LaMa (version 3.1.0)

smooth_dens_construct: Build the design and penalty matrices for smooth density estimation

Description

smooth_dens_construct() is deprecated, use smoothdens instead. It returns the same objects, but ordered by component first and data stream second.

Usage

smooth_dens_construct(
  data,
  par,
  type = "real",
  k = 25,
  knots = NULL,
  degree = 3,
  diff_order = 2
)

Value

a nested list containing the design matrices Z, the penalty matrices S, the initial coefficients coef the prediction design matrices Z_predict, the prediction grids xseq, and details for the basis expansion for each data stream.

Arguments

data

named data frame of 1 or multiple data streams

par

list of initial means and sds ("real", "positive") or means and concentrations ("circular", called kappa or concentration), one value per state. For several data streams, a named list of such lists.

type

type of each data stream, either "real" for data on the reals, "positive" for data on the positive reals or "circular" for angular data.

k

number of basis functions for each data stream

knots

optional vector of knots (including the boundary knots) to be used for basis construction, for several data streams a named list of such vectors. If not provided, the knots are placed equidistantly for "real" and "circular" and using polynomial spacing for "positive".

For "real" and "positive" k - degree + 1 knots are needed, for "circular" k + 1 knots are needed.

degree

degree of the B-spline basis functions for each data stream, defaults to cubic B-splines

diff_order

order of differencing used for the P-Spline penalty matrix for each data stream. Defaults to second-order differences.

Examples

data = data.frame(x = rnorm(100))
# deprecated:
# SmoothDens = smooth_dens_construct(data, par = list(x = list(mean = 0, sd = 1)))
# use instead:
dens = smoothdens(data$x, par = list(mean = 0, sd = 1))