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)



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.


For detailed documentation see help(brmsformula).

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


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_mevars = 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_mevars = TRUE) summary(fit2) }