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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
plotWeight = FALSE
variance_components = TRUE
plotFrequencies = FALSE
output = knitrFIM(object)
if ( output$typeOfFIM[1] == "BayesianFim" ){
  title_shrinkage = TRUE
  variance_components = FALSE
}else{
  title_shrinkage = FALSE
  variance_components = TRUE
}
if ( output$typeOfFIM[1] == "PopulationFim" ){
  plotFrequencies = TRUE
}

Model equations

knitrModelEquations(object)

Error model

knitrModelError(object)

Model parameters

knitrModelParameters(object)

Administration parameters

knitrAdministrationParameters(object)

Initial design

knitrInitialDesigns(object)

Determinant, condition numbers and D-criteria of the FIM

output = knitrFIM( object )
output$criteriaFimInitialDesign

Optimal design

knitrOptimalDesign(object)

Fisher information matrix

Fixed effects

output = knitrFIM(object)
output$fIMFixedEffects 

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

output = knitrFIM(object)
output$fIMRandomEffects

Correlation matrix

Fixed effects

output = knitrFIM(object)
output$correlationFixedEffects 

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

output = knitrFIM(object)
output$correlationRandomEffects 

Determinant, condition numbers and D-criteria of the FIM

output = knitrFIM(object)
output$criteriaFim

r if ( title_shrinkage ) '## Values for SE and RSE and shrinkage'

output = knitrFIM(object)
output$se_rse

r if ( variance_components ) '## Values for SE and RSE '

output = knitrFIM(object)
output$se_rse

Graphs of the responses

for ( i in 1:length( plotResponses ) )
{
print( plotResponses[[i]] )
}

Graphs of the SE and RSE

SE =plotSE( object )
print(SE[[1]])
RSE = plotRSE( object )
print(RSE[[1]])

r if ( title_shrinkage ) '# Graph of shrinkage'

if ( title_shrinkage ==TRUE){
  shrinkage = plotShrinkage( object )
  print(shrinkage[[1]])}

r if ( plotFrequencies ) '# Graph for the optimal frequencies'

output = plotFrequenciesOptimisation( object )
for ( i in 1:length(output$plot[[1]] ) )
{
print(output$plot[[1]][[i]] )
}

PFIM documentation built on June 24, 2022, 9:06 a.m.