require(ospsuite.reportingengine)
#> Loading required package: ospsuite.reportingengine
#> Loading required package: ospsuite
#> Loading required package: rClr
#> Loading the dynamic library for Microsoft .NET runtime...
#> Loaded Common Language Runtime version 4.0.30319.42000
#> Loading required package: tlf
This vignette focuses on time profiles and residuals plots (also called goodness of fit plots) generated using either mean model or population workflows.
Overview
The time profiles and residuals task first aims at generating time profile plots of specified simulation paths. The task can account for observed data, in which case residuals are calculated and plotted. For population workflows, aggregation functions are used, by default mean, median, 5th and 95th percentiles are computed and plotted in the time profiles.
Inputs
Results obtained from the simulate
task and stored as csv files within the subdirectory SimulationResults from the workflowFolder
directory are required in order to perform the goodness of fit (plotTimeProfilesAndResiduals
) task. As a consequence, if the workflow output folder does not include the appropriate simulations, the task simulate
needs to be active.
The objects SimulationSet
(or PopulationSimulationSet
for population workflows) and Output
define which and how the simulated and, if available, observed data will be plotted.
Outputs
The time profiles and residuals task generates for each simulation set plots of:
- the simulated time profile
- in linear and logarithmic scale
- if available with data
- for total simulated time range.
Multiple applications
For simulations with multiple applications, up to 6 time profile plots can be generated corresponding to simulated time profiles in linear and logarithmic scale for:
- the total simulated time range
- the time range of the first application
- the time range of the last application
The input applicationRanges
from SimulationSet
objects can be used to select which ranges to include in the evaluation.
Residuals
If observed data are available in the range selected in the output definition and selected in dataSelection
, they will be added to the time profile plots.
Furthermore, residuals will be calculated according to the residual scale defined by the Output
objects included within the simulation sets and 6 additional plots will be created figures:
- Predicted vs observed in linear scale
- Predicted vs observed in log scale
- Residuals vs time
- Residuals vs simulated output
- Histogram of residuals
- qq-plot of residuals
The residual scale corresponds to one element from the enum ResidualScales
:
ResidualScales
#> $Linear
#> [1] "Linear"
#>
#> $Logarithmic
#> [1] "Logarithmic"
The default uses “Logarithmic” and calculate residuals as: \(Residuals=log(Observed)-log(Simulated)\)
If the option “Linear” is used instead, the residuals are calculated as: \(Residuals=Observed-Simulated\)
A histogram and a qq-plot of residuals across all the simulation sets will also be added at the end of the analysis.
For population workflows, residuals are calculated from the aggregation of the Simulated output. By default, \(median(Simulated)\) is used instead of \(Simulated\). It is possible to modify this aggregation function by updating the fields of the object AggregationConfiguration.
Template examples
The following sections will introduce template scripts including goodness of fit tasks as well as their associated reports. Table 1 shows the features tested in each template script.
Example | Workflow | Simulation Sets | Outputs | Observed Data |
---|---|---|---|---|
1 | Mean model | 1 | 1 | No |
2 | Mean model | 1 | 1 | Yes |
3 | Mean model | 1 | 1 | Yes |
4 | Mean model | 1 | 2 | Yes |
5 | Mean model | 2 | 1 | Yes |
6 | Population model | 1 | 1 | No |
7 | Population model | 1 | 1 | Yes |
Example 1
Example 1 shows one of the simplest workflows including a goodness of fit. In this example, only a simulation time profile is performed since there is no observed data. Furthermore, only one simulation set and one output are requested.
Code
# Get the pkml simulation file: "MiniModel2.pkml"
simulationFile <- system.file("extdata", "MiniModel2.pkml",
package = "ospsuite.reportingengine"
)
# Define the input parameters
outputA <- Output$new(
path = "Organism|A|Concentration in container",
displayName = "Concentration of A",
displayUnit = "nmol/ml"
)
setA <- SimulationSet$new(
simulationSetName = "A",
simulationFile = simulationFile,
outputs = outputA
)
# Create the workflow instance
workflow1 <-
MeanModelWorkflow$new(
simulationSets = setA,
workflowFolder = "Example-1"
)
#> 10/03/2023 - 15:23:18
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#> Date: 10/03/2023 - 15:23:18
#> User Information:
#> Computer Name: WDEC030076
#> User: ZTSOJ
#> Login: ZTSOJ
#> System is NOT validated
#> System versions:
#> R version: R version 4.2.2 (2022-10-31 ucrt)
#> OSP Suite Package version: 11.1.198
#> OSP Reporting Engine version: 2.1.244
#> tlf version: 1.5.125
# Set the workflow tasks to be run
workflow1$activateTasks(c("simulate", "plotTimeProfilesAndResiduals"))
# Run the workflow
workflow1$runWorkflow()
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#> Executing: pandoc --self-contained --wrap=none --toc --from=markdown+tex_math_dollars+superscript+subscript+raw_attribute --reference-doc="C:/Users/ztsoj/AppData/Local/Temp/RtmpwxXS6L/temp_libpath298c4a21481f/ospsuite.reportingengine/extdata/reference.docx" --resource-path="Example-1" -t docx -o "Example-1/Report-word.docx" "Example-1/Report-word.md"
#> 10/03/2023 - 15:23:25
#>
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The output report for Example 1 is shown below. There are only 2 figures in the report (linear and log scales) showing the time profile of the output path ‘Organism|A|Concentration in container’ displayed as ‘Concentration of A’.
Report
Example 2
Example 2 shows a workflow that used the simulation results performed in Example 1. This example shows 3 new features:
- The presence of observed data compared to the simulation
- The use of results obtained from a previous workflow
- The change of display unit without re-running the
simulate
task
Code
# Get the pkml simulation file: "MiniModel2.pkml"
simulationFile <- system.file("extdata", "MiniModel2.pkml",
package = "ospsuite.reportingengine"
)
# Get the observation file and its dictionary
observationFile = system.file(
"extdata", "SimpleData.nmdat",
package = "ospsuite.reportingengine"
)
dictionaryFile = system.file(
"extdata", "tpDictionary.csv",
package = "ospsuite.reportingengine"
)
dataSource <- DataSource$new(
dataFile = observationFile,
metaDataFile = dictionaryFile
)
# Define the input parameters
outputA <- Output$new(
path = "Organism|A|Concentration in container",
displayName = "Simulated concentration of A",
displayUnit = "µmol/ml",
dataSelection = "MDV==0",
dataDisplayName = "Observed concentration of A"
)
setA <- SimulationSet$new(
simulationSetName = "A",
simulationFile = simulationFile,
outputs = outputA,
dataSource = dataSource
)
# Create the workflow instance
workflow2 <-
MeanModelWorkflow$new(
simulationSets = setA,
workflowFolder = "Example-1"
)
#> 10/03/2023 - 15:23:32
#> i Info Reporting Engine Information:
#> Date: 10/03/2023 - 15:23:32
#> User Information:
#> Computer Name: WDEC030076
#> User: ZTSOJ
#> Login: ZTSOJ
#> System is NOT validated
#> System versions:
#> R version: R version 4.2.2 (2022-10-31 ucrt)
#> OSP Suite Package version: 11.1.198
#> OSP Reporting Engine version: 2.1.244
#> tlf version: 1.5.125
# Set the workflow tasks to be run
workflow2$activateTasks("plotTimeProfilesAndResiduals")
workflow2$inactivateTasks("simulate")
# Run the workflow
workflow2$runWorkflow()
#> 10/03/2023 - 15:23:32
#> i Info Starting run of Mean Model Workflow
#> 10/03/2023 - 15:23:32
#> i Info Starting run of Plot Time profiles and Residuals task
#> 10/03/2023 - 15:23:32
#> i Info Starting run of Plot Time profiles and Residuals task for A
#> 10/03/2023 - 15:23:45
#> i Info Plot Time profiles and Residuals task completed in 0.2 min
#> Executing: pandoc --self-contained --wrap=none --toc --from=markdown+tex_math_dollars+superscript+subscript+raw_attribute --reference-doc="C:/Users/ztsoj/AppData/Local/Temp/RtmpwxXS6L/temp_libpath298c4a21481f/ospsuite.reportingengine/extdata/reference.docx" --resource-path="Example-1" -t docx -o "Example-1/Report-word.docx" "Example-1/Report-word.md"
#> 10/03/2023 - 15:23:48
#> i Info Mean Model Workflow completed in 0.3 min
It can be seen from the run results that the task simulate
was not run.
In this example, the content of the observed data file and its dictionary are respectively described Table 2 and 3.
ID | STUD | SID | TIME | TAD | DV | CMT | MDV | AMT | WGHT | HGHT | SEX | AGE | LOQ |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 9999 | 1 | 0 | 0 | 0.00000 | 2 | 1 | 450 | 82 | 160 | 1 | 32 | 0.001 |
2 | 9999 | 1 | 2 | 2 | 0.00120 | 1 | 0 | 0 | 82 | 160 | 1 | 32 | 0.001 |
3 | 9999 | 1 | 4 | 4 | 0.00142 | 1 | 0 | 0 | 82 | 160 | 1 | 32 | 0.001 |
4 | 9999 | 1 | 6 | 6 | 0.00163 | 1 | 0 | 0 | 82 | 160 | 1 | 32 | 0.001 |
5 | 9999 | 1 | 8 | 8 | 0.00269 | 1 | 0 | 0 | 82 | 160 | 1 | 32 | 0.001 |
ID | type | datasetColumn | datasetUnit | reportName | pathID | comment |
---|---|---|---|---|---|---|
sid | identifier | SID | ||||
stud | identifier | STUD | ||||
time | timeprofile | TIME | h | |||
dv | timeprofile | DV | mg/ml | |||
tad | timeprofile | TAD | h | |||
age | covariate | AGE | year(s) | Age | Organism|Age | |
wght | covariate | WGHT | kg | Body weight | Organism|Weight | |
hght | covariate | HGHT | cm | Height | Organism|Height | |
bmi | covariate | BMI | kg/m2 | BMI | Organism|BMI | |
gender | covariate | SEX | SEX | Gender | Make sure 1=male 2= female |
The output report for Example 2 is shown below. There are now many more figures in the report showing not only time profiles in µmol/ml
instead of nmol/ml
but also residuals.
Report
Example 3
In example 3, the dictionary is changed and indicates that the lower limit of quantitation (LLOQ) is included in the observed data. Like the previous example, there is no need to re-run the simulate
task.
Code
# Get the pkml simulation file: "MiniModel2.pkml"
simulationFile <- system.file("extdata", "MiniModel2.pkml",
package = "ospsuite.reportingengine"
)
# Get the observation file and its dictionary
dataSource <- DataSource$new(
dataFile = observationFile,
metaDataFile = system.file(
"extdata", "tpDictionaryLoq.csv",
package = "ospsuite.reportingengine"
)
)
# Define the input parameters
outputA <- Output$new(
path = "Organism|A|Concentration in container",
displayName = "Simulated concentration of A",
displayUnit = "µmol/ml",
dataSelection = "MDV==0",
dataDisplayName = "Observed concentration of A"
)
setA <- SimulationSet$new(
simulationSetName = "A",
simulationFile = simulationFile,
outputs = outputA,
dataSource = dataSource
)
# Create the workflow instance
workflow3 <-
MeanModelWorkflow$new(
simulationSets = setA,
workflowFolder = "Example-1"
)
#> 10/03/2023 - 15:23:58
#> i Info Reporting Engine Information:
#> Date: 10/03/2023 - 15:23:58
#> User Information:
#> Computer Name: WDEC030076
#> User: ZTSOJ
#> Login: ZTSOJ
#> System is NOT validated
#> System versions:
#> R version: R version 4.2.2 (2022-10-31 ucrt)
#> OSP Suite Package version: 11.1.198
#> OSP Reporting Engine version: 2.1.244
#> tlf version: 1.5.125
# Set the workflow tasks to be run
workflow3$activateTasks("plotTimeProfilesAndResiduals")
workflow3$inactivateTasks("simulate")
# Run the workflow
workflow3$runWorkflow()
#> 10/03/2023 - 15:23:59
#> i Info Starting run of Mean Model Workflow
#> 10/03/2023 - 15:23:59
#> i Info Starting run of Plot Time profiles and Residuals task
#> 10/03/2023 - 15:23:59
#> i Info Starting run of Plot Time profiles and Residuals task for A
#> 10/03/2023 - 15:24:14
#> i Info Plot Time profiles and Residuals task completed in 0.3 min
#> Executing: pandoc --self-contained --wrap=none --toc --from=markdown+tex_math_dollars+superscript+subscript+raw_attribute --reference-doc="C:/Users/ztsoj/AppData/Local/Temp/RtmpwxXS6L/temp_libpath298c4a21481f/ospsuite.reportingengine/extdata/reference.docx" --resource-path="Example-1" -t docx -o "Example-1/Report-word.docx" "Example-1/Report-word.md"
#> 10/03/2023 - 15:24:16
#> i Info Mean Model Workflow completed in 0.3 min
The new dictionary content is described in Table 4.
ID | type | datasetColumn | datasetUnit | reportName | pathID | comment |
---|---|---|---|---|---|---|
sid | identifier | SID | ||||
stud | identifier | STUD | ||||
time | timeprofile | TIME | h | |||
dv | timeprofile | DV | mg/ml | |||
tad | timeprofile | TAD | h | |||
age | covariate | AGE | year(s) | Age | Organism|Age | |
wght | covariate | WGHT | kg | Body weight | Organism|Weight | |
hght | covariate | HGHT | cm | Height | Organism|Height | |
bmi | covariate | BMI | kg/m2 | BMI | Organism|BMI | |
gender | covariate | SEX | SEX | Gender | Make sure 1=male 2= female |
The output report for Example 3 is shown below. The time profiles now indicate the LLOQ.
Report
Example 4
In example 4, the simulation set now includes 2 output paths. Output path Organism|A|Concentration in container has observed data to be compared to whereas output path Organism|B|Concentration in container does not. Since output path Organism|B|Concentration in container was not requested by the examples 1, 2 and 3, it is not included in the simulation results. As a consequence, the simulate
task needs to be re-run.
Code
# Get the pkml simulation file: "MiniModel2.pkml"
simulationFile <- system.file("extdata", "MiniModel2.pkml",
package = "ospsuite.reportingengine"
)
# Get the observation file and its dictionary
dataSource <- DataSource$new(
dataFile = observationFile,
metaDataFile = dictionaryFile
)
# Define the input parameters
outputA <- Output$new(
path = "Organism|A|Concentration in container",
displayName = "Simulated concentration of A",
displayUnit = "µmol/ml",
dataSelection = "MDV==0",
dataDisplayName = "Observed concentration of A"
)
outputB <- Output$new(
path = "Organism|B|Concentration in container",
displayName = "Simulated concentration of B",
displayUnit = "µmol/ml"
)
setAB <- SimulationSet$new(
simulationSetName = "A and B",
simulationFile = simulationFile,
outputs = c(outputA, outputB),
dataSource = dataSource
)
# Create the workflow instance
workflow4 <-
MeanModelWorkflow$new(
simulationSets = setAB,
workflowFolder = "Example-4"
)
#> 10/03/2023 - 15:24:24
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#> Login: ZTSOJ
#> System is NOT validated
#> System versions:
#> R version: R version 4.2.2 (2022-10-31 ucrt)
#> OSP Suite Package version: 11.1.198
#> OSP Reporting Engine version: 2.1.244
#> tlf version: 1.5.125
# Set the workflow tasks to be run
workflow4$activateTasks(c("simulate", "plotTimeProfilesAndResiduals"))
# Run the workflow
workflow4$runWorkflow()
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#> Executing: pandoc --self-contained --wrap=none --toc --from=markdown+tex_math_dollars+superscript+subscript+raw_attribute --reference-doc="C:/Users/ztsoj/AppData/Local/Temp/RtmpwxXS6L/temp_libpath298c4a21481f/ospsuite.reportingengine/extdata/reference.docx" --resource-path="Example-4" -t docx -o "Example-4/Report-word.docx" "Example-4/Report-word.md"
#> 10/03/2023 - 15:24:39
#>
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The output report for Example 4 is shown below.
Report
Example 5
In example 5, there are 2 simulation sets, each one includes one output path. Simulation set A includes output path Organism|A|Concentration in container and has observed data. Simulation set B includes output path Organism|B|Concentration in container and has no observed data. The simulate
task needs to be re-run also in this case since the simulation results are saved in files that use the simulation set names.
Code
# Get the pkml simulation file: "MiniModel1.pkml"
simulationFileA <- system.file("extdata", "MiniModel1.pkml",
package = "ospsuite.reportingengine"
)
# Get the pkml simulation file: "MiniModel2.pkml"
simulationFileB <- system.file("extdata", "MiniModel2.pkml",
package = "ospsuite.reportingengine"
)
# Get the observation file and its dictionary
dataSource <- DataSource$new(
dataFile = observationFile,
metaDataFile = dictionaryFile
)
# Define the input parameters
outputA <- Output$new(
path = "Organism|A|Concentration in container",
displayName = "Simulated concentration of A",
displayUnit = "µmol/ml",
dataSelection = "MDV==0",
dataDisplayName = "Observed concentration of A"
)
outputB <- Output$new(
path = "Organism|B|Concentration in container",
displayName = "Simulated concentration of B",
displayUnit = "µmol/ml"
)
setA <- SimulationSet$new(
simulationSetName = "A",
simulationFile = simulationFileA,
outputs = outputA,
dataSource = dataSource
)
setB <- SimulationSet$new(
simulationSetName = "B",
simulationFile = simulationFileB,
outputs = outputB
)
# Create the workflow instance
workflow5 <-
MeanModelWorkflow$new(
simulationSets = c(setA, setB),
workflowFolder = "Example-5"
)
#> 10/03/2023 - 15:24:47
#>
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#> Date: 10/03/2023 - 15:24:47
#> User Information:
#> Computer Name: WDEC030076
#> User: ZTSOJ
#> Login: ZTSOJ
#> System is NOT validated
#> System versions:
#> R version: R version 4.2.2 (2022-10-31 ucrt)
#> OSP Suite Package version: 11.1.198
#> OSP Reporting Engine version: 2.1.244
#> tlf version: 1.5.125
# Set the workflow tasks to be run
workflow5$activateTasks(c("simulate", "plotTimeProfilesAndResiduals"))
# Run the workflow
workflow5$runWorkflow()
#> 10/03/2023 - 15:24:47
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[1m
[3mB
[23m
[22m
[39m
#> 10/03/2023 - 15:25:10
#>
[32m
[1mi Info
[22m
[39m
[36m
[1m
[3mPlot Time profiles and Residuals task
[23m
[22m
[39m completed in 0.4 min
#> Executing: pandoc --self-contained --wrap=none --toc --from=markdown+tex_math_dollars+superscript+subscript+raw_attribute --reference-doc="C:/Users/ztsoj/AppData/Local/Temp/RtmpwxXS6L/temp_libpath298c4a21481f/ospsuite.reportingengine/extdata/reference.docx" --resource-path="Example-5" -t docx -o "Example-5/Report-word.docx" "Example-5/Report-word.md"
#> 10/03/2023 - 15:25:12
#>
[32m
[1mi Info
[22m
[39m
[36m
[1m
[3mMean Model Workflow
[23m
[22m
[39m completed in 0.4 min
The output report for Example 5 is shown below.
Report
Example 6
Example 6 shows a goodness of fit for a population workflow. In this example, there is no observed data, only one simulation set and one output are requested.
Code
# Get the pkml simulation file: "MiniModel2.pkml"
simulationFile <- system.file("extdata", "MiniModel2.pkml",
package = "ospsuite.reportingengine"
)
populationFile <- system.file("extdata", "Pop500_p1p2p3.csv",
package = "ospsuite.reportingengine"
)
# Define the input parameters
outputA <- Output$new(
path = "Organism|A|Concentration in container",
displayName = "Concentration of A",
displayUnit = "µmol/ml"
)
setA <- PopulationSimulationSet$new(
simulationSetName = "A",
simulationFile = simulationFile,
populationFile = populationFile,
outputs = outputA
)
# Create the workflow instance
workflow6 <-
PopulationWorkflow$new(
workflowType = PopulationWorkflowTypes$parallelComparison,
simulationSets = setA,
workflowFolder = "Example-6"
)
#> 10/03/2023 - 15:25:22
#> i Info Reporting Engine Information:
#> Date: 10/03/2023 - 15:25:22
#> User Information:
#> Computer Name: WDEC030076
#> User: ZTSOJ
#> Login: ZTSOJ
#> System is NOT validated
#> System versions:
#> R version: R version 4.2.2 (2022-10-31 ucrt)
#> OSP Suite Package version: 11.1.198
#> OSP Reporting Engine version: 2.1.244
#> tlf version: 1.5.125
# Set the workflow tasks to be run
workflow6$activateTasks(c("simulate", "plotTimeProfilesAndResiduals"))
# Run the workflow
workflow6$runWorkflow()
#> 10/03/2023 - 15:25:22
#> i Info Starting run of Population Workflow for parallelComparison
#> 10/03/2023 - 15:25:22
#> i Info Starting run of Simulation task
#> 10/03/2023 - 15:25:23
#> i Info Simulation task completed in 0 min
#> 10/03/2023 - 15:25:23
#> i Info Starting run of Plot Time profiles and Residuals task
#> 10/03/2023 - 15:25:23
#> i Info Starting run of Plot Time profiles and Residuals task for A
#> 10/03/2023 - 15:25:28
#> i Info Plot Time profiles and Residuals task completed in 0.1 min
#> Executing: pandoc --self-contained --wrap=none --toc --from=markdown+tex_math_dollars+superscript+subscript+raw_attribute --reference-doc="C:/Users/ztsoj/AppData/Local/Temp/RtmpwxXS6L/temp_libpath298c4a21481f/ospsuite.reportingengine/extdata/reference.docx" --resource-path="Example-6" -t docx -o "Example-6/Report-word.docx" "Example-6/Report-word.md"
#> 10/03/2023 - 15:25:30
#> i Info Population Workflow for parallelComparison completed in 0.1 min
The output report for Example 6 is shown below. The 2 figures of the report (linear and log scales) include median, mean 5th and 95th percentiles of the population.
Report
Example 7
Example 7 add observed data to the previous workflow.
Code
# Get the pkml simulation file: "MiniModel2.pkml"
simulationFile <- system.file("extdata", "MiniModel2.pkml",
package = "ospsuite.reportingengine"
)
populationFile <- system.file("extdata", "Pop500_p1p2p3.csv",
package = "ospsuite.reportingengine"
)
# Get the observation file and its dictionary
dataSource <- DataSource$new(
dataFile = observationFile,
metaDataFile = dictionaryFile
)
# Define the input parameters
outputA <- Output$new(
path = "Organism|A|Concentration in container",
displayName = "Simulated concentration of A",
displayUnit = "µmol/ml",
dataSelection = "MDV==0",
dataDisplayName = "Observed concentration of A"
)
setA <- PopulationSimulationSet$new(
simulationSetName = "A",
simulationFile = simulationFile,
populationFile = populationFile,
outputs = outputA,
dataSource = dataSource
)
# Create the workflow instance
workflow7 <-
PopulationWorkflow$new(
workflowType = PopulationWorkflowTypes$parallelComparison,
simulationSets = setA,
workflowFolder = "Example-6"
)
#> 10/03/2023 - 15:25:36
#> i Info Reporting Engine Information:
#> Date: 10/03/2023 - 15:25:36
#> User Information:
#> Computer Name: WDEC030076
#> User: ZTSOJ
#> Login: ZTSOJ
#> System is NOT validated
#> System versions:
#> R version: R version 4.2.2 (2022-10-31 ucrt)
#> OSP Suite Package version: 11.1.198
#> OSP Reporting Engine version: 2.1.244
#> tlf version: 1.5.125
# Set the workflow tasks to be run
workflow7$activateTasks(StandardPlotTasks$plotTimeProfilesAndResiduals)
workflow7$inactivateTasks(StandardSimulationTasks$simulate)
# Run the workflow
workflow7$runWorkflow()
#> 10/03/2023 - 15:25:36
#> i Info Starting run of Population Workflow for parallelComparison
#> 10/03/2023 - 15:25:36
#> i Info Starting run of Plot Time profiles and Residuals task
#> 10/03/2023 - 15:25:36
#> i Info Starting run of Plot Time profiles and Residuals task for A
#> 10/03/2023 - 15:25:50
#> i Info Plot Time profiles and Residuals task completed in 0.2 min
#> Executing: pandoc --self-contained --wrap=none --toc --from=markdown+tex_math_dollars+superscript+subscript+raw_attribute --reference-doc="C:/Users/ztsoj/AppData/Local/Temp/RtmpwxXS6L/temp_libpath298c4a21481f/ospsuite.reportingengine/extdata/reference.docx" --resource-path="Example-6" -t docx -o "Example-6/Report-word.docx" "Example-6/Report-word.md"
#> 10/03/2023 - 15:25:53
#> i Info Population Workflow for parallelComparison completed in 0.3 min
The output report for Example 7 is shown below. The residuals are calculated on the median simulated values.