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.
pffrSim(
scenario = "all",
n = 100,
nxgrid = 40,
nygrid = 60,
SNR = 10,
propmissing = 0,
limits = NULL
)a named list with the simulated data, and the true components of the predictor etc as attributes.
see Description
number of observations
number of evaluation points of functional covariates
number of evaluation points of the functional response
the signal-to-noise ratio for the generated data: empirical variance of the additive predictor divided by variance of the errors.
proportion of missing data in the response, default = 0. See Details.
a function that defines an integration range, see
ff
See source code for details.