me.Rmd

---
title: "``brms::me``"
output: html_document
---
  
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(brms)
```



## ``me``

Predictors with Measurement Error in brms Models

#### Description 
Specify predictors with measurement error. This function is almost solely useful when called in formulas passed to the brms package.

#### Usage
<pre><code>
me(x, sdx = NULL, gr = NULL)
</code></pre>
#### 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.


#### Examples

```{r}
# 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)
```

```{r]
# 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)

```

#### Details
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).