georob (version 0.2-3)

georob-S3methods: Common S3 Methods for Class georob

Description

This page documents the methods fixef, fixed.effects, model.frame, model.matrix, nobs, print, ranef, random.effects, resid, residuals, rstandard, rstudent, summary and vcov for the class georob.

Usage

"fixef"(object, ...)
"fixed.effects"(object, ...)
"model.frame"(formula, ...)
"model.matrix"(object, ...)
"nobs"(object, ...)
"print"(x, digits = max(3, getOption("digits") - 3), ...)
"ranef"(object, standard = FALSE, ...)
"random.effects"(object, standard = FALSE, ...)
"resid"(object,  type = c("working", "response", "deviance", "pearson", "partial"),  terms = NULL, level = 1, ...)
"residuals"(object,  type = c("working", "response", "deviance", "pearson", "partial"),  terms  = NULL, level = 1, ...) 
"rstandard"(model, level = 1, ...)
"rstudent"(model, ...)
"summary"(object, correlation = FALSE, signif = 0.95, ...)
"vcov"(object, ...)

Arguments

object, model, x

an object of class georob, see georobObject.

formula

a model formula or terms object or an object of class georob, see georobObject.

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 FALSE).

digits

positive integer indicating the number of decimal digits to print.

level

an optional integer giving the level for extracting the residuals from object. level = 0 extracts the regression residuals $hatB(s) + hat\epsilon(s)$ and level = 1 (default) only the estimated errors $hat\epsilon(s)$.

signif

confidence level for computing confidence intervals for variogram parameters (default 0.95).

standard

logical controlling whether the spatial random effects $B$ should be standardized (default FALSE).

type

character keyword indicating the type of residuals to compute, see residuals.lm. type = "huber" computes `huberized' residuals $hat\sigma/\gamma_1\psi(hat\epsilon(s)/hat\sigma)$.

terms

If type = "terms", which terms (default is all terms).

...

additional arguments passed to methods.

Details

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 $hat\epsilon(s)$ 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 regression coefficients, which may of course also be obtained by coef. Besides, the default methods of the generic functions coef, confint, df.residual, fitted, formula, termplot and update can be used for objects of class georob.

See Also

georobIntro for a description of the model and a brief summary of the algorithms; georob for (robust) fitting of spatial linear models; georobModelBuilding for stepwise building models of class georob; georobObject for a description of the class georob.

Examples

## 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,
#     control = control.georob(cov.bhat = TRUE, cov.ehat.p.bhat = TRUE))
# 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)