Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/georob_exported_functions.R
This page documents parameters used to control georob
. It
describes the arguments of the functions control.georob
,
param.transf
, fwd.transf
, dfwd.transf
,
bwd.transf
, control.rq
, control.nleqslv
,
control.nlminb
and control.optim
, which all serve to
control the behaviour of georob
.
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 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | control.georob(ml.method = c("REML", "ML"), reparam = TRUE,
maximizer = c("nlminb", "optim"), initial.param = TRUE,
initial.fixef = c("lmrob", "rq", "lm"), bhat = NULL,
min.rweight = 0.25,
param.tf = param.transf(), fwd.tf = fwd.transf(),
deriv.fwd.tf = dfwd.transf(), bwd.tf = bwd.transf(),
psi.func = c("logistic", "t.dist", "huber"),
irwls.maxiter = 50,
irwls.ftol = 1.e-5, force.gradient = FALSE,
min.condnum = 1.e-12, zero.dist = sqrt(.Machine[["double.eps"]]),
error.family.estimation = c("gaussian", "long.tailed"),
error.family.cov.effects = c("gaussian", "long.tailed"),
error.family.cov.residuals = c("gaussian", "long.tailed"),
cov.bhat = TRUE, full.cov.bhat = FALSE, cov.betahat = TRUE,
cov.delta.bhat = TRUE, full.cov.delta.bhat = TRUE,
cov.delta.bhat.betahat = TRUE,
cov.ehat = TRUE, full.cov.ehat = FALSE,
cov.ehat.p.bhat = FALSE, full.cov.ehat.p.bhat = FALSE,
hessian = TRUE,
rq = control.rq(), lmrob = lmrob.control(),
nleqslv = control.nleqslv(),
optim = control.optim(), nlminb = control.nlminb(),
pcmp = control.pcmp(), ...)
param.transf(variance = "log", snugget = "log", nugget = "log", scale = "log",
alpha = c(
RMaskey = "log", RMdewijsian = "logit2", RMfbm = "logit2", RMgencauchy = "logit2",
RMgenfbm = "logit2", RMlgd = "identity", RMqexp = "logit1", RMstable = "logit2"
),
beta = c(RMdagum = "logit1", RMgencauchy = "log", RMlgd = "log"),
delta = "logit1", gamma = c(RMcauchy = "log", RMdagum = "logit1"),
kappa = "logit3", lambda = "log", mu = "log", nu = "log",
f1 = "log", f2 ="log", omega = "identity", phi = "identity", zeta = "identity")
fwd.transf(...)
dfwd.transf(...)
bwd.transf(...)
control.rq(tau = 0.5, rq.method = c("br", "fnb", "pfn"),
rq.alpha = 0.1, ci = FALSE, iid = TRUE,
interp = TRUE, tcrit = TRUE, rq.beta = 0.99995, eps = 1e-06,
Mm.factor = 0.8, max.bad.fixup = 3, ...)
control.nleqslv(method = c("Broyden", "Newton"),
global = c("dbldog", "pwldog", "qline", "gline", "none"),
xscalm = c("fixed", "auto"), control = list(ftol = 1e-04), ...)
control.optim(method = c("BFGS", "Nelder-Mead", "CG",
"L-BFGS-B", "SANN", "Brent"), lower = -Inf, upper = Inf,
control = list(reltol = 1e-05), ...)
control.nlminb(control = list(rel.tol = 1.e-5), lower = -Inf,
upper = Inf, ...)
|
ml.method |
character keyword defining whether non-robust maximum
likelihood ( |
reparam |
logical. If |
maximizer |
character keyword defining whether the Gaussian (restricted)
log-likelihood is maximized by |
initial.param |
logical, controlling whether initial values of
variogram parameters are computed for solving the estimating equations of
the variogram and anisotropy parameters. If |
initial.fixef |
character keyword defining whether the function
|
bhat |
initial values for the spatial random effects
hatB, with
hatB=0
if |
min.rweight |
positive numeric. “Robustness weight” of
the initial |
param.tf |
a function such as |
fwd.tf |
a function such as |
deriv.fwd.tf |
a function such as |
bwd.tf |
a function such as |
psi.func |
character keyword defining what ψ_c-function should be
used for robust model fitting. Possible values are |
irwls.maxiter |
positive integer equal to the maximum number of
IRWLS iterations to solve the estimating equations of
B and
β (default |
irwls.ftol |
positive numeric convergence criterion for IRWLS.
Convergence is assumed if the objective function changes in one IRWLS
iteration does not exceed |
force.gradient |
logical controlling whether the estimating
equations or the gradient of the Gaussian restricted log-likelihood are
evaluated even if all variogram parameters are fixed (default
|
min.condnum |
positive numeric. Minimum acceptable ratio of smallest to
largest singular value of the model matrix
X (default |
zero.dist |
positive numeric equal to the maximum distance, separating two sampling locations that are still considered as being coincident. |
error.family.estimation |
character keyword, defining the
probability distribution for ε (default:
|
error.family.cov.effects |
character keyword, defining the
probability distribution for ε (default:
|
error.family.cov.residuals |
character keyword, defining the
probability distribution for ε (default: |
cov.bhat |
logical controlling whether the covariances of
hatB are returned by
|
full.cov.bhat |
logical controlling whether the full covariance
matrix ( |
cov.betahat |
logical controlling whether the covariance matrix of
hatβ is returned
(default |
cov.delta.bhat |
logical controlling whether the covariances of
B-hatB are returned (default |
full.cov.delta.bhat |
logical controlling whether the full covariance
matrix ( |
cov.delta.bhat.betahat |
logical controlling whether the covariance
matrix of B-hatB and
hatβ is returned
(default |
cov.ehat |
logical controlling whether the covariances of
hatε=Y-X hatβ -
hatB are returned (default |
full.cov.ehat |
logical controlling whether the full covariance
matrix ( |
cov.ehat.p.bhat |
logical controlling whether the covariances of
hatε+ hatB=Y-X
hatβ are returned (default |
full.cov.ehat.p.bhat |
logical controlling whether the full
covariance matrix ( |
hessian |
logical scalar controlling whether for Gaussian (RE)ML the Hessian should be computed at the MLEs. |
rq |
a list of arguments passed to |
lmrob |
a list of arguments passed to the |
nleqslv |
a list of arguments passed to
|
nlminb |
a list of arguments passed to |
optim |
a list of arguments passed to |
pcmp |
a list of arguments, passed e.g. to |
... |
for |
variance, snugget, nugget, scale, alpha, beta, delta, gamma,
kappa, lambda, mu, nu |
character strings with names of transformation functions of the variogram parameters. |
f1, f2, omega, phi, zeta |
character strings with names of transformation functions of the variogram parameters. |
tau, rq.method, rq.alpha, ci, iid, interp, tcrit |
arguments passed
as |
rq.beta, eps, Mm.factor, max.bad.fixup |
arguments passed as
|
method, global, xscalm, control, lower, upper, reltol,
rel.tol |
arguments passed to related arguments of
|
The arguments param.tf
, fwd.tf
, deriv.fwd.tf
,
bwd.tf
define the transformations of the variogram parameters for
RE(ML) estimation. Implemented are currently "log"
,
"logit1"
, "logit2"
, "logit3"
(various variants of
logit-transformation, see code of function fwd.transf
) and "identity"
(= no)
transformations. These are the possible values that the many arguments
of the function param.transf
accept (as quoted character strings)
and these are the names of the list components returned by
fwd.transf
, dfwd.transf
and bwd.transf
. Additional
transformations can be implemented by:
Extending the function definitions by arguments like
fwd.tf = fwd.transf(my.fun = function(x) your transformation)
,
deriv.fwd.tf = dfwd.transf(my.fun = function(x) your derivative)
,
bwd.tf = bwd.transf(my.fun = function(x) your back-transformation)
,
Assigning to a given argument of param.transf
the name of
the new function, e.g.
variance = "my.fun"
.
Note the values given for the arguments of param.transf
must match
the names of the functions returned by fwd.transf
,
dfwd.transf
and bwd.transf
.
The robustified estimating equations of robust REML depend on the
covariances of hatB.
These covariances (and the covariances of
B-hatB,
hatβ,
hatε,
hatε+ hatB) are
approximated by expressions that in turn depend on the variances of
ε,
ψ(ε/τ) and the expectation
of ψ'(ε/τ) (= \partial / \partial
ε ψ(ε/τ)). The arguments
error.family.estimation
, error.family.cov.effects
and
error.family.cov.residuals
control what parametric distribution
for ε is used to compute the variance of
ε,
ψ(ε/τ) and the expectation
of ψ'(ε/τ) when
solving the estimating equations (error.family.estimation
),
computing the covariances of
hatβ,
hatB and
B-hatB
(error.family.cov.effects
) and
computing the covariances of
hatε=Y-X hatβ -
hatB and hatε+ hatB=Y-X
hatβ
(error.family.cov.residuals
).
Possible options are: "gaussian"
or "long.tailed"
. In
the latter case the PDF of ε is assumed to be
proportional to 1/τ \exp(-ρ(ε/τ)), where
ψ(x)=ρ'(x).
control.georob
, control.rq
, control.nleqslv
,
control.optim
and control.nlminb
all create lists with
control parameters passed to georob
,
rq
, nleqslv
,
optim
, nlminb
, respectively.
Note that the list returned by code.georob
contains some
components (irwls.initial
, tuning.psi.nr
,
cov.bhat.betahat
, aux.cov.pred.target
) that cannot be
changed by the user.
param.transf
generates a list with character strings that
define what transformations are used for estimating the variogram
parameters, and fwd.transf
, bwd.transf
and
dfwd.transf
return lists of functions with forward and backward
transformations and the first derivatives of the forward
transformations.
Andreas Papritz andreas.papritz@env.ethz.ch.
georobIntro
for a description of the model and a brief summary of the algorithms;
georob
for (robust) fitting of spatial linear models;
georobObject
for a description of the class georob
;
profilelogLik
for computing profiles of Gaussian likelihoods;
plot.georob
for display of RE(ML) variogram estimates;
georobModelBuilding
for stepwise building models of class georob
;
cv.georob
for assessing the goodness of a fit by georob
;
georobMethods
for further methods for the class georob
;
predict.georob
for computing robust Kriging predictions;
lgnpp
for unbiased back-transformation of Kriging prediction
of log-transformed data;
georobSimulation
for simulating realizations of a Gaussian process
from model fitted by georob
; and finally
sample.variogram
and fit.variogram.model
for robust estimation and modelling of sample variograms.
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## Not run:
data(meuse)
r.logzn.rob <- georob(log(zinc) ~ sqrt(dist), data = meuse, locations = ~ x + y,
variogram.model = "RMexp",
param = c(variance = 0.15, nugget = 0.05, scale = 200),
tuning.psi = 1, control = control.georob(cov.bhat = TRUE,
cov.ehat.p.bhat = TRUE, initial.fixef = "rq"), verbose = 2)
qqnorm(rstandard(r.logzn.rob, level = 0)); abline(0, 1)
qqnorm(ranef(r.logzn.rob, standard = TRUE)); abline(0, 1)
## End(Not run)
|
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