R: Monotonic Predictors in 'brms' Models

mo {brms} | R Documentation

## Monotonic Predictors in brms Models

### Description

Monotonic Predictors in brms Models

### 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")`.

This function is almost solely useful when called in formulas passed to the brms package.

### See Also

`brmsformula`

### Examples

    ## 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)

    ## End(Not run)

[Package brms version 2.6.0 Index]