SpecModel-class {demest} | R Documentation

## S4 classes to specify a model.

### Description

Classes describing all the parts of a model that can be specified wihout knowing the exact structure of, and, possibly variation in, the data.

### Details

The specification might, for instance, contain a list of main effects and interactions to be included in a hierarchical model, but not the priors for themain effects and interactions, which depend on the length and `dimtype` of the dimensions. These are not known until the relevant data, such as outcome variable `y`, are supplied, which occurs in the call to function `estimateModel`, `estimateCounts`, or `estimateAccount`.

### Slots

`call`

  The original `call` to function `Model`.

`nameY`

  The name of the outcome variable, which, in models for the data, may be the name of a dataset.

`series`

  The name of the demographic series being modelled. Used only when dealing with demographic accounts.

`varsigma`

  Data-level standard deviation, when this is supplied by the user (and treated as known.)

`nuVarsigma`

  Degrees of freedom for truncated half-t prior for data-level standard deviation.

`AVarsigma`

  Scale for truncated half-t prior for data-level standard deviation.

`varsigmaMax`

  Maximum value for data-level standard deviation.

`prob`

  In a Poisson-binomial model, the probability that a person or event is enumerated and is placed in the correct cell.

`lower`

  Lower limit for the data-level rate, probability, or mean parameter.

`upper`

  Upper limit for the data-level rate, probability, or mean parameter.

`tolerance`

  Small quantity added to `lower` or subtracted from `upper` when testing whether a proposed value for a data-level rate, probability, or mean is within the required bounds.

`maxAttempt`

  Maximum number of attempts at generating a proposal for a data-level rate, probability or mean before giving up and retaining the current value, within one iteration of the Gibbs sampler.

`scaleTheta`

  The standard deviation of the proposal density for Metropolis-Hastings updates of the data-level rate, probability, or mean parameter.

`formulaMu`

  A `formula` describing the main effects and interactions in a hierarchical model.

`specsPriors`

  A list of object of class `SpecPrior`, describing any non-default priors for main effects and interactions.

`nameSpecPriors`

  The names of the main effects or interactions that have non-default priors.

`nu`

  Degrees of freedom for TFixed model.

`nuSigma`

  Degrees of freedom for truncated half-t prior for standard deviation in prior (level 2) model.

`ASigma`

  Scale for truncated half-t prior for standard deviation in prior (level 2) model.

`sigmaMax`

  Maximum value for standard deviation in prior (level 2) model.

`aggregate`

  An object of class `SpecAggregate`.

`mean`

  Vector of means in NormalFixed or TFixed model - subsetted to include only cells that are observed in `y`.

`meanAll`

  Vector of means in NormalFixed or TFixed model, before subsetting.

`sd`

  Vector of standard deviations or scales in NormalFixed or TFixed model - subsetted to include only cells that are observed in `y`

`sdAll`

  Vector of standard deviations or scales in NormalFixed model, or TFixed model before subsetting.

`metadata`

  Metadata for `mean` and `sd`.

`metadataAll`

  Metadata for `meanAll` and `sdAll`.

`useExpose`

  Whether the model includes and exposure term.

### Warning

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

### See Also

Object of class `SpecModel` are created by a call to function `Model`.

[Package demest version 0.0.0.3.1 Index]