Description Usage Arguments Details Value Note References See Also Examples
The function estimates arbitrary parameters of a random field specification with various methods. Currently, the models to be fitted can be
Gaussian random fields
linear models
The fitting of max-stable random fields and others has not been implemented yet.
1 2 3 4 |
model,params |
\argModel
All parameters that are set to Type |
x |
\argX |
y,z |
\argYz |
T |
\argT |
grid |
\argGrid |
data |
\argData |
lower |
\argLower |
upper |
\argUpper |
methods |
\argFitmethods |
sub.methods |
\argFitsubmethods . See Details. |
users.guess |
\argUsersguess |
distances,dim |
\argDistances |
optim.control |
\argOptimcontrol |
transform |
\argTransform |
... |
\argDots |
For details on the simulation methods see
fitgauss for Gaussian random fields
fitgauss for linear models
If x
-coordinates are not given, the function will check
data
for NA
s and will perform imputing.
The function has many more options to tune the optimizer,
see RFoptions
for details.
If the model defines a Gaussian random field, the options
for methods
and submethods
are currently
"ml"
and c("self", "plain", "sqrt.nr", "sd.inv", "internal")
,
respectively.
The result depends on the logical value of
spConform
.
If TRUE
, an S4 object is created. In case the model indicates
a Gaussian random field, an
RFfit object is created.
If spConform=FALSE
, a list is returned.
In case the model indicates
a Gaussian random field, the details are given in fitgauss.
An important optional argument is boxcox
which indicates
a Box-Cox transformation; see boxcox
in RFoptions
and RFboxcox
for details.
Instead of optim
, other optimisers can be used,
see RFfitOptimiser.
Several advanced options can be found in sections ‘General
options’ and ‘fit’ of RFoptions
.
In particular, boxcox
, boxcox_lb
, boxcox_ub
allow Box-Cox transformation.
This function does not depend on the value of
RFoptions
()$PracticalRange
.
The function RFfit
always uses the standard specification
of the covariance model as given in RMmodel
.
Burnham, K. P. and Anderson, D. R. (2002) Model selection and Multi-Model Inference: A Practical Information-Theoretic Approach. 2nd edition. New York: Springer.
RFfitOptimiser
,
RFlikelihood
,
RFratiotest
,
RMmodel
,
RandomFields
,
weather
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
RFoptions(modus_operandi="sloppy")
#########################################################
## simulate some data first ##
points <- 100
x <- runif(points, 0, 3)
y <- runif(points, 0, 3) ## random points in square [0, 3]^2
model <- RMgencauchy(alpha=1, beta=2)
d <- RFsimulate(model, x=x, y=y, grid=FALSE, n=100) #1000
#########################################################
## estimation; 'NA' means: "to be estimated" ##
estmodel <- RMgencauchy(var=NA, scale=NA, alpha=NA, beta=2) +
RMtrend(mean=NA)
RFfit(estmodel, data=d)
#########################################################
## coupling alpha and beta ##
estmodel <- RMgencauchy(var=NA, scale=NA, alpha=NA, beta=NA) +
RMtrend(NA)
RFfit(estmodel, data=d, transform = NA) ## just for information
trafo <- function(a) c(a[1], rep(a[2], 2))
fit <- RFfit(estmodel, data=d,
transform = list(c(TRUE, TRUE, FALSE), trafo))
print(fit)
print(fit, full=TRUE)
|
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