fnoise | R Documentation |

These functions and objects are mostly internal and should not be needed for routine use. Generate noise distribution, currently standard normal and standardised t-distributions. These functions can be used as templates for new distributions.

```
fdist_stdnorm()
fdist_stdt(df, fixed = TRUE)
fn_stdt(df, fixed = TRUE)
b_show(x)
distlist(type, param, ncomp = NULL, fixed = FALSE, tr = NULL, ...)
ed_nparam
ed_parse(s)
ed_skeleton(df, fixed = FALSE, n = length(df), tr = NULL)
ed_src
ed_stdnorm
ed_stdt
ed_stdt0
ed_stdt1
ft_stdt
```

`df` |
degrees of freedom |

`fixed` |
if TRUE, the parameters are fixed, otherwise they are variable, see Details. |

`x` |
a fitted object. |

`type` |
list of distributions. |

`param` |
parameters. |

`ncomp` |
number of components. |

`tr` |
transformation. |

`...` |
not used. |

`s` |
named vector. |

`n` |
number of different degrees of freedom. |

If argument `fixed`

is TRUE, estimation functions assume that the
parameter(s) are fixed, otherwise they estimate it. The support is
incomplete, see below.

`fdist_stdnorm`

is for the standard normal distribution. For
example `dist_norm`

is generated by it.

`fdist_stdt`

is for the t-distribution with `df`

degrees of
freedom.

`fn_stdt`

is also for the t-distribution but the degrees of
freedom, `df`

, may be a vector. The value is a list of
distributions. Although the list can be obtained by repeated calls of
`fdist_stdt`

The support is incomplete. In particular, if parameter `fixed`

is TRUE, changes to the parameter(s) should probably not be allowed
(this can be achieved by simply dropping the corresponding function
from the list). However, a thorough rethinking is necessary, as I
introduced it on the fly while developing estimation functions and
forbidding changes may necessitate changes in the code. Changes are
useful for estimation for convenience but also to avoid recreating the
whole distributions again and again.

However, there is a major drawback, which in the final version needs to be addressed satisfactorily. Since parameters are held in local environments, changes to the parameters are reflected in copies of the objects. For example, an estimation function (or the user) may call another function with a model containing an object generated by the above functions and assign the result to a new object. However if the parameters of the noise distribution are changed in the process this will be reflected in the original model.

Note that the above effect is valid only if an object generated by the above functions is reused. Objects created by different calls have different environments, so the problem does not arise for them.

```
stdt3 <- fdist_stdt(3)
stdt3v <- fdist_stdt(3, fixed = FALSE)
fn_stdt(c(20, 30, 40), fixed = FALSE)
mo_tf <- new("MixARgen", prob = exampleModels$WL_ibm@prob,
scale = exampleModels$WL_ibm@scale, arcoef = exampleModels$WL_ibm@arcoef@a,
dist = list(generator = function(par)
fn_stdt(par, fixed = FALSE), param = c(20, 30, 40)))
mo_tf
str(mo_tf)
noise_dist(mo_tf, "pdf")
parameters(mo_tf)
parameters(mo_tf, names = TRUE)
get_edist(mo_tf)
noise_params(mo_tf)
```

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