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.component()
.- x
An
summary_bru2
object
Examples
# \donttest{
if (bru_safe_inla(multicore = FALSE)) {
# 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, family = "gaussian", data = input.df)
# Obtain summary
fit$summary.fixed
}
#> Current num.threads is '1:1'.
#> No num.threads change needed.
#> Warning: All covariate evaluations for 'Intercept' are NULL; an intercept component was likely intended.
#> Implicit latent intercept component specification is deprecated since version 2.1.14.
#> Use explicit notation '+ Intercept(1)' instead (or '+1' for '+ Intercept(1)').
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> x 2.018472 0.04665123 1.925281 2.018473 2.111659 2.018473
#> Intercept 5.017068 0.03298271 4.951181 5.017069 5.082951 5.017070
#> kld
#> x 8.494767e-06
#> Intercept 8.494980e-06
if (bru_safe_inla(multicore = FALSE)) {
# Alternatively, we can use the like() function to construct the likelihood:
lik <- like(family = "gaussian", formula = y ~ x + Intercept, data = input.df)
fit <- bru(~ x + Intercept(1), lik)
fit$summary.fixed
}
#> Current num.threads is '1:1'.
#> No num.threads change needed.
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> x 2.018472 0.04665574 1.925272 2.018473 2.111668 2.018473
#> Intercept 5.017068 0.03298589 4.951174 5.017069 5.082957 5.017070
#> kld
#> x 8.484897e-06
#> Intercept 8.485112e-06
# An important addition to the INLA methodology is bru's ability to use
# non-linear predictors. Such a predictor can be formulated via like()'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(multicore = FALSE)) {
z <- 2
input.df <- within(input.df, y <- 5 + exp(z) * x + rnorm(10, mean = 0, sd = 0.1))
lik <- like(
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
}
#> Current num.threads is '1:1'.
#> No num.threads change needed.
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> z 1.999145 0.006461788 1.986238 1.999145 2.012053 1.999146
#> Intercept 5.014824 0.033728354 4.947448 5.014825 5.082197 5.014826
#> kld
#> z 8.485246e-06
#> Intercept 8.485029e-06
# }