Description Usage Arguments Details Value
Given an appropriate DL model formula and data-frame, prepares the data to be fit by interpreting the formula and extracting available smooth lag terms.
1 2 | interpret.dlm(formula, data, .names.func = function(n) paste("pseudoGroups",
n, sep = ""))
|
formula |
a symbolic description of the model to be fitted.
See |
data |
a model-frame containing the data for each term in the model. Should already be appropriately subset, etc. |
.names.func |
a function for creating names of dummy variables that act as placeholders for penalized spline terms in lme4's setup. There should not be a need to alter this in normal use cases |
Users should not typically have to interact with interpret.dlm
directly, but it may be useful for extensions.
Uses R
's stats::model.matrix
mechanisms to build
and parse the random effects (or penalized) components of spline-lag terms
in the model. The object returned is later passed to other dlmBE
functions in order to fit the specified model.
an S3 object of class "parsed.dlm"
with list elements:
formula
the formula passed to interpret.dlm
lme4.formula
a reconstructed formula that is then passed
to the lme4 modular
functions
model
a data.frame
returned by call to
stats::model.frame
; a copy of the fixed
effects data needed to fit the model
Bt
a matrix of the random or penalized lag basis vectors,
where each vector is a row. Stored as an object that inherits from
Matrix::dMatrix
bases
a list of all the unique bases represented in the
formula
. This may be <= the number of separate
spline-lag terms. All elements should inherit from
SmoothLag
lag.group
an integer vector returned by
parse.names
where each unique integer corresponds to a
separate spline-lag term. For lag term i
, lag.group == i
indexes the rows of Bt
that correspond to the set of random or
penalized basis vectors for that term
bi
for "basis index." Each set of lag terms indexed in
lag.group
has a matching basis decomposition in bases
.
bi
keeps track of that matching
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