brms (version 2.9.0)

mo: Monotonic Predictors in brms Models

Description

Specify a monotonic predictor term in brms. The function does not evaluate its arguments -- it exists purely to help set up a model.

Usage

mo(x)

Arguments

x

An integer variable or an ordered factor to be modeled as monotonic.

Details

For detailed documentation see help(brmsformula) as well as vignette("brms_monotonic").

References

B<U+00FC>rkner P. C. & Charpentier, E. (in review). Monotonic Effects: A Principled Approach for Including Ordinal Predictors in Regression Models. PsyArXiv preprint.

See Also

brmsformula

Examples

# NOT RUN {
  
# }
# NOT RUN {
# generate some data
income_options <- c("below_20", "20_to_40", "40_to_100", "greater_100")
income <- factor(sample(income_options, 100, TRUE), 
                 levels = income_options, ordered = TRUE)
mean_ls <- c(30, 60, 70, 75)
ls <- mean_ls[income] + rnorm(100, sd = 7)
dat <- data.frame(income, ls)

# fit a simple monotonic model
fit1 <- brm(ls ~ mo(income), data = dat)

# summarise the model
summary(fit1)
plot(fit1, N = 6)
plot(marginal_effects(fit1), points = TRUE)

# model interaction with other variables
dat$x <- sample(c("a", "b", "c"), 100, TRUE)
fit2 <- brm(ls ~ mo(income)*x, data = dat)

# summarise the model
summary(fit2)
plot(marginal_effects(fit2), points = TRUE)
# }
# NOT RUN {
 
# }