# Construct a (smooth) marginal z density approximation from a model information list

### Description

Construct a (smooth) marginal z density approximation from a model information list

### Usage

1 2 | ```
getMarginalZ(info, method = c("linear", "spline", "logspline", "normalspline",
"normal"), verbose = FALSE, plot = FALSE)
``` |

### Arguments

`info` |
the model information list |

`method` |
method for approximating the marginal density: - linear
Linearly interpolate the points. - spline
The saved points of the unnormalized density approximation are joined by a “monotonic” spline. The density is smoothed out to zero at the tails. Since the spline might be slightly negative for extreme values, the positive part is returned. - logspline
The saved points of the log unnormalized density approximation are joined by a “monotonic” spline, which is then exponentiated. - normalspline
A “monotonic” spline is fitted to the differences of the saved log density values and the log normal approximation. The resulting spline function is exponentiated and then multiplied with the normal density. - normal
Just take the normal approximation.
This may also be a vector with more than one method names, to select the modify the preference sequence: If the first method does not work, the second is tried and so on. The normal approximation always “works” (but may give bad results). |

`verbose` |
Echo the chosen method? (not default) |

`plot` |
produce plots of the different approximation steps? (not default) |

### Value

a list with the log of the normalized density approximation (“logDens”) and the random number generator (“gen”).