R: Additional Response Information

addition-terms {brms} | R Documentation

## Additional Response Information

### Description

Provide additional information on the response variable in brms models, such as censoring, truncation, or known measurement error.

### Usage

    resp_se(x, sigma = FALSE)

    resp_weights(x, scale = FALSE)

    resp_trials(x)

    resp_cat(x)

    resp_dec(x)

    resp_cens(x, y2 = NULL)

    resp_trunc(lb = -Inf, ub = Inf)

    resp_mi(sdy = NULL)

### Arguments

`x` | A vector; usually a variable defined in the data. Allowed values depend on the function: `resp_se` and `resp_weights` require positive numeric values. `resp_trials` and `resp_cat` require positive integers. `resp_dec` requires `0` and `1`, or alternatively `'lower'` and `'upper'`; `resp_cens` requires `'left'`, `'none'`, `'right'`, and `'interval'` (or equivalently `-1`, `0`, `1`, and `2`) to indicate left, no, right, or interval censoring.
`sigma` | Logical; Indicates whether the residual standard deviation parameter `sigma` should be included in addition to the known measurement error. Defaults to `FALSE` for backwards compatibility, but setting it to `TRUE` is usually the better choice.
`scale` | Logical; Indicates whether weights should be scaled so that the average weight equals one. Defaults to `FALSE`.
`y2` | A vector specifying the upper bounds in interval censoring.
`lb` | A numeric vector or single numeric value specifying the lower truncation bound.
`ub` | A numeric vector or single numeric value specifying the upper truncation bound.
`sdy` | Optional known measurement error of the response treated as standard deviation. If specified, handles measurement error and (completely) missing values at the same time using the plausible-values-technique.

### Details

These functions are almost solely useful when called in formulas passed to the brms package. Within formulas, the `resp_` prefix may be omitted. More information is given in the 'Details' section of `brmsformula`.

### Value

A vector containing additional information on the response variable in an appropriate format.

### See Also

`brm`, `brmsformula`

### Examples

    ## Not run:
    ## Random effects meta-analysis
    nstudies <- 20
    true_effects <- rnorm(nstudies, 0.5, 0.2)
    sei <- runif(nstudies, 0.05, 0.3)
    outcomes <- rnorm(nstudies, true_effects, sei)
    data1 <- data.frame(outcomes, sei)
    fit1 <- brm(outcomes | se(sei, sigma = TRUE) ~ 1,
                data = data1)
    summary(fit1)

    ## Probit regression using the binomial family
    n <- sample(1:10, 100, TRUE)  # number of trials
    success <- rbinom(100, size = n, prob = 0.4)
    x <- rnorm(100)
    data2 <- data.frame(n, success, x)
    fit2 <- brm(success | trials(n) ~ x, data = data2,
                family = binomial("probit"))
    summary(fit2)

    ## Survival regression modeling the time between the first
    ## and second recurrence of an infection in kidney patients.
    fit3 <- brm(time | cens(censored) ~ age * sex + disease + (1|patient),
                data = kidney, family = lognormal())
    summary(fit3)

    ## Poisson model with truncated counts
    fit4 <- brm(count | trunc(ub = 104) ~ log_Base4_c * Trt_c,
                data = epilepsy, family = poisson())
    summary(fit4)

    ## End(Not run)

[Package brms version 2.6.0 Index]