me: Predictors with Measurement Error in 'brms' Models

In brms: Bayesian Regression Models using 'Stan'

Predictors with Measurement Error in brms Models

Description

(Soft deprecated) Specify predictors with measurement error. The function does not evaluate its arguments – it exists purely to help set up a model.

Usage

me(x, sdx, gr = NULL)

Arguments

x	The variable measured with error.
sdx	Known measurement error of x treated as standard deviation.
gr	Optional grouping factor to specify which values of x correspond to the same value of the latent variable. If NULL (the default) each observation will have its own value of the latent variable.

Details

For detailed documentation see help(brmsformula). me terms are soft deprecated in favor of the more general and consistent mi terms. By default, latent noise-free variables are assumed to be correlated. To change that, add set_mecor(FALSE) to your model formula object (see examples).

See Also

brmsformula, brmsformula-helpers

Examples

## Not run: 
# sample some data
N <- 100
dat <- data.frame(
  y = rnorm(N), x1 = rnorm(N),
  x2 = rnorm(N), sdx = abs(rnorm(N, 1))
 )
# fit a simple error-in-variables model
fit1 <- brm(y ~ me(x1, sdx) + me(x2, sdx), data = dat,
            save_pars = save_pars(latent = TRUE))
summary(fit1)

# turn off modeling of correlations
bform <- bf(y ~ me(x1, sdx) + me(x2, sdx)) + set_mecor(FALSE)
fit2 <- brm(bform, data = dat, save_pars = save_pars(latent = TRUE))
summary(fit2)

## End(Not run)

brms documentation built on Sept. 23, 2024, 5:08 p.m.