Linear and Non-linear formulas in brms

Helper functions to specify linear and non-linear formulas for use with brmsformula.

Usage

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)

Arguments

formula

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.

flist

Optional list of formulas, which are treated in the same way as formulas passed via the ... argument.

dpar

Optional character string specifying the distributional parameter to which the formulas passed via ... and flist belong.

resp

Optional character string specifying the response variable to which the formulas passed via ... and flist belong. Only relevant in multivariate models.

loop

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.

center

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.

cmc

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.

sparse

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.

decomp

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.

autocor

A one sided formula containing autocorrelation terms. All none autocorrelation terms in autocor will be silently ignored.

nl

Logical; Indicates whether formula should be treated as specifying a non-linear model. By default, formula is treated as an ordinary linear model formula.

rescor

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.

mecor

Logical; Indicates if correlations between latent variables defined by me terms should be modeled. Defaults to TRUE.

Value

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.

See also

brmsformula, mvbrmsformula

Examples

# 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)