Arguments
- object
- verbose
logical; If
TRUE
, include more details of the component definitions. IfFALSE
, 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$summary.fixed
}
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> x 1.973491 0.03531264 1.902928 1.973491 2.044052 1.973491
#> Intercept 5.001535 0.02496624 4.951646 5.001535 5.051422 5.001535
#> kld
#> x 981.7759
#> Intercept 10440.0531
if (bru_safe_inla()) {
# Alternatively, we can use the bru_obs() function to construct the likelihood:
lik <- bru_obs(family = "gaussian",
formula = y ~ x + Intercept,
data = input.df)
fit <- bru(~ x + Intercept(1), lik)
fit$summary.fixed
}
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> x 1.973491 0.03531264 1.902928 1.973491 2.044052 1.973491
#> Intercept 5.001535 0.02496624 4.951646 5.001535 5.051422 5.001535
#> kld
#> x 981.7759
#> Intercept 10440.0531
# 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 2.006606 0.007321987 1.991975 2.006606 2.021237 2.006606
#> Intercept 4.930012 0.038504504 4.853069 4.930013 5.006950 4.930012
#> kld
#> z 23439.821
#> Intercept 4272.586
# }