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. Detailed documentation on the use of each of these functions can be found in the Details section of `brmsformula` (under "Additional response information").

### Usage

    resp_se(x, sigma = FALSE)

    resp_weights(x, scale = FALSE)

    resp_trials(x)

    resp_thres(x, gr = NA)

    resp_cat(x)

    resp_dec(x)

    resp_cens(x, y2 = NA)

    resp_trunc(lb = -Inf, ub = Inf)

    resp_mi(sdy = NA)

    resp_index(x)

    resp_rate(denom)

    resp_subset(x)

    resp_vreal(...)

    resp_vint(...)

### 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`, `resp_thres`, and `resp_cat` require positive integers. `resp_dec` requires `0` and `1`, or alternatively `'lower'` and `'upper'`. `resp_subset` requires `0` and `1`, or alternatively `FALSE` and `TRUE`. `resp_cens` requires `'left'`, `'none'`, `'right'`, and `'interval'` (or equivalently `-1`, `0`, `1`, and `2`) to indicate left, no, right, or interval censoring. `resp_index` does not make any requirements other than the value being unique for each observation.
`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`.
`gr` | A vector of grouping indicators.
`y2` | A vector specifying the upper bounds in interval censoring. Will be ignored for non-interval censored observations. However, it should NOT be `NA` even for non-interval censored observations to avoid accidental exclusion of these observations.
`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.
`denom` | A vector of positive numeric values specifying the denominator values from which the response rates are computed.
`...` | For `resp_vreal`, vectors of real values. For `resp_vint`, vectors of integer values. In Stan, these variables will be named `vreal1`, `vreal2`, ..., and `vint1`, `vint2`, ..., respectively.

### 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` (under "Additional response information").

### Value

A list of additional response information to be processed further by brms.

### 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) ~ zBase * Trt,
                data = epilepsy, family = poisson())
    summary(fit4)

    ## End(Not run)

[Package brms version 2.17.0 Index]