheet = "myTimeProfile",
  projectConfiguration = projectConfiguration,
  suppressExport = TRUE,
  inputs = list(
    scenarioResults = scenarioResults,
    dataObserved = observedData,
    aggregationFlag = c("Percentiles"),
    percentiles = c(0.025, 0.5, 0.975)
  )
)

## End(Not run)

Description

An object storing configuration used project-wide

Super class

esqlabsR::ProjectConfiguration -> ProjectConfigurationRF

Active bindings

Methods

Public methods

• ProjectConfigurationRF$new()

• ProjectConfigurationRF$print()

• ProjectConfigurationRF$addAddOnFileToConfiguration()

• ProjectConfigurationRF$addAddOnFolderToConfiguration()

• ProjectConfigurationRF$clone()

Method new()

Initializes the ProjectConfiguration object with a specified configuration file path.

Adds new line to configuration xlsx

Read configuration from file Initialize

Usage

ProjectConfigurationRF$new(projectConfigurationFilePath = character())

Arguments

Method print()

print prints a summary of the Project Configuration.

Usage

ProjectConfigurationRF$print(className = TRUE)

Arguments

Method addAddOnFileToConfiguration()

Adds an add-on file to the project configuration.

Usage

ProjectConfigurationRF$addAddOnFileToConfiguration(
  property,
  value,
  description,
  templatePath
)

Arguments

Method addAddOnFolderToConfiguration()

Adds an add-on file to the project configuration.

Usage

ProjectConfigurationRF$addAddOnFolderToConfiguration(
  property,
  value,
  description
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

ProjectConfigurationRF$clone(deep = FALSE)

Arguments

See Also

Other project initialization: createProjectConfiguration(), getQCpassedEnvironmentVariable(), initProject(), setWorkflowOptions()

Description

This function reads and processes data based on the provided project configuration.

Usage

readObservedDataByDictionary(
  projectConfiguration,
  spreadData = TRUE,
  dataClassType = c("timeprofile", "pkParameter"),
  fileIds = NULL
)

Arguments

Value

A 'data.table' containing the processed data based on the dictionary. The structure includes relevant columns defined in the data dictionary.

See Also

Other observed data processing: addBiometricsToConfig(), aggregateObservedDataGroups(), convertDataCombinedToDataTable(), convertDataTableToDataCombined(), updateDataGroupId(), updateOutputPathId(), validateObservedData()

Description

converts .Rmd file to word

Usage

renderWord(
  fileName,
  wordConversionTemplate = NULL,
  customStyles = list(FigureCaption = NULL, FigureFootnote = NULL, TableCaption = NULL,
    TableFootnote = NULL),
  ...
)

Arguments

Examples

## Not run: 
# Example with customstyles for Footnotes
renderWord(
  fileName = "myReport.Rmd",
  wordConversionTemplate = "path/to/template/myTemplate.docx",
  customStyles = list(FigureFootnote = "myFootnoteFormat", TableFootnote = "myFootnoteFormat")
)

## End(Not run)

Description

This function simulates a list of scenarios and saves the results. If results already exist for a scenario, it will overwrite them based on the provided options.

Usage

runAndSaveScenarios(
  projectConfiguration,
  scenarioList,
  simulationRunOptions = NULL,
  ...
)

Arguments

Value

A list containing the simulation results for each scenario that was run.

See Also

Other scenario management: calculatePKParameterForScenarios(), createScenarios.wrapped(), loadPKParameter(), loadScenarioResultsToFramework(), runOrLoadScenarios()

Examples

## Not run: 
runAndSaveScenarios(
  projectConfiguration = myProjectConfig,
  scenarioList = myScenarioList,
  simulationRunOptions = myRunOptions
)

## End(Not run)

Description

This function checks if the simulation results for scenarios already exist. If they do, it loads them; otherwise, it runs the scenarios and saves the results.

Usage

runOrLoadScenarios(
  projectConfiguration,
  scenarioList,
  simulationRunOptions = NULL,
  ...
)

Arguments

Value

A list containing the simulation results for each scenario that was loaded or run.

See Also

Other scenario management: calculatePKParameterForScenarios(), createScenarios.wrapped(), loadPKParameter(), loadScenarioResultsToFramework(), runAndSaveScenarios()

Description

This function generates .Rmd files for creating plots based on user-defined configurations. It reads the specified configuration table, evaluates the selected plot function, and manages the output of plots and tables.

Usage

runPlot(
  projectConfiguration,
  nameOfplotFunction,
  configTableSheet = NULL,
  rmdName = configTableSheet,
  plotNames = NULL,
  suppressExport = FALSE,
  digitsOfSignificanceCSVDisplay = 3,
  inputs = list()
)

Arguments

Details

The 'runPlot' function is designed to facilitate the generation of plots based on configurations defined in a specified configuration table. The function will: - Load the configuration table for plots based on the specified sheet name. - Validate the configuration and ensure that all required parameters are provided. - Execute the designated plot function. - Save the generated plots in the specified subfolder named rmdName, if export is not suppressed.

If the 'plotNames' parameter is set, the function will suppress the Rmd export and return a list of plots generated instead. This is useful when you want to generate specific plots without creating an accompanying .Rmd file for fast check in the daily work.

Users can utilize the 'openFigureTemplate' function to open a template for creating custom plot functions. This function can be called directly: 'openFigureTemplate().' Additionally, the template is available as an RStudio Add-in for easy access.

Value

An invisible list of plots generated by the function. Each element in the list corresponds to a plot created during the execution. If 'plotNames' is provided, the Rmd export is suppressed, and only the plot list is returned.

Examples

## Not run: 
# Run the plot generation function with Rmd export
runPlot(
  projectConfiguration = projectConfiguration,
  nameOfplotFunction = "plotTimeProfiles",
  configTableSheet = "TimeProfiles",
  inputs = list(dataObserved = dataObserved, scenarioResults = scenarioResults)
)

# Run the plot generation function without Rmd export, returning a plot list
plotList <- runPlot(
  projectConfiguration = projectConfiguration,
  nameOfplotFunction = "plotTimeProfiles",
  configTableSheet = "TimeProfiles",
  plotNames = c("Plot1", "Plot2"),
  inputs = list(dataObserved = dataObserved, scenarioResults = scenarioResults)
)

# open the template for custom plot functions
openFigureTemplate()

## End(Not run)

Description

This function executes sensitivity analysis for the specified scenarios in the project configuration. It checks for the presence of the sensitivity file and runs the analysis, saving the results to CSV files.

Usage

runSensitivityAnalysisForScenarios(
  projectConfiguration,
  scenarioList,
  scenarioNames = NULL,
  sensitivitysheet,
  sensitivityAnalysisRunOptions =
    ospsuite::SensitivityAnalysisRunOptions$new(showProgress = TRUE),
  overwrite = TRUE
)

Arguments

Description

This function can be called at the end of your script to save the session information, including the loaded packages and R version, into a log file. The path for the log file has to be initialized by the 'initLogfunction'

Usage

saveSessionInfo()

See Also

Other log file management: addMessageToLog(), captureLog(), initLogfunction(), setShowLogMessages(), writeTableToLog(), writeToLog()

Examples

## Not run: 
# Save session info to log file
saveSessionInfo()

## End(Not run)

Description

This function prepares plot and table objects by setting their export attributes, such as caption and footnote lines, which will be used when exporting through the 'PlotManager' class.

Usage

setExportAttributes(
  object,
  caption = NULL,
  footNoteLines = NULL,
  exportArguments = NULL
)

Arguments

Value

The modified object with the specified export attributes set.

Description

This function takes a data.table and converts the first letter of all column names to lowercase.

Usage

setHeadersToLowerCase(dt)

Arguments

Value

The modified data.table with updated column names.

See Also

Other function to read from and write to xlsx: splitInputs(), xlsxAddDataUsingTemplate(), xlsxAddSheet(), xlsxCloneAndSet(), xlsxReadData(), xlsxWriteData()

Examples

dt <- data.table::data.table(FirstName = c("John", "Jane"), LastName = c("Doe", "Smith"))
dtLower <- setHeadersToLowerCase(dt)
print(names(dtLower)) # Result: c("firstname", "lastname")

Description

Function to switch the display of log messages on the console on and off

Usage

setShowLogMessages(verbose = TRUE)

Arguments

See Also

Other log file management: addMessageToLog(), captureLog(), initLogfunction(), saveSessionInfo(), writeTableToLog(), writeToLog()

Description

This function sets up a configuration sheet for virtual twin populations based on the provided project configuration and observed data.

Usage

setupVirtualTwinPopConfig(projectConfiguration, dataObserved, groups = NULL)

Arguments

Value

Returns NULL invisibly if no groups are available for virtual twin population creation. Modifies the workbook specified in projectConfiguration.

See Also

Other population export: exportRandomPopulations(), exportVirtualTwinPopulations()

Description

Sets options for a reporting frame work workflow.

Usage

setWorkflowOptions(isValidRun = NULL)

Arguments

Details

This function configures options for a reporting framework workflow, which is typically applied in two distinct phases:

1. Exploratory Analysis: This phase involves preparation of outputs that are not yet qualified. In this case: - Watermarks are not applied. - Helper functions for building configuration tables are allowed.

2. Production of Final Output: This phase involves generating the "final" output (a valid run). In this case: - Figures should not have watermarks. - Functions that manipulate inputs are not allowed. - The workflow will stop if an error occurs during execution.

Relevant Options:

- 'ospsuite.plots.watermark_enabled': Set to TRUE when 'isValidRun' is FALSE to display watermarks on figures, and FALSE when 'isValidRun' is TRUE.

- 'OSPSuite.RF.skipFailingPlots': Set to TRUE when 'isValidRun' is FALSE to skip plots that fail to generate, and FALSE when 'isValidRun' is TRUE.

- 'OSPSuite.RF.stopHelperFunction': Set to TRUE when 'isValidRun' is TRUE to stop the execution of helper functions during valid runs.

See Also

Other project initialization: ProjectConfigurationRF, createProjectConfiguration(), getQCpassedEnvironmentVariable(), initProject()

Description

This function takes an original vector as input and splits its elements by comma.

Usage

splitInputs(originalVector)

Arguments

Value

A vector containing the split elements.

See Also

Other function to read from and write to xlsx: setHeadersToLowerCase(), xlsxAddDataUsingTemplate(), xlsxAddSheet(), xlsxCloneAndSet(), xlsxReadData(), xlsxWriteData()

Examples

## Not run: 
originalVector <- c("group1, group2", "group3")
splitInputs(originalVector)
# Result: c('group1', 'group2', 'group3')

## End(Not run)

Description

Manages the creation and writing test project, which can be used for this package or packages which use the reporting framework as base to build a test project

Super class

ospsuite.utils::Printable -> TestProjectBuilder

Active bindings

Methods

Public methods

• TestProjectBuilder$new()

• TestProjectBuilder$iniTestProject()

• TestProjectBuilder$setupRandomPopulations()

• TestProjectBuilder$setupSimulations()

• TestProjectBuilder$setUpSensitivity()

• TestProjectBuilder$addRandomTPData()

• TestProjectBuilder$addRandomPKData()

• TestProjectBuilder$mockManualEditingsCleanup()

• TestProjectBuilder$mockManualEditingsPopulation()

• TestProjectBuilder$mockManualEditingsScenario()

• TestProjectBuilder$mockManualEditingsPKParameter()

• TestProjectBuilder$mockManualEditingsIndividuals()

• TestProjectBuilder$mockManualEditingsSensitivity()

• TestProjectBuilder$clone()

Method new()

Initialize a new instance of the class.

Usage

TestProjectBuilder$new()

Method iniTestProject()

A short description...

This function initializes the project structure and configuration for the test project.

Usage

TestProjectBuilder$iniTestProject(rootDirectory, verbose = FALSE)

Arguments

Returns

A ProjectConfiguration object containing the details of the initialized project.

Method setupRandomPopulations()

This function sets up populations for the test project by copying necessary files and exporting defined populations.

Usage

TestProjectBuilder$setupRandomPopulations(
  projectConfiguration,
  populationNames,
  writeTestData = FALSE,
  templateDir = NULL
)

Arguments

Returns

NULL

Method setupSimulations()

This function sets up simulations for the test project by synchronizing necessary directories and running initialized scenarios to calculate pharmacokinetic (PK) parameters.

Usage

TestProjectBuilder$setupSimulations(
  projectConfiguration,
  scenarioList,
  writeTestData,
  templateDir = NULL
)

Arguments

Returns

A list containing the results from running the scenarios. Set Up Sensitivity Analysis

This function sets up sensitivity analysis for the test project based on the defined scenarios.

Method setUpSensitivity()

Usage

TestProjectBuilder$setUpSensitivity(
  projectConfiguration,
  scenarioList,
  scenarioName,
  writeTestData,
  parameterList,
  sensitivitySheet = "smallSelection",
  templateDir = NULL
)

Arguments

Returns

A ProjectConfiguration object containing the updated project configuration.

Method addRandomTPData()

This function adds random time profile data to the test project for selected individuals.

Usage

TestProjectBuilder$addRandomTPData(
  projectConfiguration,
  scenarioResults,
  ids,
  timePoints = c(15, 30, 45, 60, 90, c(3, 6, 9, 12, 18, 24) * 60),
  outputPathIds = c("Plasma", "CYP3A4total", "CYP3A4Liver"),
  sdShift = 0.05,
  lloqValue = 0.1
)

Arguments

Returns

Invisible NULL. This function adds random pharmacokinetic (PK) data to the test project, including shifting values and calculating statistics for selected individuals.

Method addRandomPKData()

Usage

TestProjectBuilder$addRandomPKData(
  projectConfiguration,
  pkParameterDT,
  ids,
  outputPathIds = c("Plasma", "CYP3A4total", "CYP3A4Liver"),
  sdShift = 0.05
)

Arguments

Returns

Invisible NULL.

Method mockManualEditingsCleanup()

This function cleans up the project configuration files by clearing existing data from relevant Excel sheets.

Usage

TestProjectBuilder$mockManualEditingsCleanup(projectConfiguration)

Arguments

Returns

NULL

Method mockManualEditingsPopulation()

This function adds biometric ranges for virtual pediatric and one adult populations to the configuration files

Usage

TestProjectBuilder$mockManualEditingsPopulation(
  projectConfiguration,
  randomPops = data.table(populationName = c("adults", "toddler", "children",
    "school-children", "adolescents"), species = "Human", population =
    "European_ICRP_2002", numberOfIndividuals = 100, proportionOfFemales = 50, ageMin =
    c(20, 0.5, 2, 6, 12), ageMax = c(40, 2, 6, 12, 18), weightUnit = "kg", heightUnit =
    "cm", bMIUnit = "kg/m²", `protein Ontogenies` = "CYP3A4:CYP3A4, UGT1A4:UGT1A4")
)

Arguments

Returns

NULL

Method mockManualEditingsScenario()

This function sets up scenarios based on the defined populations and adds PK Parameters and output paths to the project configuration.

Usage

TestProjectBuilder$mockManualEditingsScenario(
  projectConfiguration,
  dtTestScenarios,
  pKParameter = NULL
)

Arguments

Returns

NULL

Method mockManualEditingsPKParameter()

This function adds PK parameters to the project configuration based on templates.

Usage

TestProjectBuilder$mockManualEditingsPKParameter(projectConfiguration)

Arguments

Returns

NULL

Method mockManualEditingsIndividuals()

This function adjust the bio-metrics with in individuals and exports virtual twin populations.

Usage

TestProjectBuilder$mockManualEditingsIndividuals(projectConfiguration)

Arguments

Returns

'data.table' with content of sheet 'VirtualTwinPopulation'

Method mockManualEditingsSensitivity()

This function modifies specific parameters in a given Excel sheet based on the provided parameter list and saves the changes back to the workbook.

Usage

TestProjectBuilder$mockManualEditingsSensitivity(
  projectConfiguration,
  sheetName,
  parameterList
)

Arguments

Returns

NULL This function is called for its side effects (i.e., modifying the Excel file).

Method clone()

The objects of this class are cloneable with this method.

Usage

TestProjectBuilder$clone(deep = FALSE)

Arguments

Description

enumeration keys for Time range shortcut

Usage

TIMERANGE

Format

An object of class list of length 3.

See Also

Other enumerations: BIOMETRICUNITS, DATACLASS, EXPORTDIR

Description

This function updates the Data Groups sheet in an Excel workbook based on a provided data table. It reads existing data group IDs, merges them with new data, and ensures that the identifiers remain unique.

Usage

updateDataGroupId(projectConfiguration, dataDT)

Arguments

Details

The function loads the existing Data Groups from the specified Excel workbook, selects relevant columns from the input data, and appends new entries while maintaining the uniqueness of the identifiers. The updated data is then written back to the workbook.

Value

NULL This function updates the Excel workbook in place and does not return a value. It is called for its side effects.

See Also

Other observed data processing: addBiometricsToConfig(), aggregateObservedDataGroups(), convertDataCombinedToDataTable(), convertDataTableToDataCombined(), readObservedDataByDictionary(), updateOutputPathId(), validateObservedData()

Description

This function updates the Outputs sheet in an Excel workbook based on a provided data table, ensuring that output path IDs remain unique.

Usage

updateOutputPathId(projectConfiguration, dataDT)

Arguments

Details

The function loads the existing Output Paths from the specified Excel workbook, extracts unique output path IDs from the input data, and appends them while maintaining uniqueness. The updated data is then written back to the workbook.

Value

NULL This function updates the Excel workbook in place and does not return a value. It is called for its side effects.

See Also

Other observed data processing: addBiometricsToConfig(), aggregateObservedDataGroups(), convertDataCombinedToDataTable(), convertDataTableToDataCombined(), readObservedDataByDictionary(), updateDataGroupId(), validateObservedData()

Description

check if at least one of the following columns is selected

Usage

validateAtleastOneEntry(configTablePlots, columnVector)

Arguments

Description

validate types of plot configuration tables

Usage

validateConfigTablePlots(
  configTablePlots,
  charactersWithoutMissing = NULL,
  charactersWithMissing = NULL,
  numericColumns = NULL,
  logicalColumns = NULL,
  numericRangeColumns = NULL,
  subsetList = list()
)

Arguments

Description

Validates the configuration table for distribution vs demographics plots.

Usage

validateDistributionVsDemographicsConfig(configTable, scenarioList, ...)

Arguments

Value

NULL (invisible).

See Also

Other plot configuration validation function: validateHistogramsConfig(), validatePKBoxwhiskerConfig(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validatePKForestPointEstimateOfRatiosConfig(), validateSensitivityConfig(), validateTimeProfilesConfig()

Other functions to generate plots displaying distribution vs demographics: addDefaultConfigForHistograms(), plotDistributionVsDemographics()

Description

This function checks whether specified columns in a data table have consistent values within groups defined by one or more grouping columns. If any column contains more than one unique value within a group, an error is raised.

Usage

validateGroupConsistency(dt, valueColumns, groupingColumns = "plotName")

Arguments

Value

Returns NULL (invisible) if all checks pass. Raises an error if any value column contains inconsistent values within a group.

Description

checks if config table header and plot rows are strict separated

Usage

validateHeaders(configTable)

Arguments

Value

configuration table without header lines

Description

Validates the configuration table for histogram plots.

Usage

validateHistogramsConfig(configTable, ...)

Arguments

Value

NULL (invisible).

See Also

Other plot configuration validation function: validateDistributionVsDemographicsConfig(), validatePKBoxwhiskerConfig(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validatePKForestPointEstimateOfRatiosConfig(), validateSensitivityConfig(), validateTimeProfilesConfig()

Other functions to generate histograms: addDefaultConfigForDistributionsVsDemographics(), plotHistograms()

Description

Validates numeric range columns in the provided data frame.

Usage

validateNumericVectorColumns(columns, data, ...)

Arguments

Description

Checks the integrity and validity of observed data in a 'data.table'. This function verifies the presence of required attributes, checks for duplicates, and ensures that there are no missing or empty values in the relevant columns. Additionally, it checks for ambiguities in the Y unit and validates error type columns.

Usage

validateObservedData(dataDT, dataClassType)

Arguments

Details

The function performs several checks, including: - Ensuring all data columns have the appropriate attributes. - Verifying that the data is unique based on specified identifier columns. - Checking for NA or empty values in all relevant columns. - Ensuring that the Y unit is consistent across output paths. - Validating the presence of necessary columns based on the Y error type.

Value

NULL This function performs checks and stops execution if any validation fails. It does not return a value.

See Also

Other observed data processing: addBiometricsToConfig(), aggregateObservedDataGroups(), convertDataCombinedToDataTable(), convertDataTableToDataCombined(), readObservedDataByDictionary(), updateDataGroupId(), updateOutputPathId()

Description

Validates the configuration table for PK box-and-whisker plots.

Usage

validatePKBoxwhiskerConfig(configTable, pkParameterDT, ...)

Arguments

Value

NULL (invisible).

See Also

Other plot configuration validation function: validateDistributionVsDemographicsConfig(), validateHistogramsConfig(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validatePKForestPointEstimateOfRatiosConfig(), validateSensitivityConfig(), validateTimeProfilesConfig()

Other functions to generate box whisker plots: addDefaultConfigForPKBoxwhsikerPlots(), plotPKBoxwhisker()

Description

Validates the configuration for PK forest plots with absolute values and variance.

Usage

validatePKForestAggregatedAbsoluteValuesConfig(configTable, pkParameterDT, ...)

Arguments

Value

NULL (invisible).

See Also

Other plot configuration validation function: validateDistributionVsDemographicsConfig(), validateHistogramsConfig(), validatePKBoxwhiskerConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validatePKForestPointEstimateOfRatiosConfig(), validateSensitivityConfig(), validateTimeProfilesConfig()

Other functions to generate forest plots: addDefaultConfigForPKForestPlots(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validatePKForestPointEstimateOfRatiosConfig()

Description

Validates the configuration for PK forest plots with ratios and variance.

Usage

validatePKForestAggregatedRatiosConfig(configTable, pkParameterDT, ...)

Arguments

Value

NULL (invisible).

See Also

Other plot configuration validation function: validateDistributionVsDemographicsConfig(), validateHistogramsConfig(), validatePKBoxwhiskerConfig(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validatePKForestPointEstimateOfRatiosConfig(), validateSensitivityConfig(), validateTimeProfilesConfig()

Other functions to generate forest plots: addDefaultConfigForPKForestPlots(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validatePKForestPointEstimateOfRatiosConfig()

Description

Validates the configuration for PK forest plots with absolute values and confidence intervals.

Usage

validatePKForestPointEstimateOfAbsoluteValuesConfig(
  configTable,
  pkParameterDT,
  ...
)

Arguments

Value

NULL (invisible).

See Also

Other plot configuration validation function: validateDistributionVsDemographicsConfig(), validateHistogramsConfig(), validatePKBoxwhiskerConfig(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfRatiosConfig(), validateSensitivityConfig(), validateTimeProfilesConfig()

Other functions to generate forest plots: addDefaultConfigForPKForestPlots(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfRatiosConfig()

Description

Validates the configuration for PK forest plots with ratios and confidence intervals.

Usage

validatePKForestPointEstimateOfRatiosConfig(configTable, pkParameterDT, ...)

Arguments

Value

NULL (invisible).

See Also

Other plot configuration validation function: validateDistributionVsDemographicsConfig(), validateHistogramsConfig(), validatePKBoxwhiskerConfig(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validateSensitivityConfig(), validateTimeProfilesConfig()

Other functions to generate forest plots: addDefaultConfigForPKForestPlots(), plotPKForestAggregatedAbsoluteValues(), plotPKForestAggregatedRatios(), plotPKForestPointEstimateOfAbsoluteValues(), plotPKForestPointEstimateOfRatios(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig()

Description

This function checks the headers of the configuration table, validates output IDs and data group IDs for plotting, and ensures the configuration adheres to specified criteria. It also checks for file existence in the output folder.

Usage

validateSensitivityConfig(configTable, ...)

Arguments

Value

A validated data frame containing the configuration table for sensitivity plots.

See Also

Other plot configuration validation function: validateDistributionVsDemographicsConfig(), validateHistogramsConfig(), validatePKBoxwhiskerConfig(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validatePKForestPointEstimateOfRatiosConfig(), validateTimeProfilesConfig()

Description

Validation of config table for time profiles plots

Usage

validateTimeProfilesConfig(
  configTable,
  dataObserved = NULL,
  scenarioResults,
  ...
)

Arguments

See Also

Other plot configuration validation function: validateDistributionVsDemographicsConfig(), validateHistogramsConfig(), validatePKBoxwhiskerConfig(), validatePKForestAggregatedAbsoluteValuesConfig(), validatePKForestAggregatedRatiosConfig(), validatePKForestPointEstimateOfAbsoluteValuesConfig(), validatePKForestPointEstimateOfRatiosConfig(), validateSensitivityConfig()

Description

write a table to the logfile

Usage

writeTableToLog(dt, filename = "run.log")

Arguments

See Also

Other log file management: addMessageToLog(), captureLog(), initLogfunction(), saveSessionInfo(), setShowLogMessages(), writeToLog()

Description

The 'writeToLog' function is used to append log messages to a log file. The path for the logfile has to be initialized by the function initLogfile

Usage

writeToLog(type, msg, filename = NULL)

Arguments

See Also

Other log file management: addMessageToLog(), captureLog(), initLogfunction(), saveSessionInfo(), setShowLogMessages(), writeTableToLog()

Examples

## Not run: 
# Write a log message
writeToLog(type = "Info", msg = "This is an information message", filename = "run.log")

## End(Not run)

Description

If the template does not exist in the configuration file in the project directory, it is taken from the configuration file of the package installation. In this case, formats are not preserved.

Usage

xlsxAddDataUsingTemplate(
  wb,
  templateSheet,
  sheetName,
  dtNewData,
  templateXlsx = "Plots.xlsx"
)

Arguments

Value

The current configuration file with the added sheet.

See Also

Other function to read from and write to xlsx: setHeadersToLowerCase(), splitInputs(), xlsxAddSheet(), xlsxCloneAndSet(), xlsxReadData(), xlsxWriteData()

Examples

## Not run: 
wb <- loadWorkbook("config.xlsx")
newData <- data.table(Name = c("Gina", "Hank"), Age = c(29, 33))
xlsxAddDataUsingTemplate(wb, "TemplateSheet", "NewDataSheet", newData)

## End(Not run)

Description

This function wraps 'openxlsx::addWorksheet' and adds data to the newly created sheet. If the sheet already exists, it issues a warning and clears the existing content.

Usage

xlsxAddSheet(wb, sheetName, dt)

Arguments

Value

An invisible NULL value. The function performs an action (adding a sheet)

See Also

Other function to read from and write to xlsx: setHeadersToLowerCase(), splitInputs(), xlsxAddDataUsingTemplate(), xlsxCloneAndSet(), xlsxReadData(), xlsxWriteData()

Examples

## Not run: 
library(openxlsx)
wb <- loadWorkbook("example.xlsx")
data <- data.table(Name = c("Alice", "Bob"), Age = c(30, 25))
xlsxAddSheet(wb, "NewSheet", data)

## End(Not run)

Description

This function wraps 'openxlsx::cloneWorksheet' but sets new content in the cloned sheet.

Usage

xlsxCloneAndSet(wb, clonedSheet, sheetName, dt)

Arguments

Value

An invisible NULL value. The function performs an action (clone a sheet)

See Also

Other function to read from and write to xlsx: setHeadersToLowerCase(), splitInputs(), xlsxAddDataUsingTemplate(), xlsxAddSheet(), xlsxReadData(), xlsxWriteData()

Examples

## Not run: 
wb <- loadWorkbook("example.xlsx")
data <- data.table(Name = c("Eve", "Frank"), Age = c(22, 35))
xlsxCloneAndSet(wb, "ExistingSheet", "ClonedSheet", data)

## End(Not run)

Description

This function wraps 'openxlsx::read.xlsx' and returns the data as a 'data.table'.

Usage

xlsxReadData(
  wb,
  sheetName = 1,
  skipDescriptionRow = FALSE,
  alwaysCharacter = c("Group", "Id$", "Ids$"),
  emptyAsNA = TRUE,
  convertHeaders = TRUE
)