R: Predictors with Measurement Error in 'brms' Models

me {brms} | R Documentation

## 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)

[Package brms version 2.16.3 Index]