inlabru (version 2.1.9)

generate.bru: Sampling based on bru posteriors

Description

Takes a fitted bru object produced by the function bru() and produces samples given a new set of values for the model covariates or the original values used for the model fit. The samples can be based on any R expression that is valid given these values/covariates and the joint posterior of the estimated random effects.

Usage

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

Arguments

object

A bru object obtained by calling bru.

data

A data.frame or SpatialPointsDataFrame of covariates needed for sampling.

formula

A formula determining which effects to sample from and how to combine them analytically.

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.

...

additional, unused arguments.

Value

List of generated samples

See Also

predict.bru

Other sample generators: generate.inla, generate

Examples

# NOT RUN {
if (require("INLA", quietly = TRUE)) {
  
# Generate data for a simple linear model

input.df <- data.frame(x=cos(1:10))
input.df <- within(input.df, y <- 5 + 2*cos(1:10) + rnorm(10, mean=0, sd=0.1))

# Fit the model

fit <- bru(y ~ xeff(map = x, model = "linear"), "gaussian", input.df)
summary(fit)

# Generate samples for some predefined x

df = data.frame(x = seq(-4, 4, by = 0.1))
smp = generate(fit, df, ~ xeff + Intercept, n.samples = 10)

# Plot the resulting realizations

plot(df$x, smp[[1]], type = "l")
for (k in 2:length(smp)) points(df$x, smp[[k]], type = "l")

# We can also draw samples form the joint posterior

df = data.frame(x = 1)
smp = generate(fit, df, ~ data.frame(xeff, Intercept), n.samples = 10)
smp[[1]]

# ... and plot them

plot(do.call(rbind, smp))

}
# }