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In PFIM: Population Fisher Information Matrix

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

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]])}

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