
Takes a fitted bru
object produced by the function bru()
and produces
predictions given a new set of values for the model covariates or the
original values used for the model fit. The predictions can be based on any
R expression that is valid given these values/covariates and the joint
posterior of the estimated random effects.
# S3 method for bru
predict(
object,
data = NULL,
formula = NULL,
n.samples = 100,
seed = 0L,
num.threads = NULL,
include = NULL,
exclude = NULL,
drop = FALSE,
...
)
A data.frame or SpatialPointsDataFrame of covariates needed for the prediction.
A formula defining an R expression to evaluate for each generated
sample. If NULL
, the latent and hyperparameter states are generated
as named list elements. In addition to the component names (that give the effect
of each component evaluated for the input data), the suffix _latent
can be used
to directly access the latent state for a component, and the suffix _eval
can be used to evaluate a component at other input values than the expressions
defined in the component definition itself, e.g. field_eval(cbind(x,y))
for a
component defined with field(coordinates, ...)
. For "iid" models with
mapper = bru_mapper_index(n)
, rnorm()
is used to generate new realisations for
indices greater than n
.
Integer setting the number of samples to draw in order to calculate the posterior statistics. The default is rather low but provides a quick approximate result.
Random number generator seed passed on to inla.posterior.sample
Specification of desired number of threads for parallel computations. Default NULL, leaves it up to INLA. When seed != 0, overridden to "1:1"
Character vector of component labels that are needed by the predictor expression; Default: NULL (include all components that are not explicitly excluded)
Character vector of component labels that are not used by the
predictor expression. The exclusion list is applied to the list
as determined by the include
parameter; Default: NULL (do not remove
any components from the inclusion list)
logical; If keep=FALSE
, data
is a Spatial*DataFrame
, and the
prediciton summary has the same number of rows as data
, then the output is
a Spatial*DataFrame
object. Default FALSE
.
Additional arguments passed on to inla.posterior.sample
a data.frame or Spatial* object with predicted mean values and other summary statistics attached.
Mean value predictions are accompanied by the standard errors, upper and lower 2.5% quantiles, the median, variance, coefficient of variation as well as the variance and minimum and maximum sample value drawn in course of estimating the statistics.
Internally, this method calls generate.bru()
in order to draw samples from
the model.
# NOT RUN {
if (bru_safe_inla(multicore = FALSE)) {
# Load the Gorilla data
data(gorillas, package = "inlabru")
# Plot the Gorilla nests, the mesh and the survey boundary
ggplot() +
gg(gorillas$mesh) +
gg(gorillas$nests) +
gg(gorillas$boundary) +
coord_fixed()
# Define SPDE prior
matern <- INLA::inla.spde2.pcmatern(gorillas$mesh,
prior.sigma = c(0.1, 0.01),
prior.range = c(0.01, 0.01)
)
# Define domain of the LGCP as well as the model components (spatial SPDE effect and Intercept)
cmp <- coordinates ~ mySmooth(main = coordinates, model = matern) + Intercept(1)
# Fit the model, with "eb" instead of full Bayes
fit <- lgcp(cmp, gorillas$nests,
samplers = gorillas$boundary,
domain = list(coordinates = gorillas$mesh),
options = list(control.inla = list(int.strategy = "eb"))
)
# Once we obtain a fitted model the predict function can serve various purposes.
# The most basic one is to determine posterior statistics of a univariate
# random variable in the model, e.g. the intercept
icpt <- predict(fit, NULL, ~ c(Intercept = Intercept_latent))
plot(icpt)
# The formula argument can take any expression that is valid within the model, for
# instance a non-linear transformation of a random variable
exp.icpt <- predict(fit, NULL, ~ c(
"Intercept" = Intercept_latent,
"exp(Intercept)" = exp(Intercept_latent)
))
plot(exp.icpt, bar = TRUE)
# The intercept is special in the sense that it does not depend on other variables
# or covariates. However, this is not true for the smooth spatial effects 'mySmooth'.
# In order to predict 'mySmooth' we have to define where (in space) to predict. For
# this purpose, the second argument of the predict function can take \code{data.frame}
# objects as well as Spatial objects. For instance, we might want to predict
# 'mySmooth' at the locations of the mesh vertices. Using
vrt <- vertices(gorillas$mesh)
# we obtain these vertices as a SpatialPointsDataFrame
ggplot() +
gg(gorillas$mesh) +
gg(vrt, color = "red")
# Predicting 'mySmooth' at these locations works as follows
mySmooth <- predict(fit, vrt, ~mySmooth)
# Note that just like the input also the output will be a SpatialPointsDataFrame
# and that the predicted statistics are simply added as columns
class(mySmooth)
head(vrt)
head(mySmooth)
# Plotting the mean, for instance, at the mesh node is straight forward
ggplot() +
gg(gorillas$mesh) +
gg(mySmooth, aes(color = mean), size = 3)
# However, we are often interested in a spatial field and thus a linear interpolation,
# which can be achieved by using the gg mechanism for meshes
ggplot() +
gg(gorillas$mesh, color = mySmooth$mean)
# Alternatively, we can predict the spatial field at a grid of locations, e.g. a
# SpatialPixels object covering the mesh
pxl <- pixels(gorillas$mesh)
mySmooth2 <- predict(fit, pxl, ~mySmooth)
# This will give us a SpatialPixelDataFrame with the columns we are looking for
head(mySmooth2)
ggplot() +
gg(mySmooth2)
}
# }
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