# | 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)
```

```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]])}
```

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