Description Usage Arguments Value Examples
For the specified basis functions, spatial dependence is
incorporated through the covariance structure of the
spatial fields connected to the coefficients. The
coefficients of the first basis funciton is a simulated
Gaussian random field with covariance model equal to the
first specified covariance model and parameters equal to
the first specified covariance parameters. The same is true
for the remaining basis functions, which means that the
number of basis functions should be equal to the number of
covariance models specified, and the order in which they
are specified is matters. The Gaussian random fields are
simulated with the grf
function in the geoR
package. See geoR
documentation for specification of
covariance models.
1 2 3 |
nBasis |
number of basis functions |
cov.model |
character vector specifying covariance function(s). The length of this vector should equal the number of basis functions. |
cov.pars |
parameters for the covariance function(s) specified in cov.model |
... |
additional arguments sent to |
type |
character specifying the type of basis
functions to use. Default is |
basis.pars |
extra parameters used in the construction of basis functions |
grid.dim |
vector with 2 numbers specifying the
dimension of the rectangular grid. Only used if
|
grid.xlim |
vector of length 2 specifying the xlim
for the grid. Only used if |
grid.ylim |
vector of length 2 specifying the ylim
for the grid. Only used if |
locs |
nx2 matrix of locations. If NULL, then grid values are used to create locations |
write |
logical. If |
file |
character specifying the file name. Only used
if |
locs matrix containing spatial locations of the curves
coef matrix containing coefficients of the basis functions. The number of columns is equal to the number of basis functions
basis.fns list of basis functions created by
create_basis
1 2 3 4 5 | 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)
|
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