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Define the B-splines Used for the Time-varying Coefficients of the Model.

Source: R/splines.R
splines.Rd

This function can be used as part of dynamiteformula() to define the splines used for the time-varying coefficients \(\delta\).

Usage

splines(
  df = NULL,
  degree = 3L,
  lb_tau = 0,
  noncentered = FALSE,
  override = FALSE
)

Arguments

df

[integer(1)]
Degrees of freedom, i.e., the total number of spline coefficients. See splines::bs(). Note that the knots are always defined as an equidistant sequence on the interval starting from the first non-fixed time point to the last time point in the data. See dynamiteformula() for more information on fixed time points. Should be an (unrestricted) positive integer.

degree

[integer(1)]
See splines::bs(). Should be an (unrestricted) positive integer.

lb_tau

[numeric()]
Hard constraint(s) on the lower bound of the standard deviation parameters \(\tau\) of the random walk priors. Can be useful in avoiding divergences in some cases. See also the noncentered argument. Can be a single positive value, or vector defining the lower bound separately for each channel, even for channels without varying effects. The ordering is based on the order of channel definitions in the dynamiteformula object.

noncentered

[logical()]
If TRUE, use a noncentered parameterization for the spline coefficients. Default is FALSE. Try changing this if you encounter divergences or other problems in sampling for example when simulating from prior predictive distribution. Can be a single logical value, or vector of logical values, defining the parameterization separately for each channel, even for channels without varying effects.

override

[logical(1)]
If FALSE (the default), an existing definition for the splines will not be overridden by another call to splines(). If TRUE, any existing definitions will be replaced.

Value

An object of class splines.

See also

Model formula construction dynamite(), dynamiteformula(), lags(), lfactor(), random_spec()

Examples

data.table::setDTthreads(1) # For CRAN
# Two channel model with varying effects, with explicit lower bounds for the
# random walk prior standard deviations, with noncentered parameterization
# for the first channel and centered for the second channel.
obs(y ~ 1, family = "gaussian") + obs(x ~ 1, family = "gaussian") +
  lags(type = "varying") +
  splines(
    df = 20, degree = 3, lb_tau = c(0, 0.1),
    noncentered = c(TRUE, FALSE)
  )
#>   Family   Formula
#> y gaussian y ~ 1  
#> x gaussian x ~ 1  
#> 
#> Lagged responses added as varying predictors with: k = 1