R: An internal function, allowing back-compatibility, which...

Formula.translate.compat {R2MLwiN}

R Documentation

An internal function, allowing back-compatibility, which translates a model formula from a formula object or character string into an R list object.

Description

Supports Formula syntax as used in earlier (<0.8-0) versions of R2MLwiN. A model formula, as a formula object (or a character string) is translated into an R list object. Called by runMLwiN if oldsyntax = TRUE (when user specifies levID not NULL in runMLwiN function call). For corresponding function supporting new syntax, see Formula.translate.

Usage

Formula.translate.compat(Formula, levID, D = "Normal", indata)

Arguments

`Formula`	A formula object (or a character string) specifying a multilevel model. See `Value` for details.
`levID`	A character (vector) specifying the level ID(s).
`D`	A character string/vector specifying the distribution to be modelled, which can include `'Normal'` (the default), `'Binomial'`, `'Poisson'`, `'Negbinom'`, `'Unordered Multinomial'`, `'Ordered Multinomial'`, `'Multivariate Normal'`, or `'Mixed'`.
`indata`	A data.frame object containing the data to be modelled.

Details

If Formula is a character string, then the following syntax applies:

~ A tilde is used to separate response variable(s) and explanatory variable(s).
() Round brackets are used to specify each random variable in the model together with its fixed/random part information.
| Separates explanatory variable(s) (placed to the right of |) from the fixed/random part information (placed to the left of |) when placed within ().
[] When placed immediately after an explanatory variable, indicates that the variable is categorical. The string in the [] represents the reference category; if empty, no reference category is used; See note.
: Indicates an interaction term: i.e. the variables adjacent to :, and separated by it, are interacted with each other.
0 When placed to the left of | within () indicates that the variables to the right of | within the same () are to be added to the fixed part of the model.
1 When placed to the left of | within () indicates that the coefficients of the variables placed to the right of | within the same () are to be allowed to randomly vary at level 1 (and so on for 2 for level 2, 3 for level 3, etc.)
0s/0c When placed to the left of | within () indicates that separate (hence s) / common (hence c) coefficients for the variables to the right of | within the same () are to be added to the fixed part (hence 0) of multivariate normal, multinomial and mixed responses models.
2s/2c When placed to the left of | within () indicates that separate (hence s) / common (hence c) coefficients for the variables to the right of | within the same () are to be added to the random part of the model, and allowed to vary at level 2; applies to multivariate normal, multinomial and mixed responses models only.
{} gives a vector of binary indicators specifying a common coefficient. 1 is to include the component at the corresponding positions; zero otherwise. These digits are separated by commas; applies to multivariate normal, multinomial and mixed responses models only.
. Used for adding a separate coefficient for a particular component at a specific level; applies to multivariate normal, multinomial and mixed responses models only

If Formula is a formula object, 0s/0c, 2s/2c, .... and {} have to be replaced by `0s`/`0c`, `2s`/`2c`, .... and () respectively. Other syntax remains the same.

Value

Outputs an R list object, which is then used as the input for write.IGLS and/or write.MCMC.

Note

Note that some characters listed above have special meanings in the formula, so avoid using them when you name the random variable. Alphanumeric characters (i.e. [:alnum:]) are recommended for naming the random variable. They are also recommended for naming a reference category, inside []. Note: use [] notation only in the fixed part when there is no categorical variable in the random effects. If there is one in the random part, the categorical variable has to be converted into a set of binary variables (e.g., using Untoggle).

Author(s)

Zhang, Z., Charlton, C.M.J., Parker, R.M.A., Leckie, G., and Browne, W.J. (2016) Centre for Multilevel Modelling, University of Bristol.