Predictors with Measurement Error in brms Models
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)
x |
The variable measured with error. |
sdx |
Known measurement error of |
gr |
Optional grouping factor to specify which
values of |
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).
## 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_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)
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