SpecDLM-class {demest} | R Documentation

## An S4 class to specify a dynamic linear model (DLM) prior.

### Description

An object of class `SpecDLM` specifies a prior in which elements that are next to each other are are expected to be more similar than elements that are distant for each other. DLMs are typically used to model variation over time, but are often also appropriate for variation over age.

### Details

The prior consists of a level term, optional trend, seasonal, and covariates terms, and an error term. The error term can follow a normal or t distribution. For details, see the documentation for function `DLM`.

### Slots

`along`

  The name of the dimension that the time series (or age equivalents) extend along.

`phi`

  Damping parameter.

`phiKnown`

  Whether the damping parameter is known or is estimated from the data.

`minPhi`

  Minimum value for the damping parameter.

`maxPhi`

  Maximum value for the damping parameter.

`nuAlpha`

  Degrees of freedom for the prior for the standard deviation for innovations in the level term.

`AAlpha`

  Scale for the prior for the standard deviation for innovations in the level term.

`omegaAlphaAMax`

  Maximum for the standard deviation for innovations in the level term.

`nuDelta`

  Degrees of freedom for the prior for the standard deviation for innovations in the trend term.

`ADelta`

  Scale for the prior for the standard deviation for innovations in the trend term.

`omegaDeltaAMax`

  Maximum for the standard deviation for innovations in the trend term.

`nSeason`

  Number of seasons.

`nuSeason`

  Degrees of freedom for the prior for the standard deviation for innovations in the season term.

`ASeason`

  Scale for the prior for the standard deviation for innovations in the season term.

`omegaSeasonAMax`

  Maximum for the standard deviation for innovations in the season term.

`formula`

  A `formula` with response `mean`.

`data`

  A data.frame containing covariate data.

`contrastsArg`

  A named list, the elements which are matrices or names of contrasts functions.

`AIntercept`

  The standard deviation for the prior for the intercept in the covariates term.

`nuEtaCoef`

  The degrees of freedom for the prior for the coefficients in the covariates term.

`AEtaCoef`

  The scale for the prior for the coefficients in the covariates term.

`nuBeta`

  The degrees of freedom for the error term, if the error term has a t distribution.

`nuTau`

  The degrees of freedom for the truncated half-t prior for the standard deviation or scale parameter for the error term.

`ATau`

  The scale for the truncated half-t prior for the standard deviation or scale parameter for the error term.

`maxTau`

  The maximum value for the standard deviation or scale parameter for the error term.

`shape1Phi`

  First 'sample size' parameter in beta prior for damping parameter.

`shape2Phi`

  Second 'sample size' parameter in beta prior for damping parameter.

### Warning

In normal usage, it should not be necessary to access, or even know about, the slots of a `SpecDLM` object. The slots are not part of the API of the package, and may change in future.

### See Also

Object of class `SpecDLM` are generated using function `DLM`.

[Package demest version 0.0.0.3.1 Index]