inlabru (version 2.1.1)

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.

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
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.

...

ignored arguments (needed for S3 compatibility).

Value

Predicted values

See Also

Other sample generators: generate

Examples

# NOT RUN {
# 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))
# }