Function to incorporate information on the low-r behaviour of G(r) into the Bayesian model.
set.Gr(data, r1=seq(0, 1, 0.005), r2=NA, rho.0,
type1="gaussianNoise", type2=NA, sigma.f=NA, l=NA)an object of type data. See set.data for details.
numeric vectors, specify grids on which the G(r) behaviour is controlled.
numeric, atomic number density of the material: a number of atoms per unit cell divided by a volume of the unit cell.
characters, specify the way to control the behavior of G(r). See details.
numerics or numeric vectors, specify parameters for a squared-exponential covariance function.
An object of type data.
type1 can be either "gaussianNoise" or "correlatedNoise". G(r) is restricted to the \(-4\pi\rho.0r1\) line plus independent Gaussian noise or correlated Gaussian noise, respectively.
type2 can be either "secondDeriv" or "gaussianProcess" to impose smoothness conditions over the interval r2. If type2 is "secondDeriv", a minimum of the second derivative is sought. If type2 is "gaussianProcess", the smoothness is controlled via the Gaussian process using parameters sigma.f and l.
According to our experience, the most efficient way is to impose type1="gaussianNoise" and type2=NA conditions.