Description Usage Arguments Details Value Author(s) See Also Examples
This page documents the methods coef
, fixef
,
fixed.effects
, model.frame
, model.matrix
,
nobs
, print
, ranef
, random.effects
,
resid
, residuals
, rstandard
,
summary
and vcov
for the class georob
which extract
the respective components or summarize a georob
object.
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 | ## S3 method for class 'georob'
coef(object, what = c("trend", "variogram"), ...)
## S3 method for class 'georob'
fixef(object, ...)
## S3 method for class 'georob'
fixed.effects(object, ...)
## S3 method for class 'georob'
model.frame(formula, ...)
## S3 method for class 'georob'
model.matrix(object, ...)
## S3 method for class 'georob'
nobs(object, ...)
## S3 method for class 'georob'
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'georob'
ranef(object, standard = FALSE, ...)
## S3 method for class 'georob'
random.effects(object, standard = FALSE, ...)
## S3 method for class 'georob'
resid(object,
type = c("working", "response", "deviance", "pearson", "partial"),
terms = NULL,
level = 1, ...)
## S3 method for class 'georob'
residuals(object,
type = c("working", "response", "deviance", "pearson", "partial"),
terms = NULL,
level = 1, ...)
## S3 method for class 'georob'
rstandard(model, level = 1, ...)
## S3 method for class 'georob'
summary(object, correlation = FALSE, signif = 0.95, ...)
## S3 method for class 'georob'
vcov(object, ...)
|
object, model, x |
an object of class |
formula |
a model |
correlation |
logical controlling whether the correlation matrix of
the estimated regression coefficients and of the fitted variogram
parameters (only for non-robust fits) is computed (default |
digits |
positive integer indicating the number of decimal digits to print. |
level |
an optional integer giving the level for extracting the
residuals from |
signif |
confidence level for computing confidence intervals for
variogram parameters (default |
standard |
logical controlling whether the spatial random effects
B should be standardized (default
|
type |
character keyword indicating the type of residuals to
compute, see |
terms |
If |
what |
If |
... |
additional arguments passed to methods. |
For robust REML fits deviance
returns (possibly with a warning)
the deviance of the Gaussian REML fit of the equivalent Gaussian spatial
linear model with heteroscedastic nugget.
The methods model.frame
, model.matrix
and nobs
extract the model frame, model matrix and the number of observations, see
help pages of respective generic functions.
The methods residuals
(and resid
) extract either the
estimated independent errors
hatε(s)
or the sum of the latter quantities and the spatial random effects
hatB(s).
rstandard
does the same but standardizes the residuals to unit
variance. ranef
(random.effects
) extracts the spatial
random effects with the option to standardize them as well, and
fixef
(fixed.effects
) extracts the fitted fixed-effects
regression coefficients, which may of course also be obtained by
coef
.
For Gaussian REML the method summary
computes confidence intervals
of the estimated variogram and anisotropy parameters from the Hessian
matrix of the (restricted) log-likelihood (= observed Fisher
information), based on the asymptotic normal distribution of (RE)ML
estimates. Note that the Hessian matrix with respect to the
transformed variogram and anisotropy parameters is used for this.
Hence the inverse Hessian matrix is the covariance matrix of the
transformed parameters, confidence intervals are first computed for the
transformed parameters and the limits of these intervals are transformed
back to the orginal scale of the parameters. Optionally, summary
reports the correlation matrix of the transformed parameters, also
computed from the Hessian matrix.
Note that the methods coef
and summary
generate objects of
class coef.georob
and summary.georob
, respectively, for
which only print
methods are available.
Besides, the default methods of the generic functions
confint
,
df.residual
, fitted
,
formula
, termplot
and
update
can be used for objects of class
georob
.
The methods fixef
, fixed.effects
and coef
return the
numeric vector of estimated fixed-effects regression coefficients or
variogram and anisotropy parameters (coef
only), and vcov
returns the covariance matrix of the estimated regression coefficients.
The methods resid
, residuals
and rstandard
return
numeric vectors of (standardized) residuals, and ranef
and
random.effects
the numeric vector of (standardized) spatial random
effects, see Details.
The methods model.frame
and model.matrix
return a model
frame and the fixed-effects model matrix, respectively, and nobs
returns the number of observations used to fit a spatial linear model.
The method summary
generates a list with components extracted
directly from object
(call
, residuals
, bhat
,
rweights
, converged
, convergence.code
, iter
,
loglik
, variogram.object
, gradient
,
tuning.psi
, df.residual
, control
, terms
) and
complements the list by the following components:
scale
the square root of the estimated nugget effect τ^2.
coefficients
a 4-column matrix with estimated regression coefficients, their standard errors t-statistics and corresponding (two-sided) p-values.
correlation
an optional lower-triagonal matrix with the Pearson correlation coefficients of the estimated regression coefficients.
param.aniso
either a vector (robust REML) or a 3-column matrix (Gaussian REML) with estimated variogram and anisotropy parameters, complemented for Gaussian REML with confidence limits, see Details.
cor.tf.param
an optional lower-triagonal matrix with the Pearson correlation coefficients of estimated transformed variogram and anisotropy parameters, see Details.
se.residuals
a vector with the standard errors of the estimated ε.
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;
control.georob
for controlling the behaviour of georob
;
georobModelBuilding
for stepwise building models of class georob
;
cv.georob
for assessing the goodness of a fit by 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 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ## Not run:
data(meuse)
## Gaussian REML fit
r.logzn.reml <- 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 = 1000)
summary(r.logzn.reml, correlation = TRUE)
## robust REML fit
r.logzn.rob <- update(r.logzn.reml, tuning.psi = 1)
summary(r.logzn.rob, correlation = TRUE)
## residual diagnostics
old.par <- par(mfrow = c(2,3))
plot(fitted(r.logzn.reml), rstandard(r.logzn.reml))
abline(h = 0, lty = "dotted")
qqnorm(rstandard(r.logzn.reml))
abline(0, 1)
qqnorm(ranef(r.logzn.reml, standard = TRUE))
abline(0, 1)
plot(fitted(r.logzn.rob), rstandard(r.logzn.rob))
abline(h = 0, lty = "dotted")
qqnorm(rstandard(r.logzn.rob))
abline(0, 1)
qqnorm(ranef(r.logzn.rob, standard = TRUE))
abline(0, 1)
par(old.par)
## End(Not run)
|
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