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

me(x, sdx, gr = NULL)

## Arguments

x The variable measured with error. Known measurement error of x treated as standard deviation. 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).

brmsformula, brmsformula-helpers

## Examples

if (FALSE) {
# 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)
}