update examples (#13)

* run styler * run styler on package * temp remove R folder * Create parameter.R * Update NAMESPACE * ud doc * ud documentation for cod-generation * ud distribution documentation * ud output documentation * ud * ud uncertainty documentation * ud local sensitivity documentation * Create efast-functions.R * move list structure generation to functions where they are used instead of passing them from runEFAST * rename evaluation lists * Update efast documentation * up efast documentation * move functions used by multiple methods from sobol script * temp revert to fix run NaN errors * Update efast-functions.R * Update efast-functions.R * Update efast-functions.R * ud efast documentation * Update utilities.R * ud su functions * ud * ud * unused function * document morris * ud * ud * ud * Create app.R * ud * ud * add number labels to morris plot * add uncertainty plots to GUI * rename gsa bar plot function * ud * ud * use distribution objects * ctrl-i * Update lowry-functions.R * Update app.R * ud - remove export lowry, only bar graphs for gsa * Update morris-functions.R * Update morris-functions.R * ud examples, merge midazolam * Create example2-UV-filters.R * ud * Create example4-itraconazole.R * ud * Update example-UV-filters.R * Update example-UV-filters.R * Update app.R * ud * Delete pltGSABarGraph.Rd * Update appveyor.yml
Open-Systems-Pharmacology · Nov 6, 2024 · 383f636 · 383f636
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diff --git a/NAMESPACE b/NAMESPACE
@@ -3,16 +3,14 @@
 export(LogNormalDistribution)
 export(LogUniformDistribution)
 export(NormalDistribution)
-export(SADistribution)
 export(SAOutput)
 export(SAParameter)
 export(UniformDistribution)
 export(distribution)
-export(generateLowryPlot)
+export(generateEFASTBarGraph)
 export(generateMorrisPlot)
+export(generateSobolBarGraph)
 export(generateTornadoPlot)
-export(getParameterListForUncertaintyAnalysis)
-export(pltGSABarGraph)
 export(runEFAST)
 export(runGUI)
 export(runMorris)

diff --git a/R/app.R b/R/app.R
diff --git a/R/distribution.R b/R/distribution.R
@@ -1,6 +1,5 @@
 #' @title SADistribution
 #' @description R6 class defining a statistical distribution object
-#' @export
 SADistribution <- R6::R6Class(classname = "SADistribution")
 
 #' @title UniformDistribution

diff --git a/R/lowry-functions.R b/R/lowry-functions.R
@@ -2,15 +2,14 @@
 #' @description Function to generate a Lowry plot of Sobol or EFAST sensitivity analysis results.
 #' @param gsaResultsDataframe Sobol or EFAST results returned by the `runSobol` or `runFAST` functions, respectively.
 #' @return A list of ggplot Lowry plots, one corresponding to each output path/PK parameter combination.
-#' @export
 generateLowryPlot <- function(gsaResultsDataframe) {
   plotList <- list()
 
   for (op in unique(gsaResultsDataframe[["Output"]])) {
     plotList[[op]] <- list()
     for (pk in unique(gsaResultsDataframe[["PK"]][gsaResultsDataframe[["Output"]] == op])) {
       df <- gsaResultsDataframe[gsaResultsDataframe$Output == op & gsaResultsDataframe$PK == pk, ]
-      outputDisplayName <- unique(df$OutputDisplayName)
+      outputDisplayName <- unique(df$OutputDisplayName)[1]
       plotList[[op]][[pk]] <- getLowryPlot(df, outputDisplayName, pk)
     }
   }
@@ -31,9 +30,7 @@ getLowryPlot <- function(df, outputDisplayName, pk) {
   df$ParameterRanks <- sapply(df$Parameter, function(par) {
     which(sortedParameters == par)
   })
-
   df$Measure <- factor(df$Measure, levels = c("Total", "FirstOrder"))
-
   dfFirstOrder <- df[df$Measure == "FirstOrder", ][sortedParameterOrder, ]
   dfTotal <- df[df$Measure == "Total", ][sortedParameterOrder, ]
 
@@ -108,12 +105,59 @@ getLowryPlot <- function(df, outputDisplayName, pk) {
   return(plt)
 }
 
-#' @title pltGSABarGraph
+
+
+
+#' @title generateSobolBarGraph
+#' @description Function to generate a bar graph of Sobol sensitivity analysis results.
+#' @param gsaResultsDataframe Sobol results returned by the `runSobol` function.
+#' @return A list of ggplot bar graphs, one corresponding to each output path/PK parameter combination.
+#' @export
+generateSobolBarGraph <- function(gsaResultsDataframe) {
+  plotList <- generateGSABarGraph(gsaResultsDataframe = gsaResultsDataframe, plottingFunction = getSobolBarGraph)
+  return(plotList)
+}
+
+
+
+#' @title generateEFASTBarGraph
+#' @description Function to generate a bar graph of EFAST sensitivity analysis results.
+#' @param gsaResultsDataframe EFAST results returned by the `runEFAST` function.
+#' @return A list of ggplot bar graphs, one corresponding to each output path/PK parameter combination.
+#' @export
+generateEFASTBarGraph <- function(gsaResultsDataframe) {
+  plotList <- generateGSABarGraph(gsaResultsDataframe = gsaResultsDataframe, plottingFunction = getEFASTBarGraph)
+  return(plotList)
+}
+
+
+#' @title generateGSABarGraph
 #' @description Function to generate a bar graph of Sobol or EFAST sensitivity analysis results.
-#' @param df Sobol or EFAST results returned by the `runSobol` or `runFAST` functions, respectively.
+#' @param gsaResultsDataframe Sobol or EFAST results returned by the `runSobol` or `runEFAST` functions, respectively.
+#' @param plottingFunction The name of the function to use for plotting. Choose either `getSobolBarGraph` for Sobol results or `getEFASTBarGraph` for EFAST results.
+#' @return A list of ggplot bar graphs, one corresponding to each output path/PK parameter combination.
+generateGSABarGraph <- function(gsaResultsDataframe,plottingFunction) {
+  plotList <- list()
+  for (op in unique(gsaResultsDataframe[["Output"]])) {
+    plotList[[op]] <- list()
+    for (pk in unique(gsaResultsDataframe[["PK"]][gsaResultsDataframe[["Output"]] == op])) {
+      df <- gsaResultsDataframe[gsaResultsDataframe$Output == op & gsaResultsDataframe$PK == pk, ]
+      outputDisplayName <- unique(df$OutputDisplayName)[1]
+      plotList[[op]][[pk]] <- plottingFunction(df, outputDisplayName, pk)
+    }
+  }
+  return(plotList)
+}
+
+
+
+#' @title generateBarGraph
+#' @description Function to generate a bar graph of Sobol or EFAST sensitivity analysis results.
+#' @param df Sobol or EFAST results dataframe.
+#' @param outputDisplayName String. The display name of the output.
+#' @param pk String. The name of the PK parameter
 #' @return A list of ggplot bar graph plots, one corresponding to each output path/PK parameter combination.
-#' @export
-pltGSABarGraph <- function(df) {
+getGSABarGraph <- function(df, outputDisplayName, pk) {
   parameterOrder <- df[df$Measure == "FirstOrder", ]$Parameter[order(-df$Value[df$Measure == "FirstOrder"])]
   df$Parameter <- factor(df$Parameter, levels = parameterOrder)
   plt <- ggplot(data = df) +
@@ -137,3 +181,29 @@ pltGSABarGraph <- function(df) {
   plt <- plt + scale_x_discrete(labels = xlabels)
   return(plt)
 }
+
+
+#' @title getSobolBarGraph
+#' @description Function to generate a bar graph of Sobol sensitivity analysis results.
+#' @param df Sobol results dataframe.
+#' @param outputDisplayName String. The display name of the output.
+#' @param pk String. The name of the PK parameter
+#' @return A list of ggplot bar graph plots, one corresponding to each output path/PK parameter combination.
+getSobolBarGraph <- function(df, outputDisplayName, pk) {
+  plt <- getGSABarGraph(df, outputDisplayName, pk)
+  plt <- plt + ggtitle(label = paste("Sobol sensitivity"), subtitle = paste0("Output: ", outputDisplayName, "\nPK: ", pk))
+  return(plt)
+}
+
+#' @title getEFASTBarGraph
+#' @description Function to generate a bar graph of EFAST sensitivity analysis results.
+#' @param df EFAST results dataframe.
+#' @param outputDisplayName String. The display name of the output.
+#' @param pk String. The name of the PK parameter
+#' @return A list of ggplot bar graph plots, one corresponding to each output path/PK parameter combination.
+getEFASTBarGraph <- function(df, outputDisplayName, pk) {
+  plt <- getGSABarGraph(df, outputDisplayName, pk)
+  plt <- plt + ggtitle(label = paste("EFAST sensitivity"), subtitle = paste0("Output: ", outputDisplayName, "\nPK: ", pk))
+  return(plt)
+}
+
diff --git a/R/morris-functions.R b/R/morris-functions.R
@@ -346,19 +346,28 @@ runMorris <- function(simulation,
 #' @title generateMorrisPlot
 #' @description Function to generate a plot of Morris sensitivity analysis.
 #' @param morrisResults Morris sensitivity results returned by `runMorris` function.
+#' @param logPlot Logical setting.  The Morris results are plotted on a logarithmic scale if `TRUE`.
 #' @return A list of ggplots of Morris sensitivity analysis results, one corresponding to each output path/PK parameter combination.
 #' @export
-generateMorrisPlot <- function(morrisResults) {
+generateMorrisPlot <- function(morrisResults, logPlot = FALSE) {
+  pltFn <- function(x){x}
+  if(logPlot){
+    pltFn <- log10
+  }
   morrisPlots <- list()
   for (outputPath in unique(morrisResults$Output)) {
     morrisPlots[[outputPath]] <- list()
     for (pk in unique(morrisResults[morrisResults$Output == outputPath, ]$PK)) {
       df <- morrisResults[morrisResults$Output == outputPath & morrisResults$PK == pk, ]
-
-      plt <- ggplot2::ggplot(data = df, mapping = aes(x = mustar, y = stdv, color = Parameter, label = Parameter)) +
-        ggplot2::geom_point() +
-        ggplot2::labs(x = paste0("\u03bc", "*"), y = "\u03c3", title = "Morris plot", subtitle = paste0("Output ", outputPath, "\nPK: ", pk)) +
-        geom_text(hjust = 0, vjust = 0)
+      df <- df[rev(order(df$rankingNorm)),]
+      df$label <- seq_along(df$Parameter)
+      df$label <- as.factor(df$label)
+      df$legendLabel <- sapply( 1:nrow(df) , function(nn){ paste0( df$label[nn] , ": " , df$Parameter[nn] ) } )
+      plt <- ggplot2::ggplot(data = df, mapping = aes(x = pltFn(mustar), y = pltFn(stdv), color = label, label = label)) +
+        ggplot2::geom_point(size = 2) +
+        ggplot2::labs(x = paste0("\u03bc", "*"), y = "\u03c3", title = "Morris sensitivity", subtitle = paste0("Output: ", outputPath, "\nPK: ", pk)) +
+        ggplot2::scale_color_discrete(name = "Parameter" , labels = df$legendLabel) +
+        ggplot2::geom_text(hjust = 0, vjust = 0,size = 6,show.legend = FALSE)
       morrisPlots[[outputPath]][[pk]] <- plt
     }
   }

diff --git a/R/su-functions.R b/R/su-functions.R
@@ -61,7 +61,6 @@ getSensitivityClass <- function(sensitivityValue) {
 #' @param simulation The simulation object which contains parameters at paths `parameterPaths`.
 #' @param variationRange The variation range used to define the log-uniform distribution of the parameters in `parameterPaths`.
 #' @return A list of `SAParameter` objects.
-#' @export
 getParameterListForUncertaintyAnalysis <- function(parameterPaths, simulation, variationRange = 0.1) {
   parameters <- list()
 

diff --git a/appveyor.yml b/appveyor.yml
@@ -10,7 +10,7 @@ init:
 install:
   - ps: Bootstrap
   - git submodule update --init --recursive
-  - ps: cinst pandoc --no-progress
+  - ps: choco install pandoc --no-progress
   - ps: $env:Path += ";C:\Program Files (x86)\Pandoc\"
 
 environment:

diff --git a/examples/efast-DDI-Rifampicin-Midazolam-example.R b/examples/efast-DDI-Rifampicin-Midazolam-example.R
diff --git a/examples/efast-simple-example.R b/examples/efast-simple-example.R
@@ -11,25 +11,25 @@ parametersList <- list(
                   path = tree$Comp$X1$path,
                   displayName = "X1",
                   unit = ospUnits$Dimensionless$Unitless,
-                  parameterDistribution = distribution$Uniform(minimum = 0,maximum = 1)
+                  parameterDistribution = UniformDistribution$new(minimum = 0,maximum = 1)
   ),
   SAParameter$new(simulation = sim,
                   path = tree$Comp$X2$path,
                   displayName = "X2",
                   unit = ospUnits$Dimensionless$Unitless,
-                  parameterDistribution = distribution$Uniform(minimum = 0,maximum = 1)
+                  parameterDistribution = UniformDistribution$new(minimum = 0,maximum = 1)
   ),
   SAParameter$new(simulation = sim,
                   path = tree$Comp$X3$path,
                   displayName = "X3",
                   unit = ospUnits$Dimensionless$Unitless,
-                  parameterDistribution = distribution$Uniform(minimum = 0,maximum = 1)
+                  parameterDistribution = UniformDistribution$new(minimum = 0,maximum = 1)
   ),
   SAParameter$new(simulation = sim,
                   path = tree$Comp$X4$path,
                   displayName = "X4",
                   unit = ospUnits$Dimensionless$Unitless,
-                  parameterDistribution = distribution$Uniform(minimum = 0,maximum = 1)
+                  parameterDistribution = UniformDistribution$new(minimum = 0,maximum = 1)
   )
 )
 

diff --git a/...tainty-DDI-Rifampicin-Midazolam-example.R → examples/example-DDI-Rifampicin-Midazolam.R b/...tainty-DDI-Rifampicin-Midazolam-example.R → examples/example-DDI-Rifampicin-Midazolam.R
@@ -13,37 +13,37 @@ parametersList <- list(
                   path = tree$`Midazolam-CYP3A4-Optimized`$Km$path,
                   displayName = "Km",
                   unit = ospUnits$`Concentration [molar]`$`µmol/l`,
-                  parameterDistribution = distribution$LogUniform(minimum = 2,maximum = 8)
+                  parameterDistribution = LogUniformDistribution$new(minimum = 2,maximum = 8)
   ),
   SAParameter$new(simulation = sim,
                   path = tree$`Midazolam-CYP3A4-Optimized`$kcat$path,
                   displayName = "kcat",
                   unit = ospUnits$`Inversed time`$`1/min`,
-                  parameterDistribution = distribution$LogUniform(minimum = 8.76/2,maximum = 8.76*2)
+                  parameterDistribution = LogUniformDistribution$new(minimum = 8.76/2,maximum = 8.76*2)
   ),
   SAParameter$new(simulation = sim,
                   path = tree$Midazolam$Lipophilicity$path,
                   displayName = "Lipophilicity",
                   unit = ospUnits$`Log Units`$`Log Units`,
-                  parameterDistribution = distribution$Uniform(minimum = 2,maximum = 4)
+                  parameterDistribution = UniformDistribution$new(minimum = 2,maximum = 4)
   ),
   SAParameter$new(simulation = sim,
                   path = tree$Midazolam$`Fraction unbound (plasma, reference value)`$path,
                   displayName = "fu",
                   unit = ospUnits$Dimensionless$Unitless,
-                  parameterDistribution = distribution$Uniform(minimum = 0.01,maximum = 0.05)
+                  parameterDistribution = UniformDistribution$new(minimum = 0.01,maximum = 0.05)
   ),
   SAParameter$new(simulation = sim,
                   path = tree$Rifampicin$`CYP3A4-Templeton 2011`$EC50$path,
                   displayName = "EC50",
                   unit = ospUnits$`Concentration [molar]`$`µmol/l`,
-                  parameterDistribution = distribution$LogUniform(minimum = 0.34*0.5,maximum = 0.34/0.5)
+                  parameterDistribution = LogUniformDistribution$new(minimum = 0.34*0.5,maximum = 0.34/0.5)
   ),
   SAParameter$new(simulation = sim,
                   path = tree$Rifampicin$`CYP3A4-Templeton 2011`$Emax$path,
                   displayName = "Emax",
                   unit = ospUnits$Dimensionless$Unitless,
-                  parameterDistribution = distribution$LogUniform(minimum = 9*0.5,maximum = 9/0.5)
+                  parameterDistribution = LogUniformDistribution$new(minimum = 9*0.5,maximum = 9/0.5)
   )
 )
 
@@ -54,9 +54,7 @@ Y$addPKParameter(standardPKParameter = "C_max")
 Y$addPKParameter(standardPKParameter = "AUC_inf")
 outputList <- list(Y)
 
-numberOfSamples <- 100
-
-#****** RUN THE SENSITIVITY AND UNCERTAINTY ANALYSIS.
+#Run local sensitivity analysis and uncertainty analysis
 su <- runSU(simulation = sim,
             DDIsimulation = DDIsim,
             customParameters = parametersList,
@@ -68,7 +66,35 @@ su <- runSU(simulation = sim,
             sensitivityThreshold = 0,
             #Uncertainty analysis parameters:
             runUncertaintyAnalysis = TRUE,
-            numberOfUncertaintyAnalysisSamples = numberOfSamples,
+            numberOfUncertaintyAnalysisSamples = 100,
             quantiles = c(0.05,0.25,0.5,0.75,0.95))
-
 generateAndSaveSUSummaryDf(suResults = su,savePath = "data/su-DDI-summary-DF.xlsx")
+localSensitivityPlts <- generateTornadoPlot(sensitivityDataFrame = suResults$Results,generateForUncertaintyAnalysis = FALSE)
+uncertaintyPlts <- generateTornadoPlot(sensitivityDataFrame = suResults$Results,generateForUncertaintyAnalysis = TRUE)
+
+
+#Run Morris sensitivity analysis
+morrisResults <- runMorris(simulation = sim,
+                           DDIsimulation = DDIsim,
+                           parameters = parametersList,
+                           outputs = outputList,
+                           numberOfSamples = 100)
+morrisPlts <- generateMorrisPlot(morrisResults$Results)
+
+
+#Run Sobol sensitivity analysis
+sobolResults <- runSobol(simulation = sim,
+                         DDIsimulation = DDIsim,
+                         parameters = parametersList,
+                         outputs = outputList,
+                         numberOfSamples = 1000)
+sobolPlts <- generateSobolBarGraph(gsaResultsDataframe = sobolResults$Results)
+
+
+#Run EFAST sensitivity analysis
+efastResultsDf <- runEFAST(simulation = sim,
+                           DDIsimulation = DDIsim,
+                           parameters = parametersList,
+                           outputs = outputList,
+                           numberOfResamples = 1)
+efastPlts <- generateEFASTBarGraph(gsaResultsDataframe = efastResultsDf$Results)