sim_sfda_data: generates data from a family of curves

In dan410/sfdasim: Simulates spatially dependent functional curves and data

Description

generates a functional data set by evaluating curves and adding Gaussian noise.

Usage

sim_sfda_data(locs, coef, basis.fns, sigma0, m = NULL, pts = NULL,
  DIST = runif, ...)

Arguments

locs	N by 2 matrix of spatial locations
coef	matrix or data frame containing the coefficients of the basis functions. The number of columns is equal to the number of basis functions
basis.fns	basis functions given by the output of create_basis
sigma0	standard deviation of the noise term.
m	number of observations per curve
pts	vector of values where the curves will be evaluated
DIST	distribution function used to generate observatin locations for each curve. Default is runif().
...	additional parameter passed to dist function

Value

ID integer identifying a single curve

locs spatial location of each curve

Time argument values where curves are evaluated

X response variable

Examples

curves <- sim_sfda_curves(nBasis = 2, cov.model = c("gaussian", "exponential"), cov.pars = rbind(c(1, 0.5), c(1, .3)), type="Cos", basis.pars = 2, locs = expand.grid(1:5/5, 1:10/10))
plot_curves(curves$coef, curves$basis.fns, ylim=c(-3,3))

sim.data <- sim_sfda_data(curves$locs, curves$coef, curves$basis.fns, sigma0=0.4, m = 10)
## plot_data(sim.data)

dan410/sfdasim documentation built on May 14, 2019, 3:34 p.m.