Generic methods for bru_mapper objects

A bru_mapper sub-class implementation must provide an ibm_jacobian() method. If the model size 'n' and definition values 'values' are stored in the object itself, default methods are available (see Details). Otherwise the ibm_n() and ibm_values() methods also need to be provided.

Usage

ibm_n(mapper, inla_f = FALSE, ...)

ibm_n_output(mapper, input, state = NULL, inla_f = FALSE, ...)

ibm_values(mapper, inla_f = FALSE, ...)

ibm_is_linear(mapper, ...)

ibm_jacobian(mapper, input, state = NULL, inla_f = FALSE, ...)

ibm_linear(mapper, input, state = NULL, ...)

ibm_simplify(mapper, input = NULL, state = NULL, ...)

ibm_eval(mapper, input, state = NULL, ...)

ibm_eval2(mapper, input, state = NULL, ...)

ibm_names(mapper)

ibm_names(mapper) <- value

ibm_inla_subset(mapper, ...)

ibm_invalid_output(mapper, input, state, ...)

# Default S3 method
ibm_n(mapper, inla_f = FALSE, ...)

# Default S3 method
ibm_n_output(mapper, input, state = NULL, inla_f = FALSE, ...)

# Default S3 method
ibm_values(mapper, inla_f = FALSE, ...)

# Default S3 method
ibm_is_linear(mapper, ...)

# Default S3 method
ibm_jacobian(mapper, input, state = NULL, ...)

# Default S3 method
ibm_linear(mapper, input, state, ...)

# Default S3 method
ibm_simplify(mapper, input = NULL, state = NULL, ...)

# Default S3 method
ibm_eval(mapper, input, state = NULL, ..., jacobian = NULL)

# Default S3 method
ibm_eval2(mapper, input, state, ...)

# Default S3 method
ibm_names(mapper, ...)

# Default S3 method
ibm_inla_subset(mapper, ...)

# Default S3 method
ibm_invalid_output(mapper, input, state, ...)

Arguments

mapper: A mapper S3 object, inheriting from bru_mapper.
inla_f: logical; when TRUE for ibm_n() and ibm_values(), the result must be compatible with the INLA::f(...) and corresponding INLA::inla.stack(...) constructions. For ibm_{eval,jacobian,linear}, the input interpretation may be different. Implementations do not normally need to do anything different, except for mappers of the type needed for hidden multicomponent models such as "bym2", which can be handled by bm_collect.
...: Arguments passed on to other methods
input: Data input for the mapper.
state: A vector of latent state values for the mapping, of length ibm_n(mapper, inla_f = FALSE)
value: a character vector of the same length as the number of sub-mappers in the mapper
jacobian: For ibm_eval() methods, an optional pre-computed Jacobian, typically supplied by internal methods that already have the Jacobian.

Functions

ibm_n(): Implementations must return the size of the latent vector being mapped to.
ibm_n_output(): Implementations must return an integer denoting the mapper output length. The default implementation returns NROW(input). Mappers such as bm_multi and bm_collect, that can accept list() inputs require their own methods implementations.
ibm_values(): When inla_f=TRUE, implementations must return a vector that would be interpretable by an INLA::f(..., values = ...) specification. The exception is the method for bm_multi, that returns a multi-column data frame if multi=TRUE.
ibm_is_linear(): Implementations must return TRUE or FALSE. If TRUE (returned by the default method unless the mapper contains an is_linear variable), users of the mapper may assume the mapper is linear.
ibm_jacobian(): Implementations must return a (sparse) matrix of size ibm_n_output(mapper, input, inla_f) by ibm_n(mapper, inla_f = FALSE). The inla_f=TRUE argument should only affect the allowed type of input format.
ibm_linear(): Implementations must return a bm_taylor object The linearisation information includes offset, jacobian, and state0. The state information indicates for which state the offset was evaluated, with NULL meaning all-zero. The linearised mapper output is defined as
```
effect(input, state) =
  offset(input, state0) + jacobian(input, state0) %*% (state - state0)
```
The default method calls ibm_eval() and ibm_jacobian() to generate the needed information.
ibm_simplify(): Implementations must return a bru_mapper object. The default method returns ibm_linear(...) for linear mappers, and the original mapper for non-linear mappers.
ibm_eval(): Implementations must return a vector of length ibm_n_output(...). The input contents must be in a format accepted by ibm_jacobian(...) for the mapper.
ibm_eval2(): Implementations must return a list with elements offset and jacobian. The input contents must be in a format accepted by ibm_jacobian(...) for the mapper.
ibm_names(): Implementations must return a character vector of sub-mapper names, or NULL. Intended for providing information about multi-mappers and mapper collections.
ibm_names(mapper) <- value: Set mapper names.
ibm_inla_subset(): Implementations must return a logical vector of TRUE/FALSE for the subset such that, given the full A matrix and values output, A[, subset, drop = FALSE] and values[subset] (or values[subset, , drop = FALSE] for data.frame values) are equal to the inla_f = TRUE version of A and values. The default method uses the ibm_values output to construct the subset indexing.
ibm_invalid_output(): Implementations should return a logical vector of length ibm_n_output(mapper, input, state, ...) indicating which, if any, output elements of ibm_eval(mapper, input, state, ...) are known to be invalid. For for multi/collect mappers, a list, when given a multi=TRUE argument.
ibm_n(default): Returns a non-null element 'n' from the mapper object, and gives an error if it doesn't exist. If inla_f=TRUE, first checks for a 'n_inla' element.
ibm_n_output(default): Returns NROW(input)
ibm_values(default): Returns a non-null element 'values' from the mapper object, and seq_len(ibm_n(mapper)) if it doesn't exist.
ibm_is_linear(default): Returns logical is_linear from the mapper object if it exists, and otherwise TRUE.
ibm_jacobian(default): Mapper classes must implement their own ibm_jacobian method.
ibm_linear(default): Calls ibm_eval() and ibm_jacobian() and returns a bm_taylor object. The state0 information in the affine mapper indicates for which state the offset was evaluated; The affine mapper output is defined as
```
effect(input, state) =
  offset(input, state0) + jacobian(input, state0) %*% (state - state0)
```
ibm_simplify(default): Calls ibm_linear() for linear mappers, and returns the original mapper for non-linear mappers.
ibm_eval(default): Verifies that the mapper is linear with ibm_is_linear(), and then computes a linear mapping as ibm_jacobian(...) %*% state. When state is NULL, a zero vector of length ibm_n_output(...) is returned.
ibm_eval2(default): Calls jacobian <- ibm_jacobian(...) and offset <- ibm_eval(..., jacobian = jacobian) and returns a list with elements offset and jacobian, as needed by ibm_linear.default() and similar methods. Mapper classes can implement their own ibm_eval2 method if joint construction of evaluation and Jacobian is more efficient than separate or sequential construction.
ibm_names(default): Returns NULL
ibm_inla_subset(default): Uses the ibm_values output to construct the inla subset indexing, passing extra arguments such as multi on to the methods (this means it supports both regular vector values and multi=1 data.frame values).
ibm_invalid_output(default): Returns an all-FALSE logical vector.

Examples

# ibm_names
mapper <- bm_multi(list(A = bm_index(2), B = bm_index(2)))
ibm_names(mapper)
#> [1] "A" "B"
ibm_names(mapper) <- c("new", "names")
ibm_names(mapper)
#> [1] "new"   "names"

Usage

Arguments

Functions

See also

Examples