Simulates a dataset with two functional covariates, four subject-level scalar covariates, and a binary outcome.
generate.data.for.demonstration(
nsub = 400,
b0.true = -5,
b1.true = 0,
b2.true = +1,
b3.true = -1,
b4.true = +1,
nobs = 500,
observe.rate = 0.1
)The number of subjects in the simulated dataset.
The true value of the intercept.
The true value of the first covariate.
The true value of the second covariate.
The true value of the third covariate.
The true value of the fourth covariate.
The total number of possible observation times.
The average proportion of those possible times at which any given subject is observed.
Returns a data.frame representing nobs
measurements for each subject. The rows of this data.frame
tell the values of two time-varying covariates on a dense grid
of nobs observation times. It also contains an
id variable, four subject-level covariates
(s1, ..., s4) and one subject-level
response (y), which are replicated for each observation.
For each observation, there is also its observation
time time, there are both the smooth latent value of the covariates
(true.x1 and true.x2) and
versions observed with error (x1 and x2), and there are
also the local values of the functional regression coefficients
(true.betafn1 and true.betafn2). Lastly,
each row has a random value for include.in.subsample,
telling whether it should be considered as an observed data
point (versus an unobserved moment in the simulated subject's life).
include.in.subsample is simply generated as a Bernoulli random variable with
success probability observe.rate.