Description of the functions of the API
getChartsData | Get the charts data. |
plotBivariateDataViewer | Plot the bivariate viewer. |
plotCovariates | Plot the covariates. |
plotObservedData | Plot the observed data. |
plotIndividualFits | Plot the individual fits. |
plotObservationsVsPredictions | Plot the observations versus prediction. |
plotResidualsDistribution | Plot the residual distribution. |
plotResidualsScatterPlot | Plot the scatter plot of the residuals. |
plotParametersDistribution | Plot the parameter distribution. |
plotParametersVsCovariates | Plot the parameters vs covariates. |
plotRandomEffectsCorrelation | Plot the random effects correlation. |
plotStandardizedRandomEffectsDistribution | Plot the standardized random effect distribution. |
plotBlqPredictiveCheck | Plot the predictive BLQ check. |
plotNpc | Plot the NPC. |
plotPredictionDistribution | Plot the prediction distribution. |
plotVpc | Plot the VPC. |
plotImportanceSampling | Plot the Importance Sampling task results. |
plotMCMC | Plot the MCMC task results. |
plotSaem | Plot the SAEM task results. |
getPlotPreferences | Get the Plot prerefences. |
resetPlotPreferences | Reset the plot preferences. |
setPlotPreferences | Set the plot preferences. |
[Monolix – PKanalix] Compute Charts data with custom stratification options and
custom computation settings
Description
[Monolix – PKanalix] Compute Charts data with custom stratification options and
custom computation settings
Usage
getChartsData(
plotName,
computeSettings = NULL,
ids = NULL,
splitGroup = NULL,
colorGroup = NULL,
filter = NULL
)
Arguments
plotName |
(string) Name of the plot function. |
computeSettings |
(list) list with computational settings |
ids |
list of ids to display (by default all ids are displayed). |
splitGroup |
data group criteria. a list, or a list of list with fields:
(by default no split is applied). |
colorGroup |
data group criteria. a list, or a list of list with fields:
(by default no color group is defined). |
filter |
data filtering criteria. a list, or a list of list with fields:
(by default no filtering is applied). |
Value
A dataframe object or a list of dataframe object to pass to “data” argument
of plot functions
Click here to see examples
initializeLixoftConnectors(software = “pkanalix”)
project <- file.path(getDemoPath(), “2.case_studies/project_Theo_extravasc_SD.pkx”)
loadProject(project)
data <- getChartsData(plotName = “plotObservedData”, ids = c(1, 2, 3, 4))
data <- getChartsDataNCA(plotName = “plotNCAParamCorrelation”)
initializeLixoftConnectors(software = “monolix”)
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
xBinsSettings <- list(is.fixedNbBins = TRUE, nbBins = 10)
data <- getChartsData(plotName = “plotVpc”,
computeSettings = list(xBinsSettings = xBinsSettings))
data <- getChartsData(plotName = “plotVpc”, computeSettings = list(level = 75))
splitGroup <- list(name = “WEIGHT”, breaks = c(75))
filter <- list(name = “WEIGHT”, interval = c(75, 100))
data <- getChartsData(plotName = “plotVpc”, splitGroup = splitGroup)
data <- getChartsData(plotName = “plotVpc”, filter = filter)
## End(Not run)
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[Monolix – PKanalix] Generate Bivariate observations plots
Description
[Monolix – PKanalix] Generate Bivariate observations plots
Usage
plotBivariateDataViewer(
obs1 = NULL,
obs2 = NULL,
data = NULL,
settings = list(),
stratify = list(),
preferences = list()
)
Arguments
obs1 |
(string) Name of the observation to display in x axis (in dataset header). By default the first observation is considered. |
obs2 |
(string) Name of the observation to display in y axis (in dataset header). By default the second observation is considered. |
data |
List of charts data as dataframe – Output of getChartsData (getChartsData(“plotBivariateDataViewer”, …)) If data not specified, charts data will be computed inside the function. |
settings |
List with the following settings
|
stratify |
List with the stratification arguments
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotBivariateDataViewer”) to check available displays. |
Value
A ggplot object
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software = “monolix”)
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “warfarinPKPD_project.mlxtran”)
loadProject(project)
plotBivariateDataViewer(obs1 = “y1”, obs2 = “y2”)
plotBivariateDataViewer(settings = list(lines = FALSE))
# stratification
plotBivariateDataViewer(obs1 = “y1”, obs2 = “y2”, stratify = list(ids = 100))
plotBivariateDataViewer(stratify = list(splitGroup = list(name = “age”, breaks = 25),
filter = list(name = “sex”, cat = 1)))
plotBivariateDataViewer(stratify = list(colorGroup = list(name = “wt”, breaks = 75)))
plotBivariateDataViewer(stratify = list(splitGroup = list(list(name = “age”, breaks = 25),
list(name = “sex”))))
# update plot settings or preferences
plotBivariateDataViewer(preferences = list(obs = list(color = “#32CD32”)))
## End(Not run)
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[Monolix – PKanalix] Generate Covariate plots
Description
Generate scatterplots between two continuous covariates or bar plot
between categorical covariates
Usage
plotCovariates(
covariatesRows = NULL,
covariatesColumns = NULL,
data = NULL,
settings = list(),
preferences = list(),
stratify = list()
)
Arguments
covariatesRows |
vector with the name of covariates to display on rows (by default the first 4 covariates are displayed). |
covariatesColumns |
vector with the name of covariates to display on columns (by default the first 4 covariates are displayed). |
data |
List of charts data as dataframe – Output of getChartsData (getChartsData(“plotCovariates”, …)) If data not specified, charts data will be computed inside the function. |
settings |
List with the following settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotCovariates”) to check available displays. |
stratify |
List with the stratification arguments
|
Value
- A ggplot object if one element in covariatesRows and covariatesColumns,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software = “pkanalix”)
project <- file.path(getDemoPath(), “2.case_studies/project_Theo_extravasc_SD.pkx”)
loadProject(project)
# covariate distribution when only one covariate is specified
plotCovariates(covariatesRows = “HT”, settings = list(bins = 10))
# scatter plot when both covariates are continuous
plotCovariates(covariatesRows = “HT”, covariatesColumns = “AGE”, settings = list(spline = TRUE))
plotCovariates(covariatesRows = “HT”, covariatesColumns = c(“AGE”, “FORM”))
# box plot when one covariate is categorical and the othe one is continuous
preferences <- list(boxplot = list(fill = “#2075AE”), boxplotOutlier = list(shape = 3))
plotCovariates(covariatesRows = “FORM”, covariatesColumns = “AGE”, preferences = preferences)
# histogram when covariate on column is categorical
plotCovariates(covariatesRows = “FORM”, covariatesColumns = “SEQ”,
settings = list(histogramColors = c(“#5DC088”, “#DBA92B”)))
plotCovariates(covariatesRows = “AGE”, covariatesColumns = “SEQ”,
settings = list(histogramColors = c(“#5DC088”, “#DBA92B”)))
# stratification
plotCovariates(covariatesRows = “HT”, covariatesColumns = “WT”, stratify = list(
splitGroup = list(name = “AGE”, breaks = 25),
filter = list(name = “Period”, cat = 1)))
preferences <- list(regressionLine = list(color = “#E5551B”))
plotCovariates(covariatesRows = “AGE”, covariatesColumns = “WT”, stratify = list(
colorGroup = list(name = “HT”, breaks = 181),
colors = c(“#2BB9DB”, “#DD6BD2”)), preferences = preferences)
plotCovariates(covariatesRows = “HT”, covariatesColumns = “WT”,
stratify = list(splitGroup = list(list(name = “AGE”, breaks = 25),
list(name = “SEQ”))))
# Mulitple covariates
plotCovariates()
plotCovariates(covariatesRows = c(“AGE”, “SEQ”, “HT”), covariatesColumns = c(“AGE”, “SEQ”, “HT”))
plotCovariates(stratify = list(filter = list(name = “AGE”, interval = c(20, 30))))
plotCovariates(stratify = list(splitGroup = list(name = “AGE”, breaks = c(25))))
plotCovariates(stratify = list(colorGroup = list(name = “AGE”, breaks = c(25))))
## End(Not run)
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[Monolix – PKanalix] Generate Observation plots
Description
[Monolix – PKanalix] Generate Observation plots
Usage
plotObservedData(
obsName = NULL,
data = NULL,
settings = list(),
stratify = list(),
preferences = list()
)
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
data |
List of charts data as dataframe – Output of getChartsData (getChartsData(“plotObservedData”, …)) If data not specified, charts data will be computed inside the function. |
settings |
List with the following settings [CONTINUOUS – DISCRETE] Settings specific to continuous and discrete data
[DISCRETE] Settings specific to discrete data
[EVENT] Settings specific to event data
Other settings
|
stratify |
List with the stratification arguments
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotObservedData”) to check available displays. |
Value
A ggplot object
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software = “pkanalix”)
project <- file.path(getDemoPath(), “2.case_studies/project_Theo_extravasc_SD.pkx”)
loadProject(project)
plotObservedData()
plotObservedData(settings = list(binLimits = TRUE))
plotObservedData(settings = list(dosingTimes = TRUE))
plotObservedData(settings = list(meanMethod = “geometric”, mean = TRUE))
plotObservedData(settings = list(mean = TRUE, error = TRUE, dots = FALSE, lines = TRUE))
# stratification
plotObservedData(stratify = list(splitGroup = list(name = “AGE”, breaks = 25),
filter = list(name = “Period”, cat = 1)))
plotObservedData(stratify = list(colorGroup = list(name = “HT”, breaks = 181)))
plotObservedData(stratify = list(splitGroup = list(list(name = “AGE”, breaks = 25),
list(name = “Period”))))
# update plot theme or preferences
plotObservedData(settings = list(xlab = “Time”, ylab = “Plasma Concentration”))
plotObservedData(preferences = list(obs = list(color = “#32CD32”),
observationStatistics = list(lineType = “dashed”)))
## End(Not run)
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[Monolix] Plot Monolix Individual Fits
Only available for Continuous data.
Description
[Monolix] Plot Monolix Individual Fits
Only available for Continuous data.
Usage
plotIndividualFits(
obsName = NULL,
settings = list(),
preferences = list(),
stratify = list(),
data = NULL
)
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
settings |
List with the following settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotIndividualFits”) to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData(“plotIndividualFits”, …)) If data not specified, charts data will be computed inside the function. |
Value
A ggplot object
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software = “monolix”)
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
plotIndividualFits()
plotIndividualFits(settings=list(popFits=T))
plotIndividualFits(settings=list(obsLines=T, obsDots=F, predInterval=T))
plotIndividualFits(settings=list(dosingTimes=T))
# stratification options
plotIndividualFits(stratify=list(ids=c(1, 2, 3, 4)))
plotIndividualFits(stratify=list(filter=list(name=”WEIGHT”, interval=c(75, 100))))
plotIndividualFits(stratify=list(filter=list(name=”SEX”, cat =”F”)))
plotIndividualFits(stratify=list(colorGroup=list(name=”SEX”), colors=c(“#5DC088”, “#DBA92B”)))
plotIndividualFits(
settings=list(legend=T),
stratify = list(colorGroup=list(list(name = “SEX”),
list(name = “WEIGHT”, breaks = 70)))
)
# settings and preferences options
plotIndividualFits(settings=list(ylog=T, ylim=c(0.8, 11)))
preferences <- list(popFits=list(lineType=”solid”, legend=”Population fits”))
plotIndividualFits(settings=list(popFits=T), preferences=preferences)
data <- getChartsData(plotName=”plotIndividualFits”,
computeSettings=list(indivEstimate=”mean”),
ids=c(1, 2, 3, 4))
plotIndividualFits(data=data)
## End(Not run)
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[Monolix] Plot Observation VS Prediction
Description
[Monolix] Plot Observation VS Prediction
Usage
plotObservationsVsPredictions(
obsName = NULL,
predictions = c("indiv"),
settings = list(),
preferences = list(),
stratify = list(),
data = NULL
)
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
predictions |
(string) LIst of predictions to display: population prediction (“pop”), individual prediction (“indiv”) (default c(“indiv”)). |
settings |
List with the following settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotObservationsVsPredictions”) to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of cahrts data as dataframe – Output of getChartsData (getChartsData(“plotObservationsVsPredictions”, …)) If data not specified, charts data will be computed inside the function. |
Value
- A ggplot object if one prediction type,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software = “monolix”)
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
plotObservationsVsPredictions()
plotObservationsVsPredictions(predictions = “pop”)
plotObservationsVsPredictions(prediction = “indiv”, settings = list(indivEstimate = “simulated”))
plotObservationsVsPredictions(settings = list(indivEstimate = “mean”, spline = TRUE))
plotObservationsVsPredictions(settings = list(indivEstimate = “mode”, predInterval = TRUE))
# stratification
plotObservationsVsPredictions(stratify = list(filter = list(name = “SEX”, cat = “F”)))
plotObservationsVsPredictions(settings = list(ylog = TRUE, xlog = TRUE))
plotObservationsVsPredictions(stratify = list(splitGroup = list(name = “WEIGHT”, breaks = c(75))))
plotObservationsVsPredictions(stratify = list(colorGroup = list(name = “WEIGHT”, breaks = c(75))))
plotObservationsVsPredictions(
settings=list(legend=T),
stratify = list(colorGroup=list(list(name = “SEX”),
list(name = “WEIGHT”, breaks = 70)))
)
data <- getChartsData(plotName = “plotObservationsVsPredictions”,
computeSettings = list(indivEstimate = “simulated”),
colorGroup = list(name = “WEIGHT”, breaks = c(75)))
plotObservationsVsPredictions(data = data)
# display multiple predictions
plotObservationsVsPredictions(predictions = c(“pop”, “indiv”))
## End(Not run)
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[Monolix] Generate Distribution of the residuals
Description
[Monolix] Generate Distribution of the residuals
Usage
plotResidualsDistribution(
obsName = NULL,
residuals = c("indiv", "npde"),
plots = c("pdf", "cdf"),
settings = list(),
preferences = list(),
stratify = list(),
data = NULL
)
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
residuals |
(string) List of residuals to display: population residuals (“pop”), individual residuals (“indiv”), normalized prediction distribution error (“npde”) (default c(“indiv”, “npde)). |
plots |
Type of plots: probability density distribution (“pdf”), cumulative density distribution (“cdf”) (default c(“pdf”, “cdf”)). |
settings |
List with the following settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotResidualsDistribution”) to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData(“plotResidualsDistribution”, …)) If data not specified, charts data will be computed inside the function. |
Value
- A ggplot object if one prediction type,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software=”monolix”)
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
plotResidualsDistribution()
plotResidualsDistribution(residuals=”indiv”, settings=list(indivEstimate=”simulated”))
plotResidualsDistribution(residuals=”indiv”, settings=list(indivEstimate=”mode”))
plotResidualsDistribution(residuals=”pop”, plots=”pdf”)
plotResidualsDistribution(residuals=”npde”, plots=”cdf”)
plotResidualsDistribution(stratify=list(filter=list(name=”SEX”, cat=”F”)))
plotResidualsDistribution(stratify=list(splitGroup=list(name=”WEIGHT”, breaks=c(75))))
plotResidualsDistribution(
residuals=”indiv”, settings=list(legend=T),
stratify = list(splitGroup=list(list(name = “SEX”),
list(name = “WEIGHT”, breaks = 70)))
)
data <- getChartsData(plotName=”plotResidualsDistribution”,
computeSettings=list(indivEstimate=”simulated”))
plotResidualsDistribution(data=data)
plotResidualsDistribution()
plotResidualsDistribution(residuals=c(“indiv”, “npde”), settings=list(indivEstimate=”simulated”))
plotResidualsDistribution(residuals=c(“pop”, “indiv”), settings=list(indivEstimate=”mode”))
plotResidualsDistribution(plots=c(“pdf”, “cdf”))
plotResidualsDistribution(plots=c(“cdf”))
plotResidualsDistribution(residuals=”npde”)
## End(Not run)
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[Monolix] Generate Scatter plots of the residuals
Description
Note that ‘prediction interval’ setting is not available in 2021 version for this connector.
Usage
plotResidualsScatterPlot(
obsName = NULL,
residuals = c("indiv"),
xaxis = c("time", "prediction"),
settings = list(),
preferences = list(),
stratify = list(),
data = NULL
)
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
residuals |
(string) List of residuals to display: population residuals (“pop”), individual residuals (“indiv”), normalized prediction distribution error (“npde”) (default c(“indiv”)). |
xaxis |
(string) List of x-axis to display: time (“time”), prediction (“prediction”) (default c(“time”, “prediction”) for continuous data, c(“time”) for discrete data). |
settings |
List with the following settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotResidualsScatterPlot”) to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of cahrts data as dataframe – Output of getChartsData (getChartsData(“plotResidualsScatterPlot”, …)) If data not specified, charts data will be computed inside the function. |
Value
- A ggplot object if one prediction type,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software=”monolix”)
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
plotResidualsScatterPlot()
plotResidualsScatterPlot(residuals=”indiv”, settings=list(indivEstimate=”simulated”))
plotResidualsScatterPlot(residuals=”indiv”, settings=list(indivEstimate=”mode”))
plotResidualsScatterPlot(xaxis=”prediction”, residuals=”pop”)
plotResidualsScatterPlot(xaxis=”time”, residuals=”pop”)
plotResidualsScatterPlot(residuals=”npde”)
plotResidualsScatterPlot(settings=list(spline=T))
plotResidualsScatterPlot(settings=list(empPercentiles=T, level=90,
binsSettings=list(is.fixedNbBins=T, nbBins=5),
binLimits=T))
# Stratification
plotResidualsScatterPlot(stratify=list(filter=list(name=”SEX”, cat=”F”)))
plotResidualsScatterPlot(stratify=list(splitGroup=list(name=”WEIGHT”, breaks=c(75))))
plotResidualsScatterPlot(stratify=list(colorGroup=list(name=”WEIGHT”, breaks=c(75))))
data <- getChartsData(plotName=”plotResidualsScatterPlot”,
computeSettings=list(indivEstimate=”simulated”))
plotResidualsScatterPlot(data=data)
plotResidualsScatterPlot(residuals=c(“indiv”, “pop”),
settings=list(indivEstimate=”simulated”))
plotResidualsScatterPlot(residuals=”indiv”, xaxis=c(“prediction”),
settings=list(indivEstimate=”mode”))
plotResidualsScatterPlot(xaxis=c(“prediction”), residuals=c(“indiv”, “pop”))
plotResidualsScatterPlot(residuals=”npde”)
## End(Not run)
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[Monolix] Distribution of the individual parameters computed by Monolix
Description
[Monolix] Distribution of the individual parameters computed by Monolix
Usage
plotParametersDistribution(
parameters = NULL,
plot = "pdf",
settings = list(),
preferences = NULL,
stratify = list(),
data = NULL
)
Arguments
parameters |
vector of parameters to display. (by default the first 4 computed parameters are displayed). |
plot |
(string) Type of plot: probability density distribution (“pdf”), cumulative density distribution (“cdf”) (default “pdf) |
settings |
a list of optional plot settings:
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotParametersDistribution”) to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData(“plotParametersDistribution”, …)) If data not specified, charts data will be computed inside the function. |
Value
- A ggplot object if one parameter,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software=”monolix”)
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
plotParametersDistribution(parameters=”ka”)
plotParametersDistribution(parameters=”Cl”, plot=”pdf”)
plotParametersDistribution(parameters=”ka”, plot=”cdf”)
plotParametersDistribution(parameters=”ka”, plot=”cdf”,
settings=list(indivEstimate=”simulated”))
plotParametersDistribution(parameters=”Cl”, plot=”pdf”,
settings=list(theoretical=F))
# stratification
plotParametersDistribution(stratify=list(filter=list(name=”WEIGHT”, interval=c(0, 75))))
plotParametersDistribution(parameters=”Cl”, stratify=list(splitGroup=list(name=”SEX”)))
colorGroup <- list(name=”WEIGHT”, breaks=c(75))
plotParametersDistribution(parameters= “Cl”, plot=”pdf”,
stratify=list(colorGroup=colorGroup, colors=c(“#46B4AF”, “#B4468A”)))
plotParametersDistribution(parameters=”Cl”, plot=”cdf”,
stratify=list(colorGroup=colorGroup, colors=c(“#46B4AF”, “#B4468A”)))
# update preferences
preferences = list(theoretical=list(color=”#B4468A”, lineType=”solid”, lineWidth=0.8))
plotParametersDistribution(parameters=”ka”, plot=”cdf”, preferences=preferences)
# pre compute dataset
data <- getChartsData(plotName=”plotParametersDistribution”,
computeSettings=list(indivEstimate=”simulated”))
plotParametersDistribution(data=data)
# multiple plots
plotParametersDistribution(parameters=c(“ka”, “Cl”))
plotParametersDistribution(plot=”pdf”)
plotParametersDistribution(plot=”cdf”)
plotParametersDistribution(plot=”cdf”, settings=list(indivEstimate=”simulated”))
plotParametersDistribution(plot = “pdf”, settings=list(theoretical=F))
## End(Not run)
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[Monolix] Individual monolix parameter vs covariate plot
Description
[Monolix] Individual monolix parameter vs covariate plot
Usage
plotParametersVsCovariates(
parameters = NULL,
covariates = NULL,
settings = list(),
preferences = list(),
stratify = list(),
data = NULL
)
Arguments
parameters |
vector of parameters to display. (by default the first 4 computed parameters are displayed). |
covariates |
vector of covariates to display. (by default the first 4 computed covariates are displayed). |
settings |
List with the following settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotParametersVsCovariates”) to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData(“plotParametersVsCovariates”, …)) If data not specified, charts data will be computed inside the function. |
Value
- A ggplot object if one covariate and one parameter in argument,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software=”monolix”)
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
# Individual parameters
plotParametersVsCovariates(covariates=”SEX”, parameters=”Cl”)
plotParametersVsCovariates(covariates=”WEIGHT”, parameters=”V”, settings=list(spline=T))
plotParametersVsCovariates(covariates=”WEIGHT”, parameters=”V”,
settings=list(indivEstimate=”simulated”))
# Random effects
plotParametersVsCovariates(covariates=”SEX”, parameters=”V”,
settings=list(parameterType=”randomEffect”))
plotParametersVsCovariates(covariates=”WEIGHT”, parameters=”V”,
settings=list(indivEstimate=”simulated”, parameterType=”randomEffect”))
# Stratification
plotParametersVsCovariates(covariates=”SEX”, parameters=”ka”,
stratify=list(filter=list(name=”WEIGHT”, interval=c(0, 75))))
plotParametersVsCovariates(covariates=”WEIGHT”, parameters=”ka”,
stratify=list(splitGroup=list(name=”SEX”)))
plotParametersVsCovariates(covariates=”SEX”, parameters=”Cl”,
stratify=list(colorGroup=list(name=”WEIGHT”, breaks=75)))
plotParametersVsCovariates(covariates=”WEIGHT”, parameters=”V”,
stratify=list(colorGroup=list(name=”SEX”)))
plotParametersVsCovariates(covariates=”WEIGHT”, parameters=”V”,
stratify = list(colorGroup = list(list(name = “SEX”),
list(name=”WEIGHT”, breaks=70))))
# pre process dataset
data <- getChartsData(plotName=”plotParametersVsCovariates”,
computeSettings=list(indivEstimate=”simulated”))
plotParametersVsCovariates(data=data)
# multiple plots
plotParametersVsCovariates()
plotParametersVsCovariates(covariates=”WEIGHT”)
plotParametersVsCovariates(settings=list(indivEstimate=”simulated”))
plotParametersVsCovariates(settings=list(parameterType=”randomEffect”))
plotParametersVsCovariates(settings=list(parameterType=”randomEffect”, indivEstimate=”simulated”))
plotParametersVsCovariates(stratify=list(colorGroup=list(name=”WEIGHT”, breaks=75)))
plotParametersVsCovariates(stratify=list(colorGroup=list(name=”SEX”)))
## End(Not run)
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[Monolix] Correlations between random effect
Description
[Monolix] Correlations between random effect
Usage
plotRandomEffectsCorrelation(
parametersRows = NULL,
parametersColumns = NULL,
settings = list(),
preferences = list(),
stratify = list(),
data = NULL
)
Arguments
parametersRows |
vector with the name of parameters to display on rows (by default the first 4 computed parameters are displayed). |
parametersColumns |
vector with the name of parameters to display on columns (by default parametersColumns = parametersRows). |
settings |
List with the following settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotRandomEffectsCorrelation”) to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData(“plotRandomEffectsCorrelation”, …)) If data not specified, charts data will be computed inside the function. |
Value
- A ggplot object if one element in parametersRows and parametersColumns,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software = “monolix”)
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
plotRandomEffectsCorrelation()
plotRandomEffectsCorrelation(parametersRows = “ka”, parametersColumns = “V”,
settings = list(indivEstimate = “simulated”))
plotRandomEffectsCorrelation(parametersRows = “ka”, parametersColumns = “V”,
settings = list(spline = TRUE))
plotRandomEffectsCorrelation(parametersRows = c(“ka”, “V”))
# stratification
plotRandomEffectsCorrelation(parametersRows = “ka”, parametersColumns = “V”,
stratify = list(filter = list(name = “SEX”, cat = “M”)))
plotRandomEffectsCorrelation(parametersRows = “ka”, parametersColumns = “V”,
stratify = list(
colorGroup = list(name = “WEIGHT”, breaks = 75),
colors = c(“#46B4AF”, “#B4468A”)))
plotRandomEffectsCorrelation(parametersRows = “ka”, parametersColumns = “V”,
stratify = list(splitGroup = list(name = “SEX”))
plotRandomEffectsCorrelation(parametersRows = “ka”, parametersColumns = “V”,
stratify = list(splitGroup = list(list(name = “SEX”),
list(name=”WEIGHT”, breaks=70))))
# pre compute dataset
data <- getChartsData(plotName = “plotRandomEffectsCorrelation”,
computeSettings = list(indivEstimate = “simulated”))
plotRandomEffectsCorrelation(data = data)
plotRandomEffectsCorrelation(settings = list(indivEstimate = “mean”))
## End(Not run)
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[Monolix] Distribution of the standardized random effects
Description
[Monolix] Distribution of the standardized random effects
Usage
plotStandardizedRandomEffectsDistribution(
parameters = NULL,
plot = "boxplot",
settings = list(),
preferences = list(),
stratify = list(),
data = NULL
)
Arguments
parameters |
vector of parameters to display. (by default the first 4 computed parameters are displayed). |
plot |
Type of plot: probability density distribution (“pdf”), cumulative density distribution (“cdf”), boxplot (“boxplot”) (default “boxplot”). |
settings |
a list of optional plot settings:
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotStandardizedRandomEffectsDistribution”) to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData(“plotStandardizedRandomEffectsDistribution”, …)) If data not specified, charts data will be computed inside the function. |
Value
- A ggplot object if one parameter,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software=”monolix”)
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
# Random effect distribution as boxplot
plotStandardizedRandomEffectsDistribution(parameters=”ka”, plot=”boxplot”)
plotStandardizedRandomEffectsDistribution(parameters=”ka”, plot=”boxplot”,
settings=list(indivEstimate=”mode”))
plotStandardizedRandomEffectsDistribution(parameters=”ka”, plot=”boxplot”,
settings=list(quartile=F))
# Random effect distribution as pdf
plotStandardizedRandomEffectsDistribution(parameters=”ka”, plot=”pdf”)
plotStandardizedRandomEffectsDistribution(parameters=”ka”, plot=”pdf”,
settings=list(empirical=F))
plotStandardizedRandomEffectsDistribution(parameters=”ka”, plot=”pdf”,
settings=list(theoretical=F))
# Random effect distribution as cdf
plotStandardizedRandomEffectsDistribution(parameters=”ka”, plot=”cdf”)
plotStandardizedRandomEffectsDistribution(parameters=”ka”, plot=”cdf”,
settings=list(indivEstimate=”simulated”))
plotStandardizedRandomEffectsDistribution(parameters=”ka”, plot=”cdf”,
settings=list(theoretical=F))
# stratification
plotStandardizedRandomEffectsDistribution(
stratify=list(filter=list(name=”WEIGHT”, interval=c(0, 75)))
)
plotStandardizedRandomEffectsDistribution(parameters=”Cl”,
stratify=list(splitGroup=list(name=”SEX”)))
colorGroup <- list(name=”WEIGHT”, breaks=c(75))
plotStandardizedRandomEffectsDistribution(
parameters=”Cl”, plot=”pdf”,
stratify=list(colorGroup=colorGroup, colors=c(“#46B4AF”, “#B4468A”))
)
plotStandardizedRandomEffectsDistribution(
parameters=”Cl”, plot=”cdf”,
stratify=list(colorGroup=colorGroup, colors=c(“#46B4AF”, “#B4468A”))
)
plotStandardizedRandomEffectsDistribution(
parameters=”Cl”, settings=list(plot=”boxplot”),
stratify=list(colorGroup=colorGroup, colors=c(“#46B4AF”, “#B4468A”))
)
data <- getChartsData(plotName=”plotStandardizedRandomEffectsDistribution”,
computeSettings=list(indivEstimate=”simulated”))
plotStandardizedRandomEffectsDistribution(data=data)
plotStandardizedRandomEffectsDistribution(parameters=c(“ka”, “Cl”))
plotStandardizedRandomEffectsDistribution(plot=”boxplot”)
plotStandardizedRandomEffectsDistribution(plot=”pdf”)
plotStandardizedRandomEffectsDistribution(plot=”cdf”)
plotStandardizedRandomEffectsDistribution(plot=”pdf”, settings=list(theoretical=F))
## End(Not run)
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[Monolix] Plot BLQ predictive checks
Description
[Monolix] Plot BLQ predictive checks
Usage
plotBlqPredictiveCheck(
obsName = NULL,
settings = list(),
preferences = list(),
data = NULL
)
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
settings |
a list of optional plot settings:
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotBlqPredictiveCheck”) to check available displays. |
data |
List of charts data as dataframe – Output of getChartsData (getChartsData(“plotBlqPredictiveCheck”, …)) If data not specified, charts data will be computed inside the function. |
Value
a ggplot2 object
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software = “monolix”)
# continuous data
project <- file.path(getDemoPath(), “2.models_for_continuous_outcomes”,
“2.2.censored_data”, “censoring1_project.mlxtran”)
loadProject(project)
plotBlqPredictiveCheck(obsName = “Y”)
## End(Not run)
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[Monolix] Plot Numerical predictive checks
Description
[Monolix] Plot Numerical predictive checks
Usage
plotNpc(
obsName = NULL,
settings = list(),
preferences = list(),
stratify = list(),
data = NULL
)
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
settings |
a list of optional settings:
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotNpc”) to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData(“plotNpc”, …)) If data not specified, charts data will be computed inside the function. |
Value
a ggplot2 object
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software = “monolix”)
# continuous data
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
plotNpc(obsName = “CONC”)
## End(Not run)
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[Monolix] Plot distribution of the predictions
Description
Note that computation settings are not available for this connector in 2021 version:
Number of bands is set to 9 and Level is set to 90
Usage
plotPredictionDistribution(
obsName = NULL,
settings = list(),
preferences = list(),
data = NULL
)
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
settings |
a list of optional settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotPredictionDistribution”) to check available displays. |
data |
List of charts data as dataframe – Output of getChartsData (getChartsData(“plotPredictionDistribution”, …)) If data not specified, charts data will be computed inside the function. |
Details
Note that stratification options are not available for this connector in 2021 version:
Value
a ggplot2 object
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software = “monolix”)
# continuous data
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
plotPredictionDistribution()
## End(Not run)
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[Monolix] Plot Visual predictive checks
Description
[Monolix] Plot Visual predictive checks
Usage
plotVpc(
obsName = NULL,
eventPlot = "survivalFunction",
settings = list(),
preferences = list(),
stratify = list(),
data = NULL
)
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
eventPlot |
(string) Display Survival function (“survivalFunction”) or average number of event (“averageEventNumber) (default “survivalFunction”). For event data only. |
settings |
a list of optional settings:
|
preferences |
(optional) preferences for plot display, run getPlotPreferences(“plotVpc”) to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData(“plotVpc”, …)) If data not specified, charts data will be computed inside the function. |
Value
a ggplot2 object
See Also
getChartsData getPlotPreferences
Click here to see examples
initializeLixoftConnectors(software = “monolix”)
# continuous data
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
data <- getChartsDataVpc()
p <- plotVpc(data = data, obsName = “CONC”,
settings = list(outlierDots = FALSE, grid = FALSE,
ylab = “Concentration”, xlab = “time (in hour)”))
# categorical data
project <- file.path(getDemoPath(), “3.models_for_noncontinuous_outcomes”,
“3.1.categorical_data_model”, “categorical1_project.mlxtran”)
loadProject(project)
data <- getChartsData(plotName = “plotVpc”)
p <- plotVpc(data = data, obsName = “level”,
settings = list(theoretical = TRUE, outlierDots = FALSE))
# countable data
project <- file.path(getDemoPath(), “3.models_for_noncontinuous_outcomes”,
“3.2.count_data_model”, “count1a_project.mlxtran”)
loadProject(project)
data <- getChartsData(plotName = “plotVpc”)
p <- plotVpc(data = data, obsName = “Y”)
# time to event data
project <- file.path(getDemoPath(), “3.models_for_noncontinuous_outcomes”,
“3.3.time_to_event_data_model”, “tte1_project.mlxtran”)
loadProject(project)
data <- getChartsData(plotName = “plotVpc”)
plotVpc(data = data, obsName = “Event”, eventPlot = “survivalFunction”)
## End(Not run)
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[Monolix] Plot Importance sampling convergence
Description
[Monolix] Plot Importance sampling convergence
Usage
plotImportanceSampling(settings = list(), data = NULL)
Arguments
settings |
a list of optional settings:
|
data |
dataframe – Output of getChartsData (getChartsData(“plotImportanceSampling”, …)) If data not specified, charts data will be computed inside the function. |
Value
A ggplot object
See Also
getChartsData
Click here to see examples
initializeLixoftConnectors(software = “monolix”)
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
plotImportanceSampling()
## End(Not run)
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[Monolix] Plot MCMC convergence
Description
[Monolix] Plot MCMC convergence
Usage
plotMCMC(settings = list(), data = NULL)
Arguments
settings |
a list of optional settings:
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData(“plotMCMC”, …)) If data not specified, charts data will be computed inside the function. |
Value
A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData
Click here to see examples
initializeLixoftConnectors(software = “monolix”)
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
plotMCMC()
## End(Not run)
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[Monolix] Plot SAEM convergence
Description
[Monolix] Plot SAEM convergence
Usage
plotSaem(settings = list(), data = NULL)
Arguments
settings |
a list of optional settings:
|
data |
dataframe – Output of getChartsData (getChartsData(“plotSaem”, …)) If data not specified, charts data will be computed inside the function. |
Value
A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData
Click here to see examples
initializeLixoftConnectors(software = “monolix”)
project <- file.path(getDemoPath(), “1.creating_and_using_models”,
“1.1.libraries_of_models”, “theophylline_project.mlxtran”)
loadProject(project)
plotSaem()
## End(Not run)
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Define Preferences to customize plots
Description
Define Preferences to customize plots
Usage
getPlotPreferences(plotName = NULL, update = NULL, ...)
Arguments
plotName |
(string) Name of the plot function. if plotName is NULL, all preferences are returned |
update |
list containing the plot elements to be updated. |
... |
additional arguments – dataType for some plots |
Details
This function creates a theme that customizes how a plot looks, i.e. legend, colors
fills, transparencies, linetypes an sizes, etc.
For each curve, list of available customizations:
- color: color (when lines or points)
- fill: color (when surfaces)
- opacity: color transparency
- radius: size of points
- shape: shape of points
- lineType: linetype
- lineWidth: line size
- legend: name of the legend (if NULL, no legend is displayed for the element)
Value
A list with theme specifiers
See Also
setPlotPreferences
resetPlotPreferences
Click here to see examples
preferences <- getPlotPreferences(update = list(
obs = list(color = “red”, legend = “Observations”),
obsCens = list(color = rgb(70, 130, 180, maxColorValue = 255))
))
# preferences that are used by default in the plots
preferences <- getPlotPreferences()
# preferences that are used by default in plotObservedData
preferences <- getPlotPreferences(plotName = “plotObservedData”)
## End(Not run)
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Reset plot preferences to go back to default preferences
Description
Reset plot preferences to go back to default preferences
Usage
resetPlotPreferences()
See Also
getPlotPreferences
setPlotPreferences
Click here to see examples
getPlotPreferences()$obs[c(“color”, “legend”)]
update = list(obs = list(color = “green”, legend = “Observation”))
setPlotPreferences(update = update)
getPlotPreferences()$obs[c(“color”, “legend”)]
resetPlotPreferences()
getPlotPreferences()$obs[c(“color”, “legend”)]
## End(Not run)
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Set preferences to customize plots
When preferences are Set, the updated preferences will used in all the plots
Description
Set preferences to customize plots
When preferences are Set, the updated preferences will used in all the plots
Usage
setPlotPreferences(update = NULL)
Arguments
update |
list containing the plot elements to be updated. |
Details
This function creates a theme that customizes how a plot looks, i.e. legend, colors
fills, transparencies, linetypes an sizes, etc.
For each curve, list of available customizations:
- color: color (when lines or points)
- fill: color (when surfaces)
- opacity: color transparency
- radius: size of points
- shape: shape of points
- lineType: linetype
- lineWidth: line size
- legend: name of the legend (if NULL, no legend is displayed for the element)
See Also
getPlotPreferences
resetPlotPreferences
Click here to see examples
getPlotPreferences()$obs[c(“color”, “legend”)]
update = list(obs = list(color = “green”, legend = “Observation”))
setPlotPreferences(update = update)
getPlotPreferences()$obs[c(“color”, “legend”)]
## End(Not run)
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