refund (version 0.1-23)

# pffrSim: 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

## Value

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

## Details

See source code for details.