Helper functions to specify linear and non-linear
formulas for use with brmsformula.
nlf(formula, ..., flist = NULL, dpar = NULL, resp = NULL, loop = NULL)
lf(
...,
flist = NULL,
dpar = NULL,
resp = NULL,
center = NULL,
cmc = NULL,
sparse = NULL,
decomp = NULL
)
acformula(autocor, resp = NULL)
set_nl(nl = TRUE, dpar = NULL, resp = NULL)
set_rescor(rescor = TRUE)
set_mecor(mecor = TRUE)Non-linear formula for a distributional parameter.
The name of the distributional parameter can either be specified
on the left-hand side of formula or via argument dpar.
Additional formula objects to specify predictors of
non-linear and distributional parameters. Formulas can either be named
directly or contain names on their left-hand side. Alternatively,
it is possible to fix parameters to certain values by passing
numbers or character strings in which case arguments have to be named
to provide the parameter names. See 'Details' for more information.
Optional list of formulas, which are treated in the
same way as formulas passed via the ... argument.
Optional character string specifying the distributional
parameter to which the formulas passed via ... and
flist belong.
Optional character string specifying the response
variable to which the formulas passed via ... and
flist belong. Only relevant in multivariate models.
Logical; Only used in non-linear models.
Indicates if the computation of the non-linear formula should be
done inside (TRUE) or outside (FALSE) a loop
over observations. Defaults to TRUE.
Logical; Indicates if the population-level design
matrix should be centered, which usually increases sampling efficiency.
See the 'Details' section for more information.
Defaults to TRUE for distributional parameters
and to FALSE for non-linear parameters.
Logical; Indicates whether automatic cell-mean coding
should be enabled when removing the intercept by adding 0
to the right-hand of model formulas. Defaults to TRUE to
mirror the behavior of standard R formula parsing.
Logical; indicates whether the population-level design matrices
should be treated as sparse (defaults to FALSE). For design matrices
with many zeros, this can considerably reduce required memory. Sampling
speed is currently not improved or even slightly decreased.
Optional name of the decomposition used for the
population-level design matrix. Defaults to NULL that is
no decomposition. Other options currently available are
"QR" for the QR decomposition that helps in fitting models
with highly correlated predictors.
A one sided formula containing autocorrelation
terms. All none autocorrelation terms in autocor will
be silently ignored.
Logical; Indicates whether formula should be
treated as specifying a non-linear model. By default, formula
is treated as an ordinary linear model formula.
Logical; Indicates if residual correlation between
the response variables should be modeled. Currently this is only
possible in multivariate gaussian and student models.
Only relevant in multivariate models.
Logical; Indicates if correlations between latent variables
defined by me terms should be modeled. Defaults to TRUE.
For lf and nlf a list that can be
passed to brmsformula or added
to an existing brmsformula or mvbrmsformula object.
For set_nl and set_rescor a logical value that can be
added to an existing brmsformula or mvbrmsformula object.
# add more formulas to the model
bf(y ~ 1) +
nlf(sigma ~ a * exp(b * x)) +
lf(a ~ x, b ~ z + (1|g)) +
gaussian()
#> y ~ 1
#> sigma ~ a * exp(b * x)
#> a ~ x
#> b ~ z + (1 | g)
# specify 'nl' later on
bf(y ~ a * inv_logit(x * b)) +
lf(a + b ~ z) +
set_nl(TRUE)
#> y ~ a * inv_logit(x * b)
#> a ~ z
#> b ~ z
# specify a multivariate model
bf(y1 ~ x + (1|g)) +
bf(y2 ~ z) +
set_rescor(TRUE)
#> y1 ~ x + (1 | g)
#> y2 ~ z
# add autocorrelation terms
bf(y ~ x) + acformula(~ arma(p = 1, q = 1) + car(W))
#> y ~ x
#> autocor ~ arma(p = 1, q = 1) + car(W)