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In PFIM: Population Fisher Information Matrix

options(kableExtra.latex.load_packages = FALSE)
options(kableExtra.auto_format = FALSE)
knitr::opts_chunk$set(echo = TRUE)

title_shrinkage = FALSE
variance_components = TRUE
if ( fimType == "BayesianFim" )
{
  title_shrinkage = TRUE
  variance_components = FALSE
}

Model outputs

tablesEvaluationFIMIntialDesignResults$tablesModelEquations$outcomes

Model equations

tablesEvaluationFIMIntialDesignResults$tablesModelEquations$equations

Model error

tablesEvaluationFIMIntialDesignResults$tablesModelError

Model parameters

tablesEvaluationFIMIntialDesignResults$tablesModelParameters

Administration parameters

tablesEvaluationFIMIntialDesignResults$tablesAdministration

Initial design

tablesEvaluationFIMIntialDesignResults$tablesDesign

Administration and sampling time constraints

variableSelection = c("Design","Arm","Outcome", "Doses", "Initial sampling times","Fixed times", "Number of samplings optimisable")

table = tablesOptimizationObject$tablesSamplingConstraints[,variableSelection]
colnames( table ) = variableSelection

knitr::kable( table ) %>%
  kable_styling( font_size = 12,
                 latex_options = c("hold_position","striped", "condensed", "bordered" ),
                 full_width = T)

Determinant, condition numbers and D-criterion of the FIM

tablesEvaluationFIMIntialDesignResults$tablesFIM$criteriaFimTable 

Optimal design

tablesOptimizationObject$tablesDesign

Fisher information matrix

Fixed effects

tablesOptimizationObject$tablesFIM$FIMFixedEffectsTable 

r if ( variance_components ) '### Variance effects'

tablesOptimizationObject$tablesFIM$FIMVarianceEffectsTable 

Correlation matrix

Fixed effects

tablesOptimizationObject$tablesFIM$correlationMatrixFixedEffectsTable 

r if ( variance_components ) '### Variance effects'

tablesOptimizationObject$tablesFIM$correlationMatrixVarianceEffectsTable 

Determinant, condition numbers and D-criterion of the FIM

tablesOptimizationObject$tablesFIM$criteriaFimTable 

Values for SE and RSE

tablesOptimizationObject$tablesFIM$SEandRSETable 

Graphs for the responses

plotOutcomesEvaluation = tablesOptimizationObject$tablesPlot$plotOutcomesEvaluation

designNames = names( plotOutcomesEvaluation )

for ( designName in designNames )
{
  armNames = names( plotOutcomesEvaluation[[ designName ]] )

  for ( armName in armNames )
  { 
    outcomes = names( plotOutcomesEvaluation[[ designName ]][[armName]] )

    for ( outcome in outcomes )
    {
      print( plotOutcomesEvaluation[[designName]][[armName]][[outcome]] )
    }
  }
}

Graphs for the sensitivity indices

plotOutcomesGradient = tablesOptimizationObject$tablesPlot$plotOutcomesGradient

designNames = names( plotOutcomesGradient )

for ( designName in designNames )
{
  armNames = names( plotOutcomesGradient[[ designName ]] )

  for ( armName in armNames )
  { 
    outcomes = names( plotOutcomesGradient[[ designName ]][[armName]] )

    for ( outcome in outcomes )
    {
      parameterNames = names( plotOutcomesGradient[[ designName ]][[armName]][[outcome]] )

      for ( parameterName in parameterNames )
      {
        print( plotOutcomesGradient[[designName]][[armName]][[outcome]][[parameterName]] )
      }
    }
  }
}

Graphs for the SE and RSE

plotSE = tablesOptimizationObject$tablesPlot$plotSE
plotRSE = tablesOptimizationObject$tablesPlot$plotRSE

designNames = names( plotSE )

for ( designName in designNames )
{
  print( plotSE[[designName]] )
  print( plotRSE[[designName]] )
}

Graph for the optimal weights

plotWeights

r if ( title_shrinkage ) '# Graph of shrinkage'

if ( title_shrinkage ==TRUE)
{
  plotShrinkage = tablesOptimizationObject$tablesPlot$plotShrinkage

  designNames = names( plotShrinkage )

  for ( designName in designNames )
  {
    print( plotShrinkage[[designName]] )
  }
}

PFIM documentation built on Oct. 30, 2024, 9:10 a.m.