Summary for an inlabru fit

Takes a fitted bru object produced by bru() or lgcp() and creates various summaries from it.

Usage

# S3 method for class 'bru'
summary(object, verbose = FALSE, ...)

# S3 method for class 'summary_bru'
print(x, ...)

Arguments

object

An object obtained from a bru() or lgcp() call

verbose

logical; If TRUE, include more details of the component definitions. If FALSE, only show basic component definition information. Default: FALSE

...

arguments passed on to component summary functions, see summary.bru_comp().

x

An object to be printed

Examples

# \donttest{
if (bru_safe_inla()) {
  # Simulate some covariates x and observations y
  input.df <- data.frame(x = cos(1:10))
  input.df <- within(input.df, {
    y <- 5 + 2 * x + rnorm(10, mean = 0, sd = 0.1)
  })

  # Fit a Gaussian likelihood model
  fit <- bru(y ~ x + Intercept(1), family = "gaussian", data = input.df)

  # Obtain summary
  fit$summary.fixed
}
#>               mean         sd 0.025quant 0.5quant 0.975quant     mode
#> x         1.963341 0.04349123   1.876435 1.963342   2.050244 1.963342
#> Intercept 4.983308 0.03074856   4.921864 4.983309   5.044749 4.983309
#>                    kld
#> x         5.771563e-06
#> Intercept 5.771707e-06


if (bru_safe_inla()) {
  # Alternatively, we can use the bru_obs() function to construct the
  # likelihood:

  lik <- bru_obs(family = "gaussian",
              formula = y ~ x + Intercept,
              data = input.df)
  fit <- bru(~ x + Intercept(1), lik)
  fit$summary.fixed
}
#>               mean         sd 0.025quant 0.5quant 0.975quant     mode
#> x         1.963341 0.04349123   1.876435 1.963342   2.050244 1.963342
#> Intercept 4.983308 0.03074856   4.921864 4.983309   5.044749 4.983309
#>                    kld
#> x         5.771563e-06
#> Intercept 5.771707e-06

# An important addition to the INLA methodology is bru's ability to use
# non-linear predictors. Such a predictor can be formulated via bru_obs()'s
# \code{formula} parameter. The z(1) notation is needed to ensure that
# the z component should be interpreted as single latent variable and not
# a covariate:

if (bru_safe_inla()) {
  z <- 2
  input.df <- within(input.df, {
    y <- 5 + exp(z) * x + rnorm(10, mean = 0, sd = 0.1)
  })
  lik <- bru_obs(
    family = "gaussian", data = input.df,
    formula = y ~ exp(z) * x + Intercept
  )
  fit <- bru(~ z(1) + Intercept(1), lik)

  # Check the result (z posterior should be around 2)
  fit$summary.fixed
}
#>               mean          sd 0.025quant 0.5quant 0.975quant     mode
#> z         1.996952 0.004747336   1.987465 1.996952   2.006438 1.996952
#> Intercept 4.977456 0.024725205   4.928049 4.977457   5.026862 4.977457
#>                    kld
#> z         5.816215e-06
#> Intercept 5.816122e-06
# }