spatial.pred.linear.Bayes {PrevMap} | R Documentation |
Bayesian spatial predictions for the geostatistical Linear Gaussian model
Description
This function performs Bayesian prediction for a geostatistical linear Gaussian model.
Usage
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
)
Arguments
object |
an object of class "Bayes.PrevMap" obtained as result of a call to |
grid.pred |
a matrix of prediction locations. |
predictors |
a data frame of the values of the explanatory variables at each of the locations in |
type |
a character indicating the type of spatial predictions: |
scale.predictions |
a character vector of maximum length 3, indicating the required scale on which spatial prediction is carried out: "logit", "prevalence" and "odds". Default is |
quantiles |
a vector of quantiles used to summarise the spatial predictions. |
standard.errors |
logical; if |
thresholds |
a vector of exceedance thresholds; default is |
scale.thresholds |
a character value indicating the scale on which exceedance thresholds are provided: |
messages |
logical; if |
Value
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
.
Author(s)
Emanuele Giorgi e.giorgi@lancaster.ac.uk
Peter J. Diggle p.diggle@lancaster.ac.uk