Data stacking for advanced INLA models

Functions for combining data, effects and observation matrices into inla.stack objects, and extracting information from such objects.

inla.stack.remove.unused(stack)

inla.stack.compress(stack, remove.unused = TRUE)

inla.stack(..., compress = TRUE, remove.unused = TRUE)

inla.stack.sum(
  data,
  A,
  effects,
  tag = "",
  compress = TRUE,
  remove.unused = TRUE
)

inla.stack.join(..., compress = TRUE, remove.unused = TRUE)

inla.stack.index(stack, tag)

inla.stack.LHS(stack)

inla.stack.RHS(stack)

inla.stack.data(stack, ...)

inla.stack.A(stack)

Arguments

stack: A inla.data.stack object, created by a call to inla.stack, inla.stack.sum, or inla.stack.join.
remove.unused: If TRUE, compress the model by removing rows of effects corresponding to all-zero columns in the A matrix (and removing those columns).
...: For inla.stack.join, two or more data stacks of class inla.data.stack, created by a call to inla.stack, inla.stack.sum, or inla.stack.join. For inla.stack.data, a list of variables to be joined with the data list.
compress: If TRUE, compress the model by removing duplicated rows of effects, replacing the corresponding A-matrix columns with a single column containing the sum.
data: A list or codedata.frame of named data vectors. Scalars are expanded to match the number of rows in the A matrices, or any non-scalar data vectors. An error is given if the input is inconsistent.
A: A list of observation matrices. Scalars are expanded to diagonal matrices matching the effect vector lengths. An error is given if the input is inconsistent or ambiguous.
effects: A collection of effects/predictors. Each list element corresponds to an observation matrix, and must either be a single vector, a list of vectors, or a data.frame. Single-element effect vectors are expanded to vectors matching the number of columns in the corresponding A matrix. An error is given if the input is inconsistent or ombiguous.
tag: A string specifying a tag for later identification.

Value

A data stack of class inla.data.stack. Elements:

data $=(y^1, \ldots, y^p, \tilde{x}^1, \ldots, \tilde{x}^{n_k})$
A $=\tilde{A}$
data.names List of data names, length $p$
effect.names List of effect names, length $n_k$
n.data Data length, $n_i$
index List indexed by tags, each element indexing into $i=1, \ldots, n_i$

Details

For models with a single effects collection, the outer list container for A and effects may be omitted.

Component size definitions:

[$n_l$] effect blocks
[$n_k$] effects
[$n_i$] data values
[$n_{j,l}$] effect size for block $l$
[$n_j$] $= \sum_{l=1}^{n_l} n_{j,l}$ total effect size

Input:

[data] $(y^1, \ldots, y^p)$ $p$ vectors, each of length $n_i$
[A] $(A^1, \ldots, A^{n_l})$ matrices of size $n_i \times n_{j,l}$
[effects] $\left((x^{1,1},\ldots,x^{n_k,1}), \ldots, (x^{1,n_l},\ldots,x^{n_k,n_l})\right)$ collections of effect vectors of length $n_{j,l}$

$$ \mbox{predictor}(y^1, \ldots, y^p) \sim \sum_{l=1}^{n_l} A^l \sum_{k=1}^{n_k} g(k, x^{k,l}) = \tilde{A} \sum_{k=1}^{n_k} g(k, \tilde{x}^k) $$ where $$ \tilde{A} = \mbox{cbind}\left( A^1, \ldots, A^{n_l} \right) $$ $$ \tilde{x}^k = \mbox{rbind}\left( x^{k,1}, \ldots, x^{k,n_l} \right) $$ and for each block $l$, any missing $x^{k,l}$ is replaced by an NA vector.

Functions

inla.stack.remove.unused(): Remove unused entries from an existing stack
inla.stack.compress(): Compress an existing stack by removing duplicates
inla.stack(): Shorthand for inla.stack.join and inla.stack.sum
inla.stack.sum(): Create data stack as a sum of predictors
inla.stack.join(): Join two or more data stacks
inla.stack.index(): Extract tagged indices
inla.stack.LHS(): Extract data associated with the "left hand side" of the model (e.g. the data itself, Ntrials, link, E)
inla.stack.RHS(): Extract data associated with the "right hand side" of the model (all the covariates/predictors)
inla.stack.data(): Extract data for an inla call, and optionally join with other variables
inla.stack.A(): Extract the "A matrix" for control.predictor

Examples