inlabru (version 2.1.9)

predict.bru: Prediction from fitted bru model

Description

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.

Mean value predictions are accompanied by the standard errors, upper and lower 2.5 median, variance, coefficient of variation as well as the variance and minimum and maximum sample value drawn in course of estimating the statistics.

Usage

# S3 method for bru
predict(object, data = NULL, formula = NULL,
  n.samples = 100, ...)

Arguments

object

An object obtained by calling bru or lgcp.

data

A data.frame or SpatialPointsDataFrame of covariates needed for the prediction.

formula

A formula determining which effects to predict and how to combine them.

n.samples

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.

...

ignored arguments (S3 generic compatibility).

Value

a data.frame or Spatial* object with predicted mean values and other summary statistics attached.

Details

Internally, this method calls generate.bru in order to draw samples from the model.

Examples

# NOT RUN {
if (require("INLA", quietly = TRUE)) {

# 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.spde2.pcmatern(gorillas$mesh,
                              prior.sigma = c(0.1, 0.01),
                              prior.range = c(5, 0.01))

# Define domain of the LGCP as well as the model components (spatial SPDE effect and Intercept)

cmp <- coordinates ~ mySmooth(map = coordinates, model = matern) + Intercept

# Fit the model
fit <- lgcp(cmp, gorillas$nests, samplers = gorillas$boundary)

# 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, ~ Intercept)
rownames(icpt) = "Intercept"
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, ~ exp(Intercept))
rownames(exp.icpt) = "exp(Intercept)"
plot(rbind(icpt, 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)
mySmooth = predict(fit, pxl, ~ mySmooth)

# This will give us a SpatialPixelDataFrame with the columns we are looking for

head(mySmooth)
ggplot() + gg(mySmooth)

}
# }