Description Usage Arguments Details Value Author(s) References Examples
Creates a standard covariance model (cmodStd)
object for geostatistical data. This function will be
deprecated in the future. Please update your code to use
the cmod_std function, which also
allows the user to specify geometric anisotropy.
1 | cmod.std(model, psill, r, evar = 0, fvar = 0, par3 = 0.5)
|
model |
A covariance model (e.g.,
|
psill |
The partial sill of the model. Must be a positive number. |
r |
The range parameter |
evar |
The variance of the errors. Must be non-negative number. The default is 0. |
fvar |
The finescale variance (microscale error). Must be a non-negative number. The default is 0. |
par3 |
The value of the third parameter for 3 parameter models. Must be a positive number. The default is 0.5. |
The general form of the specified covariance function is
psill * ρ(d; r) +
(evar + fvar)*(d==0), where
ρ is the covariance function of the parametric
models.
For the exponential model, ρ(d;
r) is exp(-d/r).
For the gaussian model, ρ(d;
r) is exp(-d^2/r^2).
For the matern model, ρ(d;
r) is
2^(1-par3)/gamma(par3)*sd^par3*besselK(sd,
nu = par3), where sd = d/r.
For the amatern (alternative Matern) model,
ρ(d; r) is
2^(1-par3)/gamma(par3)*sd^par3*besselK(sd, nu =
par3), where sd = 2 * sqrt(par3) * d/r.
For the spherical model, ρ(d;
r) is 1 - 1.5*sd + 0.5*(sd)^3 if d <
r, and 0 otherwise, with sd = d/r.
For the wendland1 model, ρ(d;
r) is (1 - sd)^4 * (4*sd + 1) if d <
r, and 0 otherwise, with sd = d/r.
For the wendland2 model, ρ(d;
r) is (1 - sd)^6 * (35*sd^2 + 18*sd + 3))/3
if d < r, and 0 otherwise, with sd = d/r.
For the wu1 model, ρ(d; r)
is (1 - sd)^3 * (1 + 3*sd + sd^2) if d < r,
and 0 otherwise, with sd = d/r.
For the wu2 model, ρ(d; r)
is (1 - sd)^4*(4 + 16*sd + 12*sd^2 + 3*sd^3))/4 if
d < r, and 0 otherwise, with sd = d/r.
For the wu3 model, ρ(d; r)
is (1 - sd)^6 * (1 + 6*sd + 41/3*sd^2 + 12*sd^3 +
5*sd^4 + 5/6*sd^5) if d < r, and 0 otherwise,
with sd = d/r.
Returns a cmodStd object.
Joshua French
Waller, L. A., & Gotway, C. A. (2004). Applied Spatial Statistics for Public Health Data. John Wiley & Sons.
1 | cmod.std(model = "exponential", psill = 1, r = 1)
|
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