Description Usage Arguments Details Value Note Author(s) References See Also Examples

Fit ranges and/or sills from a simple or nested variogram model to a sample variogram

1 2 | ```
fit.variogram(object, model, fit.sills = TRUE, fit.ranges = TRUE,
fit.method = 7, debug.level = 1, warn.if.neg = FALSE, fit.kappa = FALSE)
``` |

`object` |
sample variogram, output of variogram |

`model` |
variogram model, output of vgm; see Details below
for details on how |

`fit.sills` |
logical; determines whether the partial sill coefficients (including nugget variance) should be fitted; or logical vector: determines for each partial sill parameter whether it should be fitted or fixed. |

`fit.ranges` |
logical; determines whether the range coefficients (excluding that of the nugget component) should be fitted; or logical vector: determines for each range parameter whether it should be fitted or fixed. |

`fit.method` |
fitting method, used by gstat. The default method uses
weights $N_h/h^2$ with $N_h$ the number of point pairs and $h$ the
distance. This criterion is not supported by theory, but by practice.
For other values of |

`debug.level` |
integer; set gstat internal debug level |

`warn.if.neg` |
logical; if TRUE a warning is issued whenever a sill value of a direct variogram becomes negative |

`fit.kappa` |
logical; if |

If any of the initial parameters of `model`

are `NA`

,
they are given default values as follows. The range parameter
is given one third of the maximum value of `object$dist`

.
The nugget value is given the mean value of the first three values
of `object$gamma`

. The partial sill is given the mean of the
last five values of `object$gamma`

.

Values for `fit.method`

are 1: weights equal to
$N_j$; 2: weights equal to $N_j/((gamma(h_j))^2)$; 5 (ignore, use
fit.variogram.reml); 6: unweighted (OLS); 7: $N_j/(h_j^2)$.
(from: http://www.gstat.org/gstat.pdf, table 4.2).

returns a fitted variogram model (of class `variogramModel`

).

This is a `data.frame`

with two attributes: (i) `singular`

a logical attribute that indicates whether the non-linear fit
converged (FALSE), or ended in a singularity (TRUE), and (ii)
`SSErr`

a numerical attribute with the (weighted) sum of
squared errors of the fitted model. See Notes below.

If fitting the range(s) is part of the job of this function,
the results may well depend on the starting values, given in
argument `model`

, which is generally the case for non-linear
regression problems. This function uses internal C code, which
uses Levenberg-Marquardt.

If for a direct (i.e. not a cross) variogram a sill parameter (partial sill or nugget) becomes negative, fit.variogram is called again with this parameter set to zero, and with a FALSE flag to further fit this sill. This implies that the search does not move away from search space boundaries.

On singular model fits: If your variogram turns out to be a flat, horizontal or sloping line, then fitting a three parameter model such as the exponential or spherical with nugget is a bit heavy: there's an infinite number of possible combinations of sill and range (both very large) to fit to a sloping line. In this case, the returned, singular model may still be useful: just try and plot it. Gstat converges when the parameter values stabilize, and this may not be the case. Another case of singular model fits happens when a model that reaches the sill (such as the spherical) is fit with a nugget, and the range parameter starts, or converges to a value smaller than the distance of the second sample variogram estimate. In this case, again, an infinite number of possibilities occur essentially for fitting a line through a single (first sample variogram) point. In both cases, fixing one or more of the variogram model parameters may help you out.

Edzer Pebesma

Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers \& Geosciences, 30: 683-691.

variogram, vgm

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
library(sp)
data(meuse)
coordinates(meuse) = ~x+y
vgm1 <- variogram(log(zinc)~1, meuse)
fit.variogram(vgm1, vgm(1, "Sph", 300, 1))
fit.variogram(vgm1, vgm("Sph"))
# optimize the value of kappa in a Matern model, using ugly <<- side effect:
f = function(x) attr(m.fit <<- fit.variogram(vgm1, vgm(,"Mat",nugget=NA,kappa=x)),"SSErr")
optimize(f, c(0.1, 5))
plot(vgm1, m.fit)
# best fit from the (0.3, 0.4, 0.5. ... , 5) sequence:
(m <- fit.variogram(vgm1, vgm("Mat"), fit.kappa = TRUE))
attr(m, "SSErr")
``` |

gstat documentation built on April 2, 2018, 5:04 p.m.

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