sim_x: sim_x

In bliss: Bayesian Functional Linear Regression with Sparse Step Functions

sim_x

Description

Simulate functional covariate observations.

Usage

sim_x(param)

Arguments

param

a list containing : grid a numerical vector, the observation times. n an integer, the sample size. p an integer, the number of observation times. diagVar a numerical vector (optional), the diagonal of the autocorrelation matrix. dim a numerical value (optional), the dimension of the Fourier basis, if "shape" is "Fourier" or "Fourier2". ksi a numerical value (optional) related to the observations correlation. x_shape a character vector (optional), the shape of the observations.

Details

Several shape are available for the observations: "Fourier", "Fourier2", "random_walk", "random_sharp", "uniform", "gaussian", "mvgauss", "mvgauss_different_scale", "mvgauss_different_scale2", "mvgauss_different_scale3" and "mvgauss_different_scale4".

Value

a matrix which contains the functional covariate observations at time points given by grid.

Examples

library(RColorBrewer)
### uniform
param <- list(n=15,p=100,grid=seq(0,1,length=100),x_type="uniform")
x <- sim_x(param)
cols <- colorRampPalette(brewer.pal(9,"YlOrRd"))(15)
matplot(param$grid,t(x),type="l",lty=1,col=cols)

bliss documentation built on May 29, 2024, 8:35 a.m.