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
)
```

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`

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

See source code for details.