Simulate example data for pffr
Simulates example data for pffr from a variety of terms.
Scenario "all" generates data from a complex multivariate model
Y_i(t) = μ(t) + \int X_{1i}(s)β_1(s,t)ds + xlin β_3(t) + f(xte1, xte2) + f(xsmoo, t) + β_4 xconst + f(xfactor, t) + ε_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 )
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
|
See source code for details.
a named list with the simulated data, and the true components of the predictor etc as attributes.
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