pffrSim: Simulate example data for pffr

In refund: Regression with Functional Data

Simulate example data for pffr

Description

Simulates example data for pffr from a variety of terms. Scenario "all" generates data from a complex multivariate model

Y_i(t) = \mu(t) + \int X_{1i}(s)\beta_1(s,t)ds + xlin \beta_3(t) + f(xte1, xte2) + f(xsmoo, t) + \beta_4 xconst + f(xfactor, t) + \epsilon_i(t)

. Scenarios "int", "ff", "lin", "te", "smoo", "const", "factor", generate data from simpler models containing only the respective term(s) in the model equation given above. Specifying a vector-valued scenario will generate data from a combination of the respective terms. Sparse/irregular response trajectories can be generated by setting propmissing to something greater than 0 (and smaller than 1). The return object then also includes a ydata-item with the sparsified data.

Usage

pffrSim(
  scenario = "all",
  n = 100,
  nxgrid = 40,
  nygrid = 60,
  SNR = 10,
  propmissing = 0,
  limits = NULL
)

Arguments

scenario	see Description
n	number of observations
nxgrid	number of evaluation points of functional covariates
nygrid	number of evaluation points of the functional response
SNR	the signal-to-noise ratio for the generated data: empirical variance of the additive predictor divided by variance of the errors.
propmissing	proportion of missing data in the response, default = 0. See Details.
limits	a function that defines an integration range, see ff

Details

See source code for details.

Value

a named list with the simulated data, and the true components of the predictor etc as attributes.

refund documentation built on Sept. 21, 2024, 1:07 a.m.