This function performs Bayesian prediction for a geostatistical linear Gaussian model.
spatial.pred.linear.Bayes(
object,
grid.pred,
predictors = NULL,
type = "marginal",
scale.predictions = c("logit", "prevalence", "odds"),
quantiles = c(0.025, 0.975),
standard.errors = FALSE,
thresholds = NULL,
scale.thresholds = NULL,
messages = TRUE
)
an object of class "Bayes.PrevMap" obtained as result of a call to linear.model.Bayes
.
a matrix of prediction locations.
a data frame of the values of the explanatory variables at each of the locations in grid.pred
; each column correspond to a variable and each row to a location. Warning: the names of the columns in the data frame must match those in the data used to fit the model. Default is predictors=NULL
for models with only an intercept.
a character indicating the type of spatial predictions: type="marginal"
for marginal predictions or type="joint"
for joint predictions. Default is type="marginal"
. In the case of a low-rank approximation only joint predictions are available.
a character vector of maximum length 3, indicating the required scale on which spatial prediction is carried out: "logit", "prevalence" and "odds". Default is scale.predictions=c("logit","prevalence","odds")
.
a vector of quantiles used to summarise the spatial predictions.
logical; if standard.errors=TRUE
, then standard errors for each scale.predictions
are returned. Default is standard.errors=FALSE
.
a vector of exceedance thresholds; default is thresholds=NULL
.
a character value indicating the scale on which exceedance thresholds are provided: "logit"
, "prevalence"
or "odds"
. Default is scale.thresholds=NULL
.
logical; if messages=TRUE
then status messages are printed on the screen (or output device) while the function is running. Default is messages=TRUE
.
A "pred.PrevMap" object list with the following components: logit
; prevalence
; odds
; exceedance.prob
, corresponding to a matrix of the exceedance probabilities where each column corresponds to a specified value in thresholds
; grid.pred
prediction locations.
Each of the three components logit
, prevalence
and odds
is also a list with the following components:
predictions
: a vector of the predictive mean for the associated quantity (logit, odds or prevalence).
standard.errors
: a vector of prediction standard errors (if standard.errors=TRUE
).
quantiles
: a matrix of quantiles of the resulting predictions with each column corresponding to a quantile specified through the argument quantiles
.